problem solving methodology in engineering

• Oct 13, 2019

10 Steps to Problem Solving for Engineers

Updated: Dec 6, 2020

With the official launch of the engineering book 10+1 Steps to Problem Solving: An Engineer's Guide it may be interesting to know that formalization of the concept began in episode 2 of the Engineering IRL Podcast back in July 2018.

As noted in the book remnants of the steps had existed throughout my career and in this episode I actually recorded the episode off the top of my head.

My goal was to help engineers build a practical approach to problem solving.

Have a listen.

Who can advise on the best approach to problem solving other than the professional problem solvers - Yes. I'm talking about being an Engineer.

There are 2 main trains of thought with Engineering work for non-engineers and that's trying to change the world with leading edge tech and innovations, or plain old boring math nerd type things.

Whilst, somewhat the case what this means is most content I read around Tech and Engineering are either super technical and (excruciatingly) detailed. OR really riff raff at the high level reveling at the possibilities of changing the world as we know it. And so what we end up with is a base (engineer only details) and the topping (media innovation coverage) but what about the meat? The contents?

There's a lot of beauty and interesting things there too. And what's the centrepiece? The common ground between all engineers? Problem solving.

The number one thing an Engineer does is problem solving. Now you may say, "hey, that's the same as my profession" - well this would be true for virtually every single profession on earth. This is not saying there isn't problem solving required in other professions. Some problems require very basic problem solving techniques such is used in every day life, but sometimes problems get more complicated, maybe they involve other parties, maybe its a specific quirk of the system in a specific scenario. One thing you learn in engineering is that not all problems are equal. These are

 The stages of problem solving like a pro:

Is the problem identified (no, really, are you actually asking the right question?)

Have you applied related troubleshooting step to above problem?

Have you applied basic troubleshooting steps (i.e. check if its plugged in, turned it on and off again, checked your basics)

Tried step 2 again? (Desperation seeps in, but check your bases)

Asked a colleague or someone else that may have dealt with your problem? (50/50 at this point)

Asked DR. Google (This is still ok)

Deployed RTFM protocol (Read the F***ing Manual - Engineers are notorious for not doing this)

Repeated tests, changing slight things, checking relation to time, or number of people, or location or environment (we are getting DEEP now)

Go to the bottom level, in networking this is packet sniffers to inspect packets, in systems this is taking systems apart and testing in isolation, in software this is checking if 1 equals 1, you are trying to prove basic human facts that everyone knows. If 1 is not equal to 1, you're in deep trouble.At this point you are at rebuild from scratch, re install, start again as your answer (extremely expensive, very rare)

And there you have it! Those are your levels of problem solving. As you go through each step, the more expensive the problem is. -- BUT WAIT. I picked something up along the way and this is where I typically thrive. Somewhere between problem solving step 8 and 10. 

The secret step

My recommendation at this point is to try tests that are seemingly unrelated to anything to do with the problem at all.Pull a random cable, test with a random system off/on, try it at a specific time of the day, try it specifically after restarting or replugging something in. Now, not completely random but within some sort of scope. These test are the ones that when someone is having a problem when you suggest they say "that shouldn't fix the problem, that shouldn't be related" and they are absolutely correct.But here's the thing -- at this stage they have already tried everything that SHOULD fix the problem. Now it's time for the hail mary's, the long shots, the clutching at straws. This method works wonders for many reasons. 1. You really are trying to try "anything" at this point.

2. Most of the time we may think we have problem solving step number 1 covered, but we really don't.

3. Triggering correlations.

This is important.

Triggering correlations

In a later post I will cover correlation vs causation, but for now understand that sometimes all you want to do is throw in new inputs to the system or problem you are solving in order to get clues or re identify problems or give new ways to approach earlier problem solving steps. There you have it. Problem solve like a ninja. Approach that extremely experienced and smart person what their problem and as they describe all the things they've tried, throw in a random thing they haven't tried. And when they say, well that shouldn't fix it, you ask them, well if you've exhausted everything that should  have worked, this is the time to try things that shouldn't. Either they will think of more tests they haven't considered so as to avoid doing your preposterous idea OR they try it and get a new clue to their problem. Heck, at worst they confirm that they do know SOMETHING about the system.

Go out and problem solve ! As always, thanks for reading and good luck with all of your side hustles.

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• TeachEngineering
• Problem Solving

Lesson Problem Solving

Grade Level: 8 (6-8)

(two 40-minute class periods)

Lesson Dependency: The Energy Problem

Subject Areas: Physical Science, Science and Technology

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Curriculum in this Unit Units serve as guides to a particular content or subject area. Nested under units are lessons (in purple) and hands-on activities (in blue). Note that not all lessons and activities will exist under a unit, and instead may exist as "standalone" curriculum.

• Energy Forms and States Demonstrations
• Energy Conversions
• Watt Meters to Measure Energy Consumption
• Household Energy Audit
• Light vs. Heat Bulbs
• Efficiency of an Electromechanical System
• Efficiency of a Water Heating System
• Solving Energy Problems
• Energy Projects

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Engineering connection, learning objectives, worksheets and attachments, more curriculum like this, introduction/motivation, associated activities, user comments & tips.

Scientists, engineers and ordinary people use problem solving each day to work out solutions to various problems. Using a systematic and iterative procedure to solve a problem is efficient and provides a logical flow of knowledge and progress.

• Students demonstrate an understanding of the Technological Method of Problem Solving.
• Students are able to apply the Technological Method of Problem Solving to a real-life problem.

Educational Standards Each TeachEngineering lesson or activity is correlated to one or more K-12 science, technology, engineering or math (STEM) educational standards. All 100,000+ K-12 STEM standards covered in TeachEngineering are collected, maintained and packaged by the Achievement Standards Network (ASN) , a project of D2L (www.achievementstandards.org). In the ASN, standards are hierarchically structured: first by source; e.g. , by state; within source by type; e.g. , science or mathematics; within type by subtype, then by grade, etc .

Ngss: next generation science standards - science.

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International Technology and Engineering Educators Association - Technology

State standards, national science education standards - science.

Scientists, engineers, and ordinary people use problem solving each day to work out solutions to various problems. Using a systematic and iterative procedure to solve a problem is efficient and provides a logical flow of knowledge and progress.

In this unit, we use what is called "The Technological Method of Problem Solving." This is a seven-step procedure that is highly iterative—you may go back and forth among the listed steps, and may not always follow them in order. Remember that in most engineering projects, more than one good answer exists. The goal is to get to the best solution for a given problem. Following the lesson conduct the associated activities Egg Drop and Solving Energy Problems for students to employ problem solving methods and techniques. 

Lesson Background and Concepts for Teachers

The overall concept that is important in this lesson is: Using a standard method or procedure to solve problems makes the process easier and more effective.

The specific process of problem solving used in this unit was adapted from an eighth-grade technology textbook written for New York State standard technology curriculum. The process is shown in Figure 1, with details included below. The spiral shape shows that this is an iterative, not linear, process. The process can skip ahead (for example, build a model early in the process to test a proof of concept) and go backwards (learn more about the problem or potential solutions if early ideas do not work well).

This process provides a reference that can be reiterated throughout the unit as students learn new material or ideas that are relevant to the completion of their unit projects.

Brainstorming about what we know about a problem or project and what we need to find out to move forward in a project is often a good starting point when faced with a new problem. This type of questioning provides a basis and relevance that is useful in other energy science and technology units. In this unit, the general problem that is addressed is the fact that Americans use a lot of energy, with the consequences that we have a dwindling supply of fossil fuels, and we are emitting a lot of carbon dioxide and other air pollutants. The specific project that students are assigned to address is an aspect of this problem that requires them to identify an action they can take in their own live to reduce their overall energy (or fossil fuel) consumption.

The Seven Steps of Problem Solving

1.  Identify the problem

Clearly state the problem. (Short, sweet and to the point. This is the "big picture" problem, not the specific project you have been assigned.)

2.  Establish what you want to achieve

• Completion of a specific project that will help to solve the overall problem.
• In one sentence answer the following question: How will I know I've completed this project?
• List criteria and constraints: Criteria are things you want the solution to have. Constraints are limitations, sometimes called specifications, or restrictions that should be part of the solution. They could be the type of materials, the size or weight the solution must meet, the specific tools or machines you have available, time you have to complete the task and cost of construction or materials.

3.  Gather information and research

• Research is sometimes needed both to better understand the problem itself as well as possible solutions.
• Don't reinvent the wheel – looking at other solutions can lead to better solutions.
• Use past experiences.

4.  Brainstorm possible solutions

List and/or sketch (as appropriate) as many solutions as you can think of.

5.  Choose the best solution

Evaluate solution by: 1) Comparing possible solution against constraints and criteria 2) Making trade-offs to identify "best."

6.  Implement the solution

• Develop plans that include (as required): drawings with measurements, details of construction, construction procedure.
• Define tasks and resources necessary for implementation.
• Implement actual plan as appropriate for your particular project.

7.  Test and evaluate the solution

• Compare the solution against the criteria and constraints.
• Define how you might modify the solution for different or better results.
• Egg Drop - Use this demonstration or activity to introduce and use the problem solving method. Encourages creative design.
• Solving Energy Problems - Unit project is assigned and students begin with problem solving techniques to begin to address project. Mostly they learn that they do not know enough yet to solve the problem.
• Energy Projects - Students use what they learned about energy systems to create a project related to identifying and carrying out a personal change to reduce energy consumption.

The results of the problem solving activity provide a basis for the entire semester project. Collect and review the worksheets to make sure that students are started on the right track.

Learn the basics of the analysis of forces engineers perform at the truss joints to calculate the strength of a truss bridge known as the “method of joints.” Find the tensions and compressions to solve systems of linear equations where the size depends on the number of elements and nodes in the trus...

Through role playing and problem solving, this lesson sets the stage for a friendly competition between groups to design and build a shielding device to protect humans traveling in space. The instructor asks students—how might we design radiation shielding for space travel?

A process for technical problem solving is introduced and applied to a fun demonstration. Given the success with the demo, the iterative nature of the process can be illustrated.

The culminating energy project is introduced and the technical problem solving process is applied to get students started on the project. By the end of the class, students should have a good perspective on what they have already learned and what they still need to learn to complete the project.

Hacker, M, Barden B., Living with Technology , 2nd edition. Albany NY: Delmar Publishers, 1993.

Other Related Information

This lesson was originally published by the Clarkson University K-12 Project Based Learning Partnership Program and may be accessed at http://internal.clarkson.edu/highschool/k12/project/energysystems.html.

Contributors

Supporting program, acknowledgements.

This lesson was developed under National Science Foundation grants no. DUE 0428127 and DGE 0338216. However, these contents do not necessarily represent the policies of the National Science Foundation, and you should not assume endorsement by the federal government.

Last modified: August 16, 2023

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• Steps in solving well-defined engineering process problems, including textbook problems
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Engineering Problem Solving ¶

Some problems are so complex that you have to be highly intelligent and well-informed just to be undecided about them. —Laurence J. Peter

Steps in solving ‘real world’ engineering problems ¶

The following are the steps as enumerated in your textbook:

Collaboratively define the problem

List possible solutions

Evaluate and rank the possible solutions

Develop a detailed plan for the most attractive solution(s)

Re-evaluate the plan to check desirability

Implement the plan

Check the results

A critical part of the analysis process is the ‘last’ step: checking and verifying the results.

Depending on the circumstances, errors in an analysis, procedure, or implementation can have significant, adverse consequences (NASA Mars orbiter crash, Bhopal chemical leak tragedy, Hubble telescope vision issue, Y2K fiasco, BP oil rig blowout, …).

In a practical sense, these checks must be part of a comprehensive risk management strategy.

My experience with problem solving in industry was pretty close to this, though encumbered by numerous business practices (e.g., ‘go/no-go’ tollgates, complex approval processes and procedures).

In addition, solving problems in the ‘real world’ requires a multidisciplinary effort, involving people with various expertise: engineering, manufacturing, supply chain, legal, marketing, product service and warranty, …

Exercise: Problem solving

Step 3 above refers to ranking of alternatives.

Think of an existing product of interest.

What do you think was ranked highest when the product was developed?

Consider what would have happened if a different ranking was used. What would have changed about the product?

Brainstorm ideas with the students around you.

Defining problems collaboratively ¶

Especially in light of global engineering , we need to consider different perspectives as we define our problem. Let’s break the procedure down into steps:

Identify each perspective that is involved in the decision you face. Remember that problems often mean different things in different perspectives. Relevant differences might include national expectations, organizational positions, disciplines, career trajectories, etc. Consider using the mnemonic device “Location, Knowledge, and Desire.”

Location : Who is defining the problem? Where are they located or how are they positioned? How do they get in their positions? Do you know anything about the history of their positions, and what led to the particular configuration of positions you have today on the job? Where are the key boundaries among different types of groups, and where are the alliances?

Knowledge : What forms of knowledge do the representatives of each perspective have? How do they understand the problem at hand? What are their assumptions? From what sources did they gain their knowledge? How did their knowledge evolve?

Desire : What do the proponents of each perspective want? What are their objectives? How do these desires develop? Where are they trying to go? Learn what you can about the history of the issue at hand. Who might have gained or lost ground in previous encounters? How does each perspective view itself at present in relation to those it envisions as relevant to its future?

As formal problem definitions emerge, ask “Whose definition is this?” Remember that “defining the problem clearly” may very well assert one perspective at the expense of others. Once we think about problem solving in relation to people, we can begin to see that the very act of drawing a boundary around a problem has non-technical, or political dimensions, depending on who controls the definition, because someone gains a little power and someone loses a little power.

Map what alternative problem definitions mean to different participants. More than likely you will best understand problem definitions that fit your perspective. But ask “Does it fit other perspectives as well?” Look at those who hold Perspective A. Does your definition fit their location, their knowledge, and their desires? Now turn to those who hold Perspective B. Does your definition fit their location, knowledge, and desires? Completing this step is difficult because it requires stepping outside of one’s own perspective and attempting to understand the problem in terms of different perspectives.

To the extent you encounter disagreement or conclude that the achievement of it is insufficient, begin asking yourself the following: How might I adapt my problem definition to take account of other perspectives out there? Is there some way of accommodating myself to other perspectives rather than just demanding that the others simply recognize the inherent value and rationality of mine? Is there room for compromise among contrasting perspectives?

How ‘good’ a solution do you need ¶

There is also an important aspect of real-world problem solving that is rarely articulated and that is the idea that the ‘quality’ of the analysis and the resources expended should be dependent on the context.

This is difficult to assess without some experience in the particular environment.

How ‘Good’ a Solution Do You Need?

Some rough examples:

10 second answer (answering a question at a meeting in front of your manager or vice president)

10 minute answer (answering a quick question from a colleague)

10 hour answer (answering a request from an important customer)

10 day answer (assembling information as part of a trouble-shooting team)

10 month answer (putting together a comprehensive portfolio of information as part of the design for a new $200,000,000 chemical plant)

Steps in solving well-defined engineering process problems, including textbook problems ¶

Essential steps:

Carefully read the problem statement (perhaps repeatedly) until you understand exactly the scenario and what is being asked.

Translate elements of the word problem to symbols. Also, look for key words that may convey additional information, e.g., ‘steady state’, ‘constant density’, ‘isothermal’. Make note of this additional information on your work page.

Draw a diagram. This can generally be a simple block diagram showing all the input, output, and connecting streams.

Write all known quantities (flow rates, densities, etc.) from step 2 in the appropriate locations on, or near, the diagram. If symbols are used to designate known quantities, include those symbols.

Identify and assign symbols to all unknown quantities and write them in the appropriate locations on, or near, the diagram.

Construct the relevant equation(s). These could be material balances, energy balances, rate equations, etc.

Write down all equations in their general forms. Don’t simplify anything yet.

Discard terms that are equal to zero (or are assumed negligible) for your specific problem and write the simplified equations.

Replace remaining terms with more convenient forms (because of the given information or selected symbols).

Construct equations to express other known relationships between variables, e.g., relationships between stoichiometric coefficients, the sum of species mass fractions must be one.

Whenever possible, solve the equations for the unknown(s) algebraically .

Convert the units of your variables as needed to have a consistent set across your equations.

Substitute these values into the equation(s) from step 7 to get numerical results.

Check your answer.

Does it make sense?

Are the units of the answer correct?

Is the answer consistent with other information you have?

Exercise: Checking results

How do you know your answer is right and that your analysis is correct?

This may be relatively easy for a homework problem, but what about your analysis for an ill-defined ‘real-world’ problem?

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1.7: Problem Solving Process

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Learning how to use a structured problem solving process will help you to be more organized and support your future courses. Also, it will train your brain how to approach problems. Just like basketball players practice jump shots over and over to train their body how to act in high pressure scenarios, if you are comfortable and familiar with a structured problem solving process, when you’re in a high pressure situation like a test, you can just jump into the problem like muscle memory.

6 Step Problem Solving Method:

• Write out the answer with all necessary information that is given to you. It feels like it takes forever, but it’s important to have the problem and solution next to each other.
• Draw the problem, this is usually a free-body diagram (don’t forget a coordinate frame). Eventually, as you get further into the course, you might need a few drawings. One would be a quick sketch of the problem in the real world, then modelling it into a simplified engineering drawing, and finally the free-body diagram.
• Write out a list of the known/given values with the variable and unit, i.e m = 14 kg   (variable = number unit)
• Write out a list of the unknown values that you will have to solve for in order to solve the problem
• You can also add any assumptions you made here that change the problem.
• Also state any constants, i.e. g = 32.2 ft/m 2   or g = 9.81 m/s 2
• This step helps you to have all of the information in one place when you solve the problem. It’s also important because each number should include units, so you can see if the units match or if you need to convert some numbers so they are all in English or SI. This also gives you the variables side by side to ensure they are unique (so you don’t accidentally have 2 ‘d’ variables and can rename one with a subscript).
• Write a simple sentence or phrase explaining what method/approach you will be using to solve the problem.
• For example: ‘use method of joints’, or equilibrium equations for a rigid body, MMOI for a certain shape, etc.
• This is going to be more important when you get to the later chapters and especially next semester in Dynamics where you can solve the same problem many ways. Might as well practice now!
• This is the actual solving step. This is where you show all the work you have done to solve the problem.
• When you get an answer, restate the variable you are solving for, include the unit, and put a box around the answer.
• Write a simple sentence explaining why (or why not) your answer makes sense. Use logic and common sense for this step.
• When possible, use a second quick numerical analysis to verify your answer. This is the “gut check” to do a quick calculation to ensure your answer is reasonable.
• This is the most confusing step as students often don’t know what to put here and up just writing ‘The number looks reasonable’. This step is vitally important to help you learn how to think about your answer. What does that number mean? What is it close to? For example, if you find that x = 4000 m, that’s a very large distance! In the review, I would say, ‘the object is 4 km long which is reasonable for a long bridge’. See how this is compared to something similar? Or you could do a second calculation to verify the number is correct, such as adding up multiple parts of the problem to confirm the total length is accurate i.e. ‘x + y + z = total, yes it works!’

Additional notes for this course:

• It’s important to include the number and label the steps so it’s clear what you’re doing, as shown in the example below.
• It’s okay if you make mistakes, just put a line through it and keep going.
• Remember your header should include your name, the page number, total number of pages, the course number, and the assignment number. If a problem spans a number of pages, you should include it in the header too.

Key Takeaways

Basically: Use a 6-step structured problem solving process: 1. Problem, 2. Draw, 3. Known & Unknown, 4. Approach, 5. Analysis (Solve), 6. Review

Application: In your future job there is likely a structure for analysis reports that will be used. Each company has a different approach, but most have a standard that should be followed. This is good practice.

Looking ahead: This will be part of every homework assignment.

Written by Gayla & Libby

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A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering: Guidance for Teaching and Assessment

• Argenta M. Price
• Candice J. Kim
• Eric W. Burkholder
• Amy V. Fritz
• Carl E. Wieman

*Address correspondence to: Argenta M. Price ( E-mail Address: [email protected] ).

Department of Physics, Stanford University, Stanford, CA 94305

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Graduate School of Education, Stanford University, Stanford, CA 94305

School of Medicine, Stanford University, Stanford, CA 94305

Department of Electrical Engineering, Stanford University, Stanford, CA 94305

A primary goal of science and engineering (S&E) education is to produce good problem solvers, but how to best teach and measure the quality of problem solving remains unclear. The process is complex, multifaceted, and not fully characterized. Here, we present a detailed characterization of the S&E problem-solving process as a set of specific interlinked decisions. This framework of decisions is empirically grounded and describes the entire process. To develop this, we interviewed 52 successful scientists and engineers (“experts”) spanning different disciplines, including biology and medicine. They described how they solved a typical but important problem in their work, and we analyzed the interviews in terms of decisions made. Surprisingly, we found that across all experts and fields, the solution process was framed around making a set of just 29 specific decisions. We also found that the process of making those discipline-general decisions (selecting between alternative actions) relied heavily on domain-specific predictive models that embodied the relevant disciplinary knowledge. This set of decisions provides a guide for the detailed measurement and teaching of S&E problem solving. This decision framework also provides a more specific, complete, and empirically based description of the “practices” of science.

INTRODUCTION

Many faculty members with new graduate students and many managers with employees who are recent college graduates have had similar experiences. Their advisees/employees have just completed a program of rigorous course work, often with distinction, but they seem unable to solve the real-world problems they encounter. The supervisor struggles to figure out exactly what the problem is and how they can guide the person in overcoming it. This paper is providing a way to answer those questions in the context of science and engineering (S&E). By characterizing the problem-solving process of experts, this paper investigates the “mastery” performance level and specifies an overarching learning goal for S&E students, which can be taught and measured to improve teaching.

The importance of problem solving as an educational outcome has long been recognized, but too often postsecondary S&E graduates have serious difficulties when confronted with real-world problems ( Quacquarelli Symonds, 2018 ). This reflects two long-standing educational problems with regard to problem solving: how to properly measure it, and how to effectively teach it. We theorize that the root of these difficulties is that good “problem solving” is a complex multifaceted process, and the details of that process have not been sufficiently characterized. Better characterization of the problem-solving process is necessary to allow problem solving, and more particularly, the complex set of skills and knowledge it entails, to be measured and taught more effectively. We sought to create an empirically grounded conceptual framework that would characterize the detailed structure of the full problem-solving process used by skilled practitioners when solving problems as part of their work. We also wanted a framework that would allow use and comparison across S&E disciplines. To create such a framework, we examined the operational decisions (choices among alternatives that result in subsequent actions) that these practitioners make when solving problems in their discipline.

Various aspects of problem solving have been studied across multiple domains, using a variety of methods (e.g., Newell and Simon, 1972 ; Dunbar, 2000 ; National Research Council [NRC], 2012b ; Lintern et al. , 2018 ). These ranged from expert self-reflections (e.g., Polya, 1945 ), to studies on knowledge lean tasks to discover general problem-solving heuristics (e.g., Egan and Greeno, 1974 ), to comparisons of expert and novice performances on simplified problems across a variety of disciplines (e.g., Chase and Simon, 1973 ; Chi et al. , 1981 ; Larkin and Reif, 1979 ; Ericsson et al. , 2006 , 2018 ). These studies revealed important novice–expert differences—notably, that experts are better at identifying important features and have knowledge structures that allow them to reduce demands on working memory. Studies that specifically gave the experts unfamiliar problems in their disciplines also found that, relative to novices, they had more deliberate and reflective strategies, including more extensive planning and managing of their own behavior, and they could use their knowledge base to better define the problem ( Schoenfeld, 1985 ; Wineburg, 1998 ; Singh, 2002 ). While these studies focused on discrete cognitive steps of the individual, an alternative framing of problem solving has been in terms of “ecological psychology” of “situativity,” looking at how the problem solver views and interacts with the environment in terms of affordances and constraints ( Greeno, 1994 ). “Naturalistic decision making” is a related framework that specifically examines how experts make decisions in complex, real-world, settings, with an emphasis on the importance of assessing the situation surrounding the problem at hand ( Klein, 2008 ; Mosier et al. , 2018 ).

While this work on expertise has provided important insights into the problem-solving process, its focus has been limited. Most has focused on looking for cognitive differences between experts and novices using limited and targeted tasks, such as remembering the pieces on a chessboard ( Chase and Simon, 1973 ) or identifying the important concepts represented in an introductory physics textbook problem ( Chi et al. , 1981 ). It did not attempt to explore the full process of solving, particularly for solving the type of complex problem that a scientist or engineer encounters as a member of the workforce (“authentic problems”).

There have also been many theoretical proposals as to expert problem-solving practices, but with little empirical evidence as to their completeness or accuracy (e.g., Polya, 1945 ; Heller and Reif, 1984 ; Organisation for Economic Cooperation and Development [OECD], 2019 ). The work of Dunbar (2000) is a notable exception to the lack of empirical work, as his group did examine how biologists solved problems in their work by analyzing lab meetings held by eight molecular biology research groups. His groundbreaking work focused on creativity and discovery in the research process, and he identified the importance of analogical reasoning and distributed reasoning by scientists in answering research questions and gaining new insights. Kozma et al. (2000) studied professional chemists solving problems, but their work focused only on the use of specialized representations.

The “cognitive systems engineering” approach ( Lintern et al. , 2018 ) takes a more empirically based approach looking at experts solving problems in their work, and as such tends to span aspects of both the purely cognitive and the ecological psychological theories. It uses both observations of experts in authentic work settings and retrospective interviews about how experts carried out particular work tasks. This theoretical framing and the experimental methods are similar to what we use, particularly in the “naturalistic decision making” area of research ( Mosier et al. , 2018 ). That work looks at how critical decisions are made in solving specific problems in their real-world setting. The decision process is studied primarily through retrospective interviews about challenging cases faced by experts. As described below, our methods are adapted from that work ( Crandall et al. , 2006 ), though there are some notable differences in focus and field. A particular difference is that we focused on identifying what are decisions to be made, which are more straight-forward to identify from retrospective interviews than how those decisions are made. We all have the same ultimate goal, however, to improve the training/teaching of the respective expertise.

Problem solving is central to the processes of science, engineering, and medicine, so research and educational standards about scientific thinking and the process and practices of science are also relevant to this discussion. Work by Osborne and colleagues describes six styles of scientific reasoning that can be used to explain how scientists and students approach different problems ( Kind and Osborne, 2016 ). There are also numerous educational standards and frameworks that, based on theory, lay out the skills or practices that science and engineering students are expected to master (e.g., American Association for the Advancement of Science [AAAS], 2011 ; Next Generation Science Standards Lead States, 2013 ; OECD, 2019 ; ABET, 2020 ). More specifically related to the training of problem solving, Priemer et al. (2020) synthesizes literature on problem solving and scientific reasoning to create a “STEM [science, technology, engineering, and mathematics] and computer science framework for problem solving” that lays out steps that could be involved in a students’ problem-solving efforts across STEM fields. These frameworks provide a rich groundwork, but they have several limitations: 1) They are based on theoretical ideas of the practice of science, not empirical evidence, so while each framework contains overlapping elements of the problem-solving process, it is unclear whether they capture the complete process. 2) They are focused on school science, rather than the actual problem solving that practitioners carry out and that students will need to carry out in future STEM careers. 3) They are typically underspecified, so that the steps or practices apply generally, but it is difficult to translate them into measurable learning goals for students to practice. Working to address that, Clemmons et al. (2020) recently sought to operationalize the core competencies from the Vision and Change report ( AAAS, 2011 ), establishing a set of skills that biology students should be able to master.

Our work seeks to augment this prior work by building a conceptual framework that is empirically based, grounded in how scientists and engineers solve problems in practice instead of in school. We base our framework on the decisions that need to be made during problem solving, which makes each item clearly defined for practice and assessment. In our analysis of expert problem solving, we empirically identified the entire problem-solving process. We found this includes deciding when and how to use the steps and skills defined in the work described previously but also includes additional elements. There are also questions in the literature about how generalizable across fields a particular set of practices may be. Here, we present the first empirical examination of the entire problem-solving process, and we compare that process across many different S&E disciplines.

A variety of instructional methods have been used to try and teach science and engineering problem solving, but there has been little evidence of their efficacy at improving problem solving (for a review, see NRC, 2012b ). Research explicitly on teaching problem solving has primarily focused on textbook-type exercises and utilized step-by-step strategies or heuristics. These studies have shown limited success, often getting students to follow specific procedural steps but with little gain in actually solving problems and showing some potential drawbacks ( Heller and Reif, 1984 ; Heller et al. , 1992 ; Huffman, 1997 ; Heckler, 2010 ; Kuo et al. , 2017 ). As discussed later, the framework presented here offers guidance for different and potentially more effective approaches to teaching problem solving.

These challenges can be illustrated by considering three different problems taken from courses in mechanical engineering, physics, and biology, respectively ( Figure 1 ). All of these problems are challenging, requiring considerable knowledge and effort by the student to solve correctly. Problems such as these are routinely used to both assess students’ problem-solving skills, and students are expected to learn such skills by practicing doing such problems. However, it is obvious to any expert in the respective fields, that, while these problems might be complicated and difficult to answer, they are vastly different from solving authentic problems in that field. They all have well-defined answers that can be reached by straightforward solution paths. More specifically, they do not involve needing to use judgment to make any decisions based on limited information (e.g., insufficient to specify a correct decision with certainty). The relevant concepts and information and assumptions are all stated or obvious. The failure of problems like these to capture the complexity of authentic problem solving underlies the failure of efforts to measure and teach problem solving. Recognizing this failure motivated our efforts to more completely characterize the problem-solving process of practicing scientists, engineers, and doctors.

FIGURE 1. Example problems from courses or textbooks in mechanical engineering, physics and biology. Problems from: Mechanical engineering: Wayne State mechanical engineering sample exam problems (Wayne State, n.d.), Physics: A standard physics problem in nearly every advanced quantum mechanics course, Biology: Molecular Biology of the Cell 6th edition, Chapter 7 end of chapter problems ( Alberts et al ., 2014 ).

We are building on the previous work studying expert–novice differences and problem solving but taking a different direction. We sought to create an empirically grounded framework that would characterize the detailed structure of the full problem-solving process by focusing on the operational decisions that skilled practitioners make when successfully solving authentic problems in their scientific, engineering, or medical work. We chose to identify the decisions that S&E practitioners made, because, unlike potentially nebulous skills or general problem-solving steps that might change with the discipline, decisions are sufficiently specified that they can be individually practiced by students and measured by instructors or departments. The authentic problems that we analyzed are typical problems practitioners encounter in “doing” the science or engineering entailed in their jobs. In the language of traditional problem-
solving and expertise research, such authentic problems are “ill-structured” ( Simon, 1973 ) and require “adaptive expertise” ( Hatano and Inagaki, 1986 ) to solve. However, our authentic problems are considerably more complex and unstructured than what is normally considered in those literatures, because not only do they lack a clear solution path, but in many cases, it is not clear a priori that they have any solution at all. Determining that, and whether the problem needs to be redefined to be soluble, is part of the successful expert solution process. Another way in which our set of decisions goes beyond the characterization of what is involved in adaptive expertise is the prominent role of making judgments with limited information.

A common reaction of scientists and engineers to seeing the list of decisions we obtain as our primary result is, “Oh, yes, these are things I always do in solving problems. There is nothing new here.” It is comforting that these decisions all look familiar; that supports their validity. However, what is new is not that experts are making such decisions, but rather that there is a relatively small but complete set of decisions that has now been explicitly identified and that applies so generally.

We have used a much larger and broader sample of experts in this work than used in prior expert–novice studies, and we used a more stringent selection criterion. Previous empirical work has typically involved just a few experts, almost always in a single domain, and included graduate students as “experts” in some cases. Our semistructured interview sample was 31 experienced practitioners from 10 different disciplines of science, engineering, and medicine, with demonstrated competence and accomplishments well beyond those of most graduate students. Also, approximately 25 additional experts from across science, engineering, and medicine served as consultants during the planning and execution of this work.

Our research question was: What are the decisions experts make in solving authentic problems, and to what extent is this set of decisions to be made consistent both within and across disciplines?

Our approach was designed to identify the level of consistency and unique differences across disciplines. Our hypothesis was that there would be a manageable number (20–50) of decisions to be made, with a large amount of overlap of decisions made between experts within each discipline and a substantial but smaller overlap across disciplines. We believed that if we had found that every expert and/or discipline used a large and completely unique set of decisions, it would have been an interesting research result but of little further use. If our hypothesis turned out to be correct, we expected that the set of decisions obtained would have useful applications in guiding teaching and assessment, as they would show how experts in the respective disciplines applied their content knowledge to solve problems and hence provide a model for what to teach. We were not expecting to find the nearly complete degree of overlap in the decisions made across all the experts.

We first conducted 22 relatively unstructured interviews with a range of S&E experts, in which we asked about problem-solving expertise in their fields. From these interviews, we developed an initial list of decisions to be made in S&E problem solving. To refine and validate the list, we then carried out a set of 31 semistructured interviews in which S&E experts chose a specific problem from their work and described the solution process in detail. The semistructured interviews were coded for the decisions represented, either explicitly stated or implied by a choice of action. This provided a framework of decisions that characterize the problem-solving process across S&E disciplines. The research was approved by the Stanford Institutional Review Board (IRB no. 48785), and informed consent was obtained from all the participants.

This work involved interviewing many experts across different fields. We defined experts as practicing scientists, engineers, or physicians with considerable experience working as faculty at highly rated universities or having several years of experience working in moderately high-level technical positions at successful companies. We also included a few longtime postdocs and research staff in biosciences to capture more details of experimental decisions from which faculty members in those fields often were more removed. This definition of expert allows us to identify the practices of skilled professionals; we are not studying what makes only the most exceptional experts unique.

Experts were volunteers recruited through direct contact via the research team's personal and professional networks and referrals from experts in our networks. This recruitment method likely biased our sample toward people who experienced relatively similar training (most were trained in STEM disciplines at U.S. universities within the last 15–50 years). Within this limitation, we attempted to get a large range of experts by field and experience. This included people from 10 different fields (including molecular biology/biochemistry, ecology, and medicine), 11 U.S. universities, and nine different companies or government labs, and the sample was 33% female (though our engineering sample only included one female). The medical experts were volunteers from a select group of medical school faculty chosen to serve as clinical reasoning mentors for medical students at a prestigious university. We only contacted people who met our criteria for being an “expert,” and everyone who volunteered was included in the study. Most of the people who were contacted volunteered, and the only reason given for not volunteering was insufficient time. Other than their disciplinary expertise, there was little to distinguish these experts beyond the fact they were acquaintances with members of the team or acquaintances of acquaintances of team or project advisory board members. The precise number from each field was determined largely by availability of suitable experts.

We defined an “authentic problem” to be one that these experts solve in their actual jobs. Generally, this meant research projects for the science and engineering faculty, design problems for the industry engineers, and patient diagnoses for the medical doctors. Such problems are characterized by complexity, with many factors involved and no obvious solution process, and involve substantial time, effort, and resources. Such problems involve far more complexity and many more decisions, particularly decisions with limited information, than the typical problems used in previous problem-solving research or used with students in instructional settings.

Creating an Initial List of Problem-Solving Decisions

We first interviewed 22 experts ( Table 1 ), most of whom were faculty at a prestigious university, in which we asked them to discuss expertise and problem solving in their fields as it related to their own experiences. This usually resulted in their discussing examples of one or more problems they had solved. Based on the first seven interviews, plus reflections on personal experience from the research team and review of the literature on expert problem solving and teaching of scientific practices ( Ericsson et al. , 2006 ; NRC, 2012a ; Wieman, 2015 ), we created a generic list of decisions that were made in S&E problem solving. In the rest of the unstructured interviews (15), we also provided the experts with our list and asked them to comment on any additions or deletions they would suggest. Faculty who had close supervision of graduate students and industry experts who had extensively supervised inexperienced staff were particularly informative. Their observations of the way inexperienced people could fail made them sensitive to the different elements of expertise and where incorrect decisions could be made. Although we initially expected to find substantial differences across disciplines, from early in the process, we noted a high degree of overlap across the interviews in the decisions that were described.

URM (under-represented minority) included 3 African American and 2 Hispanic/Latinx. One medical faculty member was interviewed twice – in both informal and structure interviews, for a total of 53 interviews with 52 experts.

Refinement and Validation of the List of Decisions

After creating the preliminary list of decisions from the informal interviews, we conducted a separate set of more structured interviews to test and refine the list. Semistructured interviews were conducted with 31 experts from across science, engineering, and medical fields ( Table 1 ). For these interviews, we recruited experts from a range of universities and companies, though the range of institutions is still limited, given the sample size. Interviews were conducted in person or over video chat and were transcribed for analysis. In the semistructured interviews, experts were asked to choose a problem or two from their work that they could recall the details of solving and then describe the process, including all the steps and decisions they made. So that we could get a full picture of the successful problem-solving process, we decided to focus the interviews on problems that they had eventually solved successfully, though their processes inherently involved paths that needed to be revised and reconsidered. Transcripts from interviewees who agreed to have their interview transcript published are available in the supplemental data set.

Our interview protocol (see Supplemental Text) was inspired in part by the critical decision method of cognitive task analysis ( Crandall et al. , 2006 ; Lintern et al. , 2018 ), which was created for research in cognitive systems engineering and naturalistic decision making. There are some notable differences between our work and theirs, both in research goal and method. First, their goal is to improve training in specific fields by focusing on how critical decisions are made in that field during an unusual or important event; the analysis seeks to identify factors involved in making those critical decisions. We are focusing on the overall problem solving and how it compares across many different fields, which quickly led to attention on what decisions are to be made, rather than how a limited set of those decisions are made. We asked experts to describe a specific, but not necessarily unusual, problem in their work, and focused our analysis on identifying all decisions made, not reasons for making them or identifying which were most critical. The specific order of problem-solving steps was also less important to us, in part because it was clear that there was no consistent order that was followed. Second, we are looking at different types of work. Cognitive systems engineering work has primarily focused on performance in professions like firefighters, power plant operators, military technicians, and nurses. These tend to require time-sensitive critical skills that are taught with modest amounts of formal training. We are studying scientists, engineers, and doctors solving problems that require much longer and less time-critical solutions and for which the formal training occupies many years.

Given our different focus, we made several adaptations to eliminate some of the more time-consuming steps from the interview protocol, allowing us to limit the interview time to approximately 1 hour. Both protocols seek to elicit an accurate and complete reporting of the steps taken and decisions made in the process of solving a problem. Our general strategy was: 1) Have the expert explain the problem and talk step by step through the decisions involved in solving it, with relatively few interruptions from the interviewer except to keep the discussion focused on the specific problem and occasionally to ask for clarifications. 2) Ask follow-up questions to probe for more detail about particular steps and aspects of the problem-solving process. 3) Occasionally ask for general thoughts on how a novice's process might differ.

While some have questioned the reliability of information from retrospective interviews ( Nisbett and Wilson, 1977 ), we believe we avoid these concerns, because we are only identifying a decision to be made, which in this case, means identifying a well-defined action that was chosen from alternatives. This is less subjective and much more likely to be accurately recalled than is the rationale behind such a decision. See Ericsson and Simon (1980) . However, the decisions identified may still be somewhat limited—the process of deciding among possible actions might involve additional decisions in the moment, when the solution is still unknown, that we are unable to capture in the retrospective context. For the decisions we can identify, we are able to check their accuracy and completeness by comparing them with the actions taken in the conduct of the research/design. For example, consider this quote from a physician who had to re-evaluate a diagnosis, “And, in my very subjective sense, he seemed like he was being forthcoming and honest. Granted people can fool you, but he seemed like he was being forthcoming. So we had to reevaluate.” The physician then considered alternative diagnoses that could explain a test result that at first had indicated an incorrect diagnosis. While this quote does describe the (retrospective) reasoning behind a decision, we do not need to know whether that reasoning is accurately recalled. We can simply code this as “decision 18, how believable is info?” The physician followed up by considering alternative diagnoses, which in this context was coded as “26, how good is solution?” and “8, potential solutions?” This was followed by the description of the literature and additional tests conducted. These indicated actions taken that confirm the physician made a decision about the reliability of the information given by the patient.

Interview Coding

We coded the semistructured interviews in terms of decisions made, through iterative rounds of coding ( Chi, 1997 ), following a “directed content analysis approach,” which involves coding according to predefined theoretical categories and updating the codes as needed based on the data ( Hsieh and Shannon, 2005 ). Our predefined categories were the list of decisions we had developed during the informal interviews. This approach means that we limited the focus of our qualitative analysis—we were able to test and refine the list of decisions, but we did not seek to identify all possible categories of approach to selecting and solving problems. The goals of each iterative round of coding are described in the next three paragraphs. To code for decisions in general, we matched decisions from the list to statements in each interview, based on the following criteria: 1) there was an explicit statement of a decision or choice made or needing to be made; 2) there was the description of the outcome of a decision, such as listing important features of the problem (that had been decided on) or conclusions arrived at; or 3) there was a statement of actions taken that indicated a decision about the appropriate action had been made, usually from a set of alternatives. Two examples illustrate the types of comments we identified as decisions: A molecular biologist explicitly stated the decisions required to decompose a problem into subproblems (decision 11), “Which cell do we use? The gene. Which gene do we edit? Which part of that gene do we edit? How do we build the enzyme that is going to do the cutting? … And how do we read out that it worked?” An ecologist made a statement that was also coded as a decomposition decision, because it described the action taken: “So I analyze the bird data first on its own, rather than trying to smash all the taxonomic groups together because they seem really apples and oranges. And just did two kinds of analysis, one was just sort of across all of these cases, around the world.” A single statement could be coded as multiple decisions if they were occurring simultaneously in the story being recalled or were intimately interconnected in the context of that interview, as with the ecology quote, in which the last sentence leads into deciding what data analysis is needed. Inherent in nearly every one of these decisions was that there was insufficient information to know the answer with certainty, so judgment was required.

Our primary goal for the first iterative round of coding was to check whether our list was complete by checking for any decisions that were missing, as indicated by either an action taken or a stated decision that was not clearly connected to a decision on our initial list. In this round, we also clarified wording and combined decisions that we were consistently unable to differentiate during the coding. A sample of three interviews (from biology, medicine, and electrical engineering) were first coded independently by four coders (AP, EB, CK, and AF), then discussed. The decision list was modified to add decisions and update wording based on that discussion. Then the interviews were recoded with the new list and rediscussed, leading to more refinements to the list. Two additional interviews (from physics and chemical engineering) were then coded by three coders (AP, EB, and CK) and further similar refinements were made. Throughout the subsequent rounds of coding, we continued to check for missing decisions, but after the additions and adjustments made based on these five interviews, we did not identify any more missing decisions.

In our next round of coding, we focused on condensing overlapping decisions and refining wording to improve the clarity of descriptions as they applied across different disciplinary contexts and to ensure consistent interpretation by different coders. Two or three coders independently coded an additional 11 interviews, iteratively meeting to discuss codes identified in the interviews, refining wording and condensing the list to improve agreement and combine overlapping codes, and then using the updated list to code subsequent interviews. We condensed the list by combining decisions that represented the same cognitive process taking place at different times, that were discipline-specific variations on the same decision, or that were substeps involved in making a larger decision. We noticed that some decisions were frequently co-coded with others, particularly in some disciplines. But if they were identified as distinct a reasonable fraction of the time in any discipline, we listed them as separate. This provided us with a list, condensed from 42 to 29 discrete decisions (plus five additional non-decision themes that were so prevalent that they are important to describe), that gave good consistency between coders.

Finally, we used the resulting codes to tabulate which decisions occurred in each interview, simplifying our coding process to focus on deciding whether or not each decision had occurred, with an example if it did occur to back up the “yes” code, but no longer attempting to capture every time each decision was mentioned. Individual coders identified decisions mentioned in the remaining 15 interviews. Interviews that had been coded with the early versions of the list were also recoded to ensure consistency. Coders flagged any decisions they were unsure about occurring in a particular interview, and two to four coders (AP, EB, CK, and CW) met to discuss those debated codes, with most uncertainties being resolved by explanations from a team member who had more technical expertise in the field of the interview. Minor wording changes were made during this process to ensure that each description of a decision captured all instantiations of the decision across disciplines, but no significant changes to the list were needed or made.

Coding an interview in terms of decisions made and actions taken in the research often required a high level of expertise in the discipline in question. The coder had to be familiar with the conduct of research in the field in order to recognize which actions corresponded to a decision between alternatives, but our team was assembled with this requirement in mind. It included high-level expertise across five different fields of science, engineering, and medicine and substantial familiarity with several other fields.

Supplemental Table S1 shows the final tabulation of decisions identified in each interview. In the tabulation, most decisions were marked as either “yes” or “no” for each interview, though 65 out of 1054 total were marked as “implied,” for one of the following reasons: 1) for 40/65, based on the coder's knowledge of the field, it was clear that a step must have been taken to achieve an outcome or action, even though that decision was not explicitly mentioned (e.g., interviewees describe collecting certain raw data and then coming to a specific conclusion, so they must have decided how to analyze the data, even if they did not mention the analysis explicitly); 2) for 15/65, the interview context was important, in that multiple statements from different parts of the interview taken together were sufficient to conclude that the decision must have happened, though no single statement described that decision explicitly; 3) 10/65 involved a decision that was explicitly discussed as an important step in problem solving, but they did not directly state how it was related to the problem at hand, or it was stated only in response to a direct prompt from the interviewer. The proportion of decisions identified in each interview, broken down by either explicit or explicit + implied, is presented in Supplemental Tables S1 and S2. Table 2 and Figure 2 of the main text show explicit + implied decision numbers.

a See supplementary text and Table S2 for full description and examples of each decision. A set of other non-decision knowledge and skill development themes were also frequently mentioned as important to professional success: Staying up to date in the field (84%), intuition and experience (77%), interpersonal and teamwork (100%), efficiency (32%), and attitude (68%).

b Percentage of interviews in which category or decision was mentioned.

c Numbering is for reference. In practice ordering is fluid – involves extensive iteration with other possible starting points.

d Chosen predictive framework(s) will inform all other decisions.

e Reflection occurs throughout process, and often leads to iteration. Reflection on solution occurs at the end as well.

FIGURE 2. Proportion of decisions coded in interviews by field. This tabulation includes decisions 1–29, not the additional themes. Error bars represent standard deviations. Number of interviews: total = 31; physical science = 9; biological science = 8; engineering = 8; medicine = 6. Compared with the sciences, slightly fewer decisions overall were identified in the coding of engineering and medicine interviews, largely for discipline-specific reasons. See Supplemental Table S2 and associated discussion.

Two of the interviews that had not been discussed during earlier rounds of coding (one physics [AP and EB], one medicine [AP and CK]) were independently coded by two coders to check interrater reliability using the final list of decisions. The goal of our final coding was to tabulate whether or not each expert described making each decision at any point in the problem-solving process, so the level of detail we chose for coding and interrater reliability was whether or not a decision was present in the entire interview. The decisions identified in each interview were compared for the two coders. For both interviews, the raters disagreed on whether or not only one of the 29 decisions occurred. Codes of “implied” were counted as agreement if the other coder selected either “yes” or “implied.” This equates to a percent agreement of 97% for each interview (28 agree/29 total decisions per interview = 97%). As a side note, there was also one disagreement per interview on the coding of the five other themes, but those themes were not a focus of this work nor the interviews.

We identified a total set of 29 decisions to be made (plus five other themes), all of which were identified in a large fraction of the interviews across all disciplines ( Table 2 and Figure 2 ). There was a surprising degree of overlap across the different fields with all the experts mentioning similar decisions to be made. All 29 were evident by the fifth semistructured interview, and on average, each interview revealed 85% of the 29 decisions. Many decisions occurred multiple times in an interview, with the number of times varying widely, depending on the length and complexity of the problem-solving process discussed.

We focused our analysis on what decisions needed to be made, not on the experts’ processes for making those decisions: noting that a choice happened, not how they selected and chose among different alternatives. This is because, while the decisions to be made were the same across disciplines, how the experts made those decisions varied greatly by discipline and individual. The process of making the decisions relied on specialized disciplinary knowledge and experience and may vary depending on demographics or other factors that our study design (both our sample and nature of retrospective interviews) did not allow us to investigate. However, while that knowledge was distinct and specialized, we could tell that it was consistently organized according to a common structure we call a “predictive framework,” as discussed in the “ Predictive Framework ” section below. Also, while every “decision” reflected a step in the problem solving involved in the work, and the expert being interviewed was involved in making or approving the decision, that does not mean the decision process was carried out only by that individual. In many cases, the experts described the decisions made in terms of ideas and results of their teams, and the importance of interpersonal skills and teamwork was an important non-decision theme raised in all interviews.

We were particularly concerned with the correctness and completeness of the set of decisions. Although the correctness was largely established by the statements in the interviews, we also showed the list of decisions to these experts at the end of the interviews as well as to about a dozen other experts. In all cases, they all agreed that these decisions were ones they and others in their field made when solving problems. The completeness of the list of decisions was confirmed by: 1) looking carefully at all specific actions taken in the described problem-solving process and checking that each action matched a corresponding decision from the list; and 2) the high degree of consistency in the set of decisions across all the interviews and disciplines. This implies that it is unlikely that there are important decisions that we are missing, because that would require any such missing decisions to be consistently unspoken by all 31 interviewees as well as consistently unrecognized by us from the actions that were taken in the problem-solving process.

In focusing on experts’ recollections of their successful solving of problems, our study design may have missed decisions that experts only made during failed problem-solving attempts. However, almost all interviews described solution paths that were not smooth and continuous, but rather involved going down numerous dead ends. There were approaches that were tried and failed, data that turned out to be ambiguous and worthless, and so on. Identifying the failed path involved reflection decisions (23–26). Often decision 9 (is problem solvable?) would be mentioned, because it described a path that was determined to be not solvable. For example, a biologist explained, “And then I ended up just switching to a different strain that did it [crawling off the plate] less. Because it was just … hard to really get them to behave themselves. I suppose if I really needed to rely on that very particular one, I probably would have exhausted the possibilities a bit more.” Thus, we expect unsuccessful problem solving would entail a smaller subset of decisions being made, particularly lack of reflection decisions, or poor choices on the decisions, rather than making a different set of decisions.

The set of decisions represent a remarkably consistent structure underlying S&E problem solving. For the purposes of presentation, we have categorized the decisions as shown in Figure 3 , roughly based on the purposes they achieve. However, the process is far less orderly and sequential than implied by this diagram, or in fact any characterization of an orderly “scientific method.” We were struck by how variable the sequence of decisions was in the descriptions provided. For example, experts who described how they began work on a problem sometimes discussed importance and goals (1–3, what is important in field?; opportunity fits solver’s expertise?; and goals, criteria, constraints?), but others mentioned a curious observation (20, any significant anomalies?), important features of their system that led them to questions (4, important features and info?, 6, how to narrow down problem?), or other starting points. We also saw that there were flexible connections between decisions and repeated iterations—jumping back to the same type of decision multiple times in the solution process, often prompted by reflection as new information and insights were developed. The sequence and number of iterations described varied dramatically by interview, and we cannot determine to what extent this was due to legitimate differences in the problem-solving process or to how the expert recalled and chose to describe the process. This lack of a consistent starting point, with jumping and iterating between decisions, has also been identified in the naturalistic decision-making literature ( Mosier et al. , 2018 ). Finally, the experts also often described considering multiple decisions simultaneously. In some interviews, a few decisions were always described together, while in others, they were clearly separate decisions. In summary, while the specific decisions themselves are fully grounded in expert practice, the categories and order shown here are artificial simplifications for presentation purposes.

FIGURE 3. Representation of problem-solving decisions by categories. The black arrows represent a hypothetical but unrealistic order of operations, the blue arrows represent more realistic iteration paths. The decisions are grouped into categories for presentation purposes; numbers indicate the number of decisions in each category. Knowledge and skill development were commonly mentioned themes but are not decisions.

The decisions contained in the seven categories are summarized here. See Supplemental Table S2 for specific examples of each decision across multiple disciplines.

Category A. Selection and Goals of the Problem

This category involves deciding on the importance of the problem, what criteria a solution must meet, and how well it matches the capabilities, resources, and priorities of the expert. As an example, an earth scientist described the goal of her project (decision 3, goals, criteria, constraints?) to map and date the earliest volcanic rocks associated with what is now Yellowstone and explained why the project was a good fit for her group (2, opportunity fits solver’s expertise?) and her decision to pursue the project in light of the significance of this type of eruption in major extinction events (1, what is important in field?). In many cases, decisions related to framing (see category B) were mentioned before decisions in this category or were an integral part of the process for developing goals.

1. What is important in the field?

What are important questions or problems? Where is the field heading? Are there advances in the field that open new possibilities?

2. Opportunity fits solver's expertise?

If and where are there gaps/opportunities to solve in field? Given experts’ unique perspectives and capabilities, are there opportunities particularly accessible to them? (This could involve challenging the status quo, questioning assumptions in the field.)

3. Goals, criteria, constraints?

a. What are the goals, design criteria, or requirements of the problem or its solution?

b. What is the scope of the problem?

c. What constraints are there on the solution?

d. What will be the criteria on which the solution is evaluated?

Category B. Frame Problem

These decisions lead to a more concrete formulation of the solution process and potential solutions. This involves identifying the key features of the problem and deciding on predictive frameworks to use (see “ Predictive Framework ” section below), as well as narrowing down the problem, often forming specific questions or hypotheses. Many of these decisions are guided by past problem solutions with which the expert is familiar and sees as relevant. The framing decisions of a physician can be seen in his discussion of a patient with liver failure who had previously been diagnosed with HIV but had features (4, important features and info?; 5, what predictive framework?) that made the physician question the HIV diagnosis (5, what predictive framework?; 26, how good is solution?). His team then searched for possible diagnoses that could explain liver failure and lead to a false-positive HIV test (7, related problems?; 8, potential solutions?), which led to their hypothesis the patient might have Q fever (6, how to narrow down problem?; 13, what info needed?; 15, specific plan for getting info?). While each individual decision is strongly supported by the data, the categories are groupings for presentation purposes. In particular, framing (category B) and planning (see category C) decisions often blended together in interviews.

a. Which available information is relevant to problem solving and why?

b. (When appropriate) Create/find a suitable abstract representation of core ideas and information Examples: physics, equation representing process involved; chemistry, bond diagrams/potential energy surfaces; biology, diagram of pathway steps.

5. What predictive framework?

Which potential predictive frameworks to use? (Decide among possible predictive frameworks or create framework.) This includes deciding on the appropriate level of mechanism and structure that the framework needs to embody to be most useful for the problem at hand.

6. How to narrow down the problem?

How to narrow down the problem? Often involves formulating specific questions and hypotheses.

7. Related problems?

What are related problems or work seen before, and what aspects of their problem-solving process and solutions might be useful in the present context? (This may involve reviewing literature and/or reflecting on experience.)

8. Potential solutions?

What are potential solutions? (This is based on experience and fitting some criteria for solution they have for a problem having general key features identified.)

9. Is problem solvable?

Is the problem plausibly solvable and is the solution worth pursuing given the difficulties, constraints, risks, and uncertainties?

Category C. Plan the Process for Solving

These decisions establish the specifics needed to solve the problem and include: how to simplify the problem and decompose it into pieces, what specific information is needed, how to obtain that information, and what are the resources needed and priorities? Planning by an ecologist can be seen in her extensive discussion of her process of simplifying (10, approximations/simplifications to make?) a meta-analysis project about changes in migration behavior, which included deciding what types of data she needed (13, what info needed?), planning how to conduct her literature search (15, specific plan for getting info?), difficulties in analyzing the data (12, most difficult/uncertain areas?; 16, which calculations and data analysis?), and deciding to analyze different taxonomic groups separately (11, how to decompose into subproblems?). In general, decomposition often resulted in multiple iterations through the problem-solving decisions, as subsets of decisions need to be made about each decomposed aspect of a problem. Framing (category B) and planning (category C) decisions occupied much of the interviews, indicating their importance.

10. Approximations and simplifications to make?

What approximations or simplifications are appropriate? How to simplify the problem to make it easier to solve? Test possible simplifications/approximations against established criteria.

11. How to decompose into subproblems?

How to decompose the problem into more tractable subproblems? (Subproblems are independently solvable pieces with their own subgoals.)

12. Most difficult or uncertain areas?

a. What are acceptable levels of uncertainty with which to proceed at various stages?

13. What info needed?

a. What will be sufficient to test and distinguish between potential solutions?

14. Priorities?

What to prioritize among many competing considerations? What to do first and how to obtain necessary resources?

Considerations could include: What's most important? Most difficult? Addressing uncertainties? Easiest? Constraints (time, materials, etc.)? Cost? Optimization and trade-offs? Availability of resources? (facilities/materials, funding sources, personnel)

15. Specific plan for getting information?

a. What are the general requirements of a problem-solving approach, and what general approach will they pursue? (These decisions are often made early in the problem-solving process as part of framing.)

b. How to obtain needed information? Then carry out those plans. (This could involve many discipline- and problem-specific investigation possibilities such as: designing and conducting experiments, making observations, talking to experts, consulting the literature, doing calculations, building models, or using simulations.)

c. What are achievable milestones, and what are metrics for evaluating progress?

d. What are possible alternative outcomes and paths that may arise during the problem-solving process, both consistent with predictive framework and not, and what would be paths to follow for the different outcomes?

Category D. Interpret Information and Choose Solution(s)

This category includes deciding how to analyze, organize, and draw conclusions from available information, reacting to unexpected information, and deciding upon a solution. A biologist studying aging in worms described how she analyzed results from her experiments, which included representing her results in survival curves and conducting statistical analyses (16, which calculations and data analysis?; 17, how to represent and organize info?), as well as setting up blind experiments (15, specific plan for getting info?) so that she could make unbiased interpretations (18, how believable is info?) of whether a worm was alive or dead. She also described comparing results with predictions to justify the conclusion that worm aging was related to fertility (19, how does info compare to predictions?; 21, appropriate conclusions?; 22, what is best solution?). Deciding how results compared with expectations based on a predictive framework was a key decision that often preceded several other decisions.

16. Which calculations and data analysis?

What calculations and data analysis are needed? Once determined, these must then be carried out.

17. How to represent and organize information?

What is the best way to represent and organize available information to provide clarity and insights? (Usually this will involve specialized and technical representations related to key features of predictive framework.)

18. How believable is the information?

Is information valid, reliable, and believable (includes recognizing potential biases)?

19. How does information compare to predictions?

As new information comes in, particularly from experiments or calculations, how does it compare with expected results (based on the predictive framework)?

20. Any significant anomalies?

a. Does potential anomaly fit within acceptable range of predictive framework(s) (given limitations of predictive framework and underlying assumptions and approximations)?

b. Is potential anomaly an unusual statistical variation or relevant data? Is it within acceptable levels of uncertainty?

21. Appropriate conclusions?

What are appropriate conclusions based on the data? (This involves making conclusions and deciding if they are justified.)

22. What is the best solution?

a. Which of multiple candidate solutions are consistent with all available information and which can be rejected? (This could be based on comparing data with predicted results.)

b. What refinements need to be made to candidate solutions?

Category E. Reflect

Reflection decisions occur throughout the process and include deciding whether assumptions are justified, whether additional knowledge or information is needed, how well the solution approach is working, and whether potential and then final solutions are adequate. These decisions match the categories of reflection identified by Salehi (2018) . A mechanical engineer described developing a model (to inform surgical decisions) of which muscles allow the thumb to function in the most useful manner (22, what is best solution?), including reflecting on how well engineering approximations applied in the biological context (23, assumptions and simplifications appropriate?). He also described reflecting on his approach, that is, why he chose to use cadaveric models instead of mathematical models (25, how well is solving approach working?), and the limitations of his findings in that the “best” muscle identified was difficult to access surgically (26, how good is solution?; 27, broader implications?). Reflection decisions are made throughout the problem-solving process, often lead to reconsidering other decisions, and are critical for success.

23. Assumptions and simplifications appropriate?

a. Do the assumptions and simplifications made previously still look appropriate considering new information?

b Does predictive framework need to be modified?

24. Additional knowledge needed?

a. Is solver's relevant knowledge sufficient?

b. Is more information needed and, if so, what?

c. Does some information need to be checked? (Is there a need to repeat experiment or check a different source?)

25. How well is the problem-solving approach working?

How well is the problem-solving approach working, and does it need to be modified? This includes possibly modifying the goals. (One needs to reflect on one's strategy by evaluating progress toward the solution.) and reflecting on one’s strategy by evaluating progress toward the solution.

26. How good is the solution?

a. Decide by exploring possible failure modes and limitations—“try to break” solution.

b. Does it “make sense” and pass discipline-specific tests for solutions of this type of problem?

c. Does it completely meet the goals/criteria?

Category F. Implications and Communication of Results

These are decisions about the broader implications of the work, and how to communicate results most effectively. For example, a theoretical physicist developing a method to calculate the magnetic moment of the muon decided on who would be interested in his work (28, audience for communication?) and what would be the best way to present it (29, best way to present work?). He also discussed the implications of preliminary work on a simplified aspect of the problem (10, approximations and simplifications to make?) in terms of evaluating its impact on the scientific community and deciding on next steps (27, broader implications?; 29, best way to present work?). Many interviewees described that making decisions in this category affected their decisions in other categories.

27. Broader implications?

What are the broader implications of the results, including over what range of contexts does the solution apply? What outstanding problems in the field might it solve? What novel predictions can it enable? How and why might this be seen as interesting to a broader community?

28. Audience for communication?

What is the audience for communication of work, and what are their important characteristics?

29. Best way to present work?

What is the best way to present the work to have it understood, and its correctness and importance appreciated? How to make a compelling story of the work?

Category G. Ongoing Skill and Knowledge Development

Although we focused on decisions in the problem-solving process, the experts volunteered general skills and knowledge they saw as important elements of problem-solving expertise in their fields. These included teamwork and interpersonal skills (strongly emphasized), acquiring experience and intuition, and keeping abreast of new developments in their fields.

30. Stay up to date in field

a. Reviewing literature, which does involve making decisions as to which is important.

b. Learning relevant new knowledge (ideas and technology from literature, conferences, colleagues, etc.)

31. Intuition and experience

Acquiring experience and associated intuition to improve problem solving.

32. Interpersonal, teamwork

Includes navigating collaborations, team management, patient interactions, communication skills, etc., particularly as how these apply in the context of the various types of problem-solving processes.

33. Efficiency

Time management including learning to complete certain common tasks efficiently and accurately.

34. Attitude

Motivation and attitude toward the task. Factors such as interest, perseverance, dealing with stress, and confidence in decisions.

Predictive Framework

How the decisions were made was highly dependent on the discipline and problem. However, there was one element that was fundamental and common across all interviews: the early adoption of a “predictive framework” that the experts used throughout the problem-solving process. We define this framework as “a mental model of key features of the problem and the relationships between the features.” All the predictive frameworks involved some degree of simplification and approximation and an underlying level of mechanism that established the relationships between key features. The frameworks provided a structure of knowledge and facilitated the application of that knowledge to the problem at hand, allowing experts to repeatedly run “mental simulations” to make predictions for dependencies and observables and to interpret new information.

As an example, an ecologist described her predictive framework for migration, which incorporated important features such as environmental conditions and genetic differences between species and the mechanisms by which these interacted to impact the migration patterns for a species. She used this framework to guide her meta-analysis of changes in migration patterns, affecting everything from her choice of data sets to include to her interpretation of why migration patterns changed for different species. In many interviews, the frameworks used evolved as additional information was obtained, with additional features being added or underlying assumptions modified. For some problems, the relevant framework was well established and used with confidence, while for other problems, there was considerable uncertainty as to a suitable framework, so developing and testing the framework was a substantial part of the solution process.

A predictive framework contains the expert knowledge organization that has been observed in previous studies of expertise ( Egan and Greeno, 1974 ) but goes further, as here it serves as an explicit tool that guides most decisions and actions during the solving of complex problems. Mental models and mental simulations that are described in the naturalistic decision-making literature are similar, in that they are used to understand the problem and guide decisions ( Klein, 2008 ; Mosier et al. , 2018 ), but they do not necessarily contain the same level of mechanistic understanding of relationships that underlies the predictive frameworks used in science and engineering problem solving. While the use of predictive frameworks was universal, the individual frameworks themselves explicitly reflected the relevant specialized knowledge, structure, and standards of the discipline, and arguably largely define a discipline ( Wieman, 2019 ).

Discipline-Specific Variation

While the set of decisions to be made was highly consistent across disciplines, there were extensive differences within and across disciplines and work contexts, which reflected the differences in perspectives and experiences. These differences were usually evident in how experts made each of the specific decisions, but not in the choice of which decisions needed to be made. In other words, the solution methods, which included following standard accepted procedures in each field, were very different. For example, planning in some experimental sciences may involve formulating a multiyear construction and data-collection effort, while in medicine it may be deciding on a simple blood test. Some decisions, notably in categories A, D, and F, were less likely to be mentioned in particular disciplines, because of the nature of the problems. Specifically, decisions 1 (what is important in field?), 2 (opportunity fits solver’s expertise?), 27 (broader implications?), 28 (audience for communication?), and 29 (best way to present work?) were dependent on the scope of the problem being described and the expert's specific role in it. These were mentioned less frequently in interviews where the problem was assigned to the expert (most often engineering or industry) or where the importance or audience was implicit (most often in medicine). Decisions 16 (which calculations and data analysis?) and 17 (how to represent and organize info?) were particularly unlikely to be mentioned in medicine, because test results are typically provided to doctors not in the form or raw data, but rather already analyzed by a lab or other medical technology professional, so the doctors we interviewed did not need to make decisions themselves about how to analyze or represent the data. Qualitatively, we also noticed some differences between disciplines in the patterns of connections between decisions. When the problem involved development of a tool or product, most commonly the case in engineering, the interview indicated relatively rapid cycles between goals (3), framing problem/potential solutions (8), and reflection on the potential solution (26), before going through the other decisions. Biology, the experimental science most represented in our interviews, had strong links between planning (15), deciding on appropriate conclusions (21), and reflection on the solution (26). This is likely because the respective problems involved complex systems with many unknowns, so careful planning was unusually important for achieving definitive conclusions. See Supplemental Text and Supplemental Table S2 for additional notes on decisions that were mentioned at lower frequency and decisions that were likely to be interconnected, regardless of field.

This work has created a framework of decisions to characterize problem solving in science and engineering. This framework is empirically based and captures the successful problem-solving process of all experts interviewed. We see that several dozen experts across many different fields all make a common set of decisions when solving authentic problems. There are flexible linkages between decisions that are guided by reflection in a continually evolving process. We have also identified the nature of the “predictive frameworks” that S&E experts consistently use in problem solving. These predictive frameworks reveal how these experts organize their disciplinary knowledge to facilitate making decisions. Many of the decisions we identified are reflected in previous work on expertise and scientific problem solving. This is particularly true for those listed in the planning and interpreting information categories ( Egan and Greeno, 1974 ). The priority experts give to framing and planning decisions over execution compared with novices has been noted repeatedly (e.g., Chi et al. , 1988 ). Expert reflection has been discussed, but less extensively ( Chase and Simon, 1973 ), and elements of the selection and implications and communication categories have been included in policy and standards reports (e.g., AAAS, 2011 ). Thus, our framework of decisions is consistent with previous work on scientific practices and expertise, but it is more complete, specific, empirically based, and generalizable across S&E disciplines.

A limitation of this study is the small number of experts we have in total, from each discipline, and from underrepresented groups (especially lack of female representation in engineering). The lack of randomized selection of participants may also bias the sample toward experts who experienced similar academic training (STEM disciplines at U.S. universities). This means we cannot prove that there are not some experts who follow other paths in problem solving. As with any scientific model, the framework described here should be subjected to further tests and modifications as necessary. However, to our knowledge, this is a far larger sample than used in any previous study of expert problem solving. Although we see a large amount of variation both within and across disciplines in the problem-solving process, this is reflected in how experts make decisions, not in what decisions they make. The very high degree of consistency in the decisions made across the entire sample strongly suggests that we are capturing elements that are common to all experts across science and engineering. A second limitation is that decisions often overlap and co-occur in an interview, so the division between decision items is often somewhat ambiguous and could be defined somewhat differently. As noted, a number of these decisions can be interconnected, and in some fields are nearly always interconnected.

The set of decisions we have observed provides a general framework for characterizing, analyzing, and teaching S&E problem solving. These decisions likely define much of the set of cognitive skills a student needs to practice and master to perform as a skilled practitioner in S&E. This framework of decisions provides a detailed and structured way to approach the teaching and measurement of problem solving at the undergraduate, graduate, and professional training levels. For teaching, we propose using the process of “deliberate practice” ( Ericsson, 2018 ) to help students learn problem solving. Deliberate practice of problem solving would involve effective scaffolding and concentrated practice, with feedback, at making the specific decisions identified here in relevant contexts. In a course, this would likely involve only an appropriately selected set of the decisions, but a good research mentor would ensure that trainees have opportunities to practice and receive feedback on their performance on each of these 29 decisions. Future work is needed to determine whether there are additional decisions that were not identified in experts but are productive components of student problem solving and should also be practiced. Measurements of individual problem-solving expertise based on our decision list and the associated discipline-specific predictive frameworks will allow a detailed measure of an individual's discipline-specific problem-solving strengths and weaknesses relative to an established expert. This can be used to provide targeted feedback to the learner, and when aggregated across students in a program, feedback on the educational quality of the program. We are currently working on the implementation of these ideas in a variety of instructional settings and will report on that work in future publications.

As discussed in the Introduction , typical science and engineering problems fail to engage students in the complete problem-solving process. By considering which of the 29 decisions are required to answer the problem, we can more clearly articulate why. The biology problem, for example, requires students to decide on a predictive framework and access the necessary content knowledge, and they need to decide which information they need to answer the problem. However, other decisions are not required or are already made for them, such as deciding on important features and identifying anomalies. We propose that different problems, designed specifically to require students to make sets of the problem-solving decisions from our framework, will provide more effective tools for measuring, practicing, and ultimately mastering the full S&E problem-solving process.

Our preliminary work with the use of such decision-based problems for assessing problem-solving expertise is showing great promise. For several different disciplines, we have given test subjects a relevant context, requiring content knowledge covered in courses they have taken, and asked them to make decisions from the list presented here. Skilled practitioners in the relevant discipline respond in very consistent ways, while students respond very differently and show large differences that typically correlate with their different educational experiences. What apparently matters is not what content they have seen, but rather what decisions they have had practice making. Our approach was to identify the decisions made by experts, this being the task that educators want students to master. Our data do not exclude the possibility that students engage in and/or should learn other decisions as a productive part of the problem-solving process while they are learning. Future work would seek to identify decisions made at intermediate levels during the development of expertise, to identify potential learning progressions that could be used to teach problem solving more efficiently. What we have seen is consistent with previous work identifying expert–novice differences but provides a much more extensive and detailed picture of a student's strengths and weaknesses and the impacts of particular educational experiences. We have also carried out preliminary development of courses that explicitly involve students making and justifying many of these decisions in relevant contexts, followed by feedback on their decisions. Preliminary results from these courses are also encouraging. Future work will involve the more extensive development and application of decision-based measurement and teaching of problem solving.

ACKNOWLEDGMENTS

We acknowledge the many experts who agreed to be interviewed for this work, M. Flynn for contributions on expertise in mechanical engineering, and Shima Salehi for useful discussions. This work was funded by the Howard Hughes Medical Institute through an HHMI Professor grant to C.E.W.

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Submitted: 2 December 2020 Revised: 11 June 2021 Accepted: 23 June 2021

© 2021 A. M. Price et al. CBE—Life Sciences Education © 2021 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Eight Disciplines of Problem Solving (8D)

– Eight Disciplines of Problem Solving –

⇓   Introduction to 8D

⇓   What is 8D

⇓   Why Apply 8D

⇓   When to Apply 8D

⇓   How to Apply 8D

Introduction to Eight Disciplines of Problem Solving (8D)

The Eight Disciplines of Problem Solving (8D) is a problem solving methodology designed to find the root cause of a problem, devise a short-term fix and implement a long-term solution to prevent recurring problems. When it’s clear that your product is defective or isn’t satisfying your customers, an 8D is an excellent first step to improving Quality and Reliability.

Ford Motor Company developed this problem solving methodology, then known as Team Oriented Problem Solving (TOPS), in the 1980s. The early usage of 8D proved so effective that it was adopted by Ford as the primary method of documenting problem solving efforts, and the company continues to use 8D today.

8D has become very popular among manufacturers because it is effective and reasonably easy to teach. Below you’ll find the benefits of an 8D, when it is appropriate to perform and how it is performed.

What is Eight Disciplines of Problem Solving (8D)

The 8D problem solving process is a detailed, team oriented approach to solving critical problems in the production process. The goals of this method are to find the root cause of a problem, develop containment actions to protect customers and take corrective action to prevent similar problems in the future.

The strength of the 8D process lies in its structure, discipline and methodology. 8D uses a composite methodology, utilizing best practices from various existing approaches. It is a problem solving method that drives systemic change, improving an entire process in order to avoid not only the problem at hand but also other issues that may stem from a systemic failure.

8D has grown to be one of the most popular problem solving methodologies used for Manufacturing, Assembly and Services around the globe. Read on to learn about the reasons why the Eight Disciplines of Problem Solving may be a good fit for your company.

Why Apply Eight Disciplines of Problem Solving (8D)

The 8D methodology is so popular in part because it offers your engineering team a consistent, easy-to-learn and thorough approach to solving whatever problems might arise at various stages in your production process. When properly applied, you can expect the following benefits:

• Improved team oriented problem solving skills rather than reliance on the individual
• Increased familiarity with a structure for problem solving
• Creation and expansion of a database of past failures and lessons learned to prevent problems in the future
• Better understanding of how to use basic statistical tools required for problem solving
• Improved effectiveness and efficiency at problem solving
• A practical understanding of Root Cause Analysis (RCA)
• Problem solving effort may be adopted into the processes and methods of the organization
• Improved skills for implementing corrective action
• Better ability to identify necessary systemic changes and subsequent inputs for change
• More candid and open communication in problem solving discussion, increasing effectiveness
• An improvement in management’s understanding of problems and problem resolution

8D was created to represent the best practices in problem solving. When performed correctly, this methodology not only improves the Quality and Reliability of your products but also prepares your engineering team for future problems.

When to Apply Eight Disciplines of Problem Solving (8D)

The 8D problem solving process is typically required when:

• Safety or Regulatory issues has been discovered
• Customer complaints are received
• Warranty Concerns have indicated greater-than-expected failure rates
• Internal rejects, waste, scrap, poor performance or test failures are present at unacceptable levels

How to Apply Eight Disciplines of Problem Solving (8D)

The 8D process alternates inductive and deductive problem solving tools to relentlessly move forward toward a solution. The Quality-One approach uses a core team of three individuals for inductive activities with data driven tools and then a larger Subject Matter Expert (SME) group for the deductive activities through brainstorming, data-gathering and experimentation.

D0: Prepare and Plan for the 8D

Proper planning will always translate to a better start. Thus, before 8D analysis begins, it is always a good idea to ask an expert first for their impressions. After receiving feedback, the following criterion should be applied prior to forming a team:

Collect information on the symptoms

Use a Symptoms Checklist to ask the correct questions

Identify the need for an Emergency Response Action (ERA), which protects the customer from further exposure to the undesired symptoms

D1: Form a Team

A Cross Functional Team (CFT) is made up of members from many disciplines. Quality-One takes this principle one step further by having two levels of CFT:

• The Core Team Structure should involve three people on the respective subjects: product, process and data
• Additional Subject Matter Experts are brought in at various times to assist with brainstorming, data collection and analysis

Teams require proper preparation. Setting the ground rules is paramount. Implementation of disciplines like checklists, forms and techniques will ensure steady progress.  8D must always have two key members: a Leader and a Champion / Sponsor:

• The Leader is the person who knows the 8D process and can lead the team through it (although not always the most knowledgeable about the problem being studied)
• The Champion or Sponsor is the one person who can affect change by agreeing with the findings and can provide final approval on such changes

D2: Describe the Problem

The 8D method’s initial focus is to properly describe the problem utilizing the known data and placing it into specific categories for future comparisons. The “Is” data supports the facts whereas the “Is Not” data does not. As the “Is Not” data is collected, many possible reasons for failure are able to be eliminated. This approach utilizes the following tools:

• Problem Statement
• Affinity Diagram (Deductive tool)
• Fishbone/Ishikawa Diagram (Deductive tool)
• Problem Description

D3: Interim Containment Action

In the interim, before the permanent corrective action has been determined, an action to protect the customer can be taken. The Interim Containment Action (ICA) is temporary and is typically removed after the Permanent Correct Action (PCA) is taken.

• Verification of effectiveness of the ICA is always recommended to prevent any additional customer dissatisfaction calls

D4: Root Cause Analysis (RCA) and Escape Point

The root cause must be identified to take permanent action to eliminate it. The root cause definition requires that it can be turned on or off, at will. Activities in D4 include:

• Comparative Analysis listing differences and changes between “Is” and “Is Not”
• Development of Root Cause Theories based on remaining items
• Verification of the Root Cause through data collection
• Review Process Flow Diagram for location of the root cause
• Determine Escape Point, which is the closest point in the process where the root cause could have been found but was not

D5: Permanent Corrective Action (PCA)

The PCA is directed toward the root cause and removes / changes the conditions of the product or process that was responsible for the problem. Activities in D5 include:

• Establish the Acceptance Criteria which include Mandatory Requirements and Wants
• Perform a Risk Assessment /  Failure Mode and Effects Analysis (FMEA) on the PCA choices
• Based on risk assessment, make a balanced choice for PCA
• Select control-point improvement for the Escape Point
• Verification of Effectiveness for both the PCA and the Escape Point are required

D6: Implement and Validate the Permanent Corrective Action

To successfully implement a permanent change, proper planning is essential. A project plan should encompass: communication, steps to complete, measurement of success and lessons learned. Activities in D6 include:

• Develop Project Plan for Implementation
• Communicate the plan to all stakeholders
• Validation of improvements using measurement

D7: Prevent Recurrence

D7 affords the opportunity to preserve and share the knowledge, preventing problems on similar products, processes, locations or families. Updating documents and procedures / work instructions are expected at this step to improve future use. Activities in D7 include:

• Review Similar Products and Processes for problem prevention
• Develop / Update Procedures and Work Instructions for Systems Prevention
• Capture Standard Work / Practice and reuse
• Assure FMEA updates have been completed
• Assure Control Plans have been updated

D8: Closure and Team Celebration

Teams require feedback to allow for satisfactory closure. Recognizing both team and individual efforts and allowing the team to see the previous and new state solidifies the value of the 8D process. Activities in D8 include:

• Archive the 8D Documents for future reference
• Document Lessons Learned on how to make problem solving better
• Before and After Comparison of issue
• Celebrate Successful Completion

8D and Root Cause Analysis (RCA)

The 8D process has Root Cause Analysis (RCA) imbedded within it. All problem solving techniques include RCA within their structure. The steps and techniques within 8D which correspond to Root Cause Analysis are as follows:

• Problem Symptom is quantified and converted to “Object and Defect”
• Problem Symptom is converted to Problem Statement using Repeated Whys
• Possible and Potential Causes are collected using deductive tools (i.e. Fishbone or Affinity Diagram)
• Problem Statement is converted into Problem Description using Is / Is Not
• Problem Description reduces the number of items on the deductive tool (from step 3)
• Comparative Analysis between the Is and Is Not items (note changes and time)
• Root Cause theories are developed from remaining possible causes on deductive tool and coupled with changes from Is / Is Not
• Compare theories with current data and develop experiments for Root Cause Verification
• Test and confirm the Root Causes

Example: Multiple Why Technique

The Multiple / Repeated Why (Similar to 5 Why) is an inductive tool, which means facts are required to proceed to a more detailed level. The steps required to determine problem statement are:

• Problem Symptom is defined as an Object and Defect i.e. “Passenger Injury”
• Why? In every case “SUV’s Roll Over”
• Why? In every case, it was preceded by a “Blown Tire”
• Why? Many explanations may be applied, therefore the team cannot continue with another repeated why past “Blown Tire”
• Therefore, the Problem Statement is “Blown Tire”
• Why? Low (Air) Pressure, Tire Defect (Degradation of an Interface) and High (Ambient) Temperature
• Counter measures assigned to low pressure and tire defect

This example uses only 4 of the 5 Whys to determine the root causes without going further into the systemic reasons that supported the failure. The Repeated Why is one way to depict this failure chain. Fault Tree Analysis (FTA) could also be used.

Learn More About Eight Disciplines of Problem Solving (8D)

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Article • 8 min read

A Powerful Methodology for Creative Problem Solving

By the Mind Tools Content Team

Projects don't always run smoothly. Even with all the analysis and data you need at your fingertips, sometimes you just can't see a way forward. At times like these, you need to develop creative solutions to the problems you face.

Chances are you already know about brainstorming , which can help with this sort of situation. But brainstorming depends on intuition and the existing knowledge of team members, and its results are often unpredictable and unrepeatable.

TRIZ, however, is a problem-solving philosophy based on logic, data and research, rather than on intuition.

It draws on the past knowledge and ingenuity of thousands of engineers to speed up creative problem solving for project teams. Its approach brings repeatability, predictability and reliability to the problem-solving process and delivers a set of dependable tools.

This article walks you through the essentials of TRIZ.

What is TRIZ?

TRIZ is the Russian acronym for the "Theory of Inventive Problem Solving," an international system of creativity developed in the U.S.S.R. between 1946 and 1985, by engineer and scientist Genrich S. Altshuller and his colleagues.

According to TRIZ, universal principles of creativity form the basis of innovation. TRIZ identifies and codifies these principles, and uses them to make the creative process more predictable.

In other words, whatever problem you're facing, somebody, somewhere, has already solved it (or one very like it). Creative problem solving involves finding that solution and adapting it to your problem.

TRIZ is most useful in roles such as product development, design engineering, and process management. For example, Six Sigma quality improvement processes often make use of TRIZ.

The Key TRIZ Tools

Let's look at two of the central concepts behind TRIZ: generalizing problems and solutions, and eliminating contradictions.

1. Generalizing Problems and Solutions

The primary findings of TRIZ research are as follows:

• Problems and solutions are repeated across industries and sciences. By representing a problem as a "contradiction" (we explore this later in this article), you can predict creative solutions to that problem.
• Patterns of technical evolution tend to repeat themselves across industries and sciences.
• Creative innovations often use scientific effects outside the field where they were developed.

Using TRIZ consists of learning these repeating patterns of problem and solution, understanding the contradictions present in a situation, and developing new methods of using scientific effects.

You then apply the general TRIZ patterns to the specific situation that confronts you, and discover a generalized version of the problem.

Figure 1, below, illustrates this process.

Figure 1 – The TRIZ Problem-Solving Method

Here, you take the specific problem that you face and generalize it to one of the TRIZ general problems. From the TRIZ general problems, you identify the general TRIZ solution you need, and then consider how you can apply it to your specific problem.

The TRIZ databases are actually a collection of "open source" resources compiled by users and aficionados of the system (such as the 40 Principles and 76 Standard Solutions, which we look at, below).

2. Eliminating Contradictions

Another fundamental TRIZ concept is that there are fundamental contradictions at the root of most problems. In many cases, a reliable way to solve a problem is to eliminate these contradictions.

TRIZ recognizes two categories of contradictions:

• The product gets stronger (good), but the weight increases (bad).
• Service is customized to each customer (good), but the service delivery system gets complicated (bad).
• Training is comprehensive (good), but it keeps employees away from their assignments (bad).

The key technical contradictions are summarized in the TRIZ Contradiction Matrix . As with all TRIZ resources, it takes time and study to become familiar with the Contradiction Matrix.

• Software should be complex (to have many features), but simple (to be easy to learn).
• Coffee should be hot (to be enjoyed), but cool (to avoid burning the drinker).
• An umbrella should be large (to keep the rain off), but small (to be maneuverable in a crowd).

You can solve physical contradictions with the TRIZ Separation Principles . These separate your requirements according to basic categories of Space, Time and Scale.

How to Use TRIZ Principles – an Example

Begin to explore TRIZ by applying it to a simple, practical problem.

For example, consider the specific problem of a furniture store in a small building. The store wants to attract customers, so it needs to have its goods on display. But it also needs to have enough storage space to keep a range of products ready for sale.

Using TRIZ, you can establish that the store has a physical contradiction. The furniture needs to be large (to be useful and attractive), but also small (to be stored in as little space as possible). Using TRIZ, the store owners generalize this contradiction into a general problem and apply one of the 40 Principles of Problem Solving – a key TRIZ technique – to it.

They find a viable general solution in Principle 1 – Segmentation. This advocates dividing an object or system into different parts, or making it easy to take apart. This could lead the owners to devise flat-pack versions of their furniture, so that display models can take up the room that they need while inventory occupies much less space per unit. This is the specific solution.

You, too, can use the 40 Principles of Problem Solving, or the 40 Inventive Principles, and the Contradiction Matrix to help you with your problem-solving.

Five Top TRIZ Concepts and Techniques

TRIZ comes with a range of ideas and techniques beyond the basic principles outlined above. Some are conceptual and analytical, such as:

• The Law of Ideality. This states that any system tends to become more reliable throughout its life, through regular improvement.
• Functional Modeling, Analysis and Trimming. TRIZ uses these methods to define problems.
• Locating the Zones of Conflict. (This is known to Six Sigma problem-solvers as " Root Cause Analysis .")

Some are more prescriptive. For example:

• The Laws of Technical Evolution and Technology Forecasting . These categorize technical evolution by demand, function and system.
• The 76 Standard Solutions . These are specific solutions devised to a range of common problems in design and innovation.

You can use one such tool or many to solve a problem, depending on its nature.

TRIZ is a system of creative problem solving, commonly used in engineering and process management. It follows four basic steps:

• Define your specific problem.
• Find the TRIZ generalized problem that matches it.
• Find the generalized solution that solves the generalized problem.
• Adapt the generalized solution to solve your specific problem.

Most problems stem from technical or physical contradictions. Apply one of hundreds of TRIZ principles and laws to eliminate these contradictions, and you can solve the problem.

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Tips for Solving Engineering Problems Effectively

Problem solving is the process of determining the best feasible action to take in a given situation. Problem solving is an essential skill for engineers to have. Engineers are problem solvers, as the popular quote says:

“Engineers like to solve problems. If there are no problems handily available, they will create their own problems.” – Scott Adams

Engineers are faced with a range of problems in their everyday life. The nature of problems that engineers must solve differs between and among the various disciplines of engineering. Because of the diversity of problems there is no universal list of procedures that will fit every engineering problem. Engineers use various approaches while solving problems.

Engineering problems must be approached systematically, applying an algorithm, or step-by-step practice by which one arrives at a feasible solution. In this post, we’ve prepared a list of tips for solving engineering problems effectively.

#1 Identify the Problem

Evaluating the needs or identifying the problem is a key step in finding a solution for engineering problems. Recognize and describe the problem accurately by exploring it thoroughly. Define what question is to be answered and what outputs or results are to be produced. Also determine the available data and information about the problem in hand.

An improper definition of the problem will cause the engineer to waste time, lengthen the problem solving process and finally arrive at an incorrect solution. It is essential that the stated needs be real needs.

As an engineer, you should also be careful not to make the problem pointlessly bound. Placing too many limitations on the problem may make the solution extremely complex and tough or impossible to solve. To put it simply, eliminate the unnecessary details and only keep relevant details and the root problem.

#2 Collect Relevant Information and Data

After defining the problem, an engineer begins to collect all the relevant information and data needed to solve the problem. The collected data could be physical measurements, maps, outcomes of laboratory experiments, patents, results of conducted surveys, or any number of other types of information. Verify the accuracy of the collected data and information.

As an engineer, you should always try to build on what has already been done before. Don’t reinvent the wheel. Information on related problems that have been solved or unsolved earlier, may help engineers find the optimal solution for a given problem.

#3 Search for Creative Solutions

There are a number of methods to help a group or individual to produce original creative ideas. The development of these new ideas may come from creativity, a subconscious effort, or innovation, a conscious effort.

You can try to visualize the problem or make a conceptual model for the given problem. So think of visualizing the given problem and see if that can help you gain more knowledge about the problem.

#4 Develop a Mathematical Model

Mathematical modeling is the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides insight, answers, and guidance useful for the originating application.

To develop a mathematical model for the problem, determine what basic principles are applicable and then draw sketches or block diagrams to better understand the problem. Then define and introduce the necessary variables so that the problem is stated purely in mathematical terms.

Afterwards, simplify the problem so that you can obtain the required result. Also identify the and justify the assumptions and constraints in the mathematical model.

#5 Use Computational Method

You can use a computational method based on the mathematical method you’ve developed for the problem. Derive a set of equations that enable the calculation of the desired parameters and variables as described in your mathematical model. You can also develop an algorithm, or step-by-step procedure of evaluating the equations involved in the solution.

To do so, describe the algorithm in mathematical terms and then execute it as a computer program.

#6 Repeat the Problem Solving Process

Not every problem solving is immediately successful. Problems aren’t always solved appropriately the first time. You’ve to rethink and repeat the problem solving process or choose an alternative solution or approach to solving the problem.

Bottom-line:

Engineers often use the reverse-engineering method to solve problems. For example, by taking things apart to identify a problem, finding a solution and then putting the object back together again. Engineers are creative , they know how things work, and so they constantly analyze things and discover how they work.

Problem-solving skills help you to resolve obstacles in a situation. As stated earlier, problem solving is a skill that an engineer must have and fortunately it’s a skill that can be learned. This skill gives engineers a mechanism for identifying things, figuring out why they are broken and determining a course of action to fix them.

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All teams and organizations encounter challenges as they grow. There are problems that might occur for teams when it comes to miscommunication or resolving business-critical issues . You may face challenges around growth , design , user engagement, and even team culture and happiness. In short, problem-solving techniques should be part of every team’s skillset.

Problem-solving methods are primarily designed to help a group or team through a process of first identifying problems and challenges , ideating possible solutions , and then evaluating the most suitable .

Finding effective solutions to complex problems isn’t easy, but by using the right process and techniques, you can help your team be more efficient in the process.

So how do you develop strategies that are engaging, and empower your team to solve problems effectively?

In this blog post, we share a series of problem-solving tools you can use in your next workshop or team meeting. You’ll also find some tips for facilitating the process and how to enable others to solve complex problems.

Let’s get started! 

How do you identify problems?

How do you identify the right solution.

• Tips for more effective problem-solving

Complete problem-solving methods

• Problem-solving techniques to identify and analyze problems
• Problem-solving techniques for developing solutions

Problem-solving warm-up activities

Closing activities for a problem-solving process.

Before you can move towards finding the right solution for a given problem, you first need to identify and define the problem you wish to solve. 

Here, you want to clearly articulate what the problem is and allow your group to do the same. Remember that everyone in a group is likely to have differing perspectives and alignment is necessary in order to help the group move forward. 

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner. It can be scary for people to stand up and contribute, especially if the problems or challenges are emotive or personal in nature. Be sure to try and create a psychologically safe space for these kinds of discussions.

Remember that problem analysis and further discussion are also important. Not taking the time to fully analyze and discuss a challenge can result in the development of solutions that are not fit for purpose or do not address the underlying issue.

Successfully identifying and then analyzing a problem means facilitating a group through activities designed to help them clearly and honestly articulate their thoughts and produce usable insight.

With this data, you might then produce a problem statement that clearly describes the problem you wish to be addressed and also state the goal of any process you undertake to tackle this issue.  

Finding solutions is the end goal of any process. Complex organizational challenges can only be solved with an appropriate solution but discovering them requires using the right problem-solving tool.

After you’ve explored a problem and discussed ideas, you need to help a team discuss and choose the right solution. Consensus tools and methods such as those below help a group explore possible solutions before then voting for the best. They’re a great way to tap into the collective intelligence of the group for great results!

Remember that the process is often iterative. Great problem solvers often roadtest a viable solution in a measured way to see what works too. While you might not get the right solution on your first try, the methods below help teams land on the most likely to succeed solution while also holding space for improvement.

Every effective problem solving process begins with an agenda . A well-structured workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

In SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Tips for more effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.  

Try different approaches  

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally 

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator 

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

• Six Thinking Hats
• Lightning Decision Jam
• Problem Definition Process
• Discovery & Action Dialogue

Design Sprint 2.0

• Open Space Technology

1. Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

2. Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on. 

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages. 

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

3. Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design. 

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition   #problem solving   #idea generation   #creativity   #online   #remote-friendly   A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

4. The 5 Whys 

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges. 

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results. 

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys   #hyperisland   #innovation   This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

5. World Cafe

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold. 

World Cafe   #hyperisland   #innovation   #issue analysis   World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

6. Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD)   #idea generation   #liberating structures   #action   #issue analysis   #remote-friendly   DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

7. Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

8. Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology   #action plan   #idea generation   #problem solving   #issue analysis   #large group   #online   #remote-friendly   Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

• The Creativity Dice
• Fishbone Analysis
• Problem Tree
• SWOT Analysis
• Agreement-Certainty Matrix
• The Journalistic Six
• LEGO Challenge
• What, So What, Now What?
• Journalists

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed. 

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.  

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It!   #gamestorming   #problem solving   #action   Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

10. The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed. 

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable. 

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

11. Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around. 

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish. 

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis   #problem solving   ##root cause analysis   #decision making   #online facilitation   A process to help identify and understand the origins of problems, issues or observations.

12. Problem Tree 

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them. 

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree   #define intentions   #create   #design   #issue analysis   A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

13. SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward. 

SWOT analysis   #gamestorming   #problem solving   #action   #meeting facilitation   The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

14. Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results. 

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process. 

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID   #gamestorming   #project planning   #issue analysis   #problem solving   When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

16. Speed Boat

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!   

Speed Boat   #gamestorming   #problem solving   #action   Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

17. The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How   #idea generation   #issue analysis   #problem solving   #online   #creative thinking   #remote-friendly   A questioning method for generating, explaining, investigating ideas.

18. LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills. 

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO! 

LEGO Challenge   #hyperisland   #team   A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

19. What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems. 

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken. 

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What?   #issue analysis   #innovation   #liberating structures   You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

20. Journalists  

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists   #vision   #big picture   #issue analysis   #remote-friendly   This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for developing solutions 

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to narrow down to the correct solution.

Use these problem-solving techniques when you want to help your team find consensus, compare possible solutions, and move towards taking action on a particular problem.

• Improved Solutions
• Four-Step Sketch
• 15% Solutions
• How-Now-Wow matrix
• Impact Effort Matrix

21. Mindspin  

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly. 

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation. 

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex. 

MindSpin   #teampedia   #idea generation   #problem solving   #action   A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

22. Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result. 

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution. 

Improved Solutions   #creativity   #thiagi   #problem solving   #action   #team   You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

23. Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged. 

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch   #design sprint   #innovation   #idea generation   #remote-friendly   The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper,  Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

24. 15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change. 

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.   

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change. 

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

25. How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process. 

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud. 

How-Now-Wow Matrix   #gamestorming   #idea generation   #remote-friendly   When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

26. Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice. 

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them. 

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

27. Dotmocracy

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action? 

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus. 

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively. 

Dotmocracy   #action   #decision making   #group prioritization   #hyperisland   #remote-friendly   Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

• Check-in/Check-out
• Doodling Together
• Show and Tell
• Constellations
• Draw a Tree

28. Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process.

Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute. 

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out   #team   #opening   #closing   #hyperisland   #remote-friendly   Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

29. Doodling Together  

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start. 

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems. 

Doodling Together   #collaboration   #creativity   #teamwork   #fun   #team   #visual methods   #energiser   #icebreaker   #remote-friendly   Create wild, weird and often funny postcards together & establish a group’s creative confidence.

30. Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team! 

Show and Tell   #gamestorming   #action   #opening   #meeting facilitation   Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

31. Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible. 

Constellations   #trust   #connection   #opening   #coaching   #patterns   #system   Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

32. Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic. 

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree   #thiagi   #opening   #perspectives   #remote-friendly   With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

• One Breath Feedback
• Who What When Matrix
• Response Cards

How do I conclude a problem-solving process?

All good things must come to an end. With the bulk of the work done, it can be tempting to conclude your workshop swiftly and without a moment to debrief and align. This can be problematic in that it doesn’t allow your team to fully process the results or reflect on the process.

At the end of an effective session, your team will have gone through a process that, while productive, can be exhausting. It’s important to give your group a moment to take a breath, ensure that they are clear on future actions, and provide short feedback before leaving the space. 

The primary purpose of any problem-solving method is to generate solutions and then implement them. Be sure to take the opportunity to ensure everyone is aligned and ready to effectively implement the solutions you produced in the workshop.

Remember that every process can be improved and by giving a short moment to collect feedback in the session, you can further refine your problem-solving methods and see further success in the future too.

33. One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round. 

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them. 

One breath feedback   #closing   #feedback   #action   This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

34. Who What When Matrix 

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward. 

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved. 

Who/What/When Matrix   #gamestorming   #action   #project planning   With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

35. Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out! 

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised. 

Response Cards   #debriefing   #closing   #structured sharing   #questions and answers   #thiagi   #action   It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Save time and effort discovering the right solutions

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks  to build your agenda. When you make changes or update your agenda, your session  timing   adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore  how to use SessionLab  to design effective problem solving workshops or  watch this five minute video  to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of creative exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you! 

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

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Most companies live with unsolved problems. Are you tired of hearing “it can’t be solved” or “that’s just how it works”? Are you fed up with having a growing list of problems and a shrinking list of solutions?

There is another way. You can change it.

We Know. We've been there. We can help.

years of problem-solving experience

crisis  problems resolved over the past decade

problem solvers  developed and certified in the past 10 years

Problem Solving to Suit your Needs

Problem solving services to fit your application, industry, and team.

Red X Problem Solving

Solve complex technical problems fast by converging on the hidden cause

• Resilient Engineering

Expose & address high risk issues before your product launches.

Transaxional Problem Solving

Discover the source of business process failures without all the finger pointing

Rolling top 5 leadership

Maximize problem solving ROI by building competency and culture

Maximize problem solving ROI by building a competency and culture

A problem solving partner you can rely on

Reduce and eliminate quality & process problems with an expert partner to guide you in solving the “unsolveable” problems.

01 | Get Acquainted

Each business is different & our recommendations for yours will be customized down to the last detail. As a global problem solving resource, we have problem solvers that understand and speak your language.

02 | Create Partnership

We’ll solidify our partnership with a custom proposal and an agreement. Our partners often don’t want their business in the public eye. Our agreement encompasses both privacy and IP.

03 | Follow our strategy

Our experts guide your team to actual results. From programs geared toward root cause training to urgent problem solving needs, our strategy yields rapid resolution for your toughest issues.

04 | Happy stakeholders

When you can solve tough problems consistently, everyone wants you on their team. You can breathe easy knowing that your team is equipped to handle anything.

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Solve complex problems fast!

Problem Solved. 

Results Delivered.

• TransaXional
• Rolling Top 5
• Develop Your Team
• Dorian Awards
• Verify a Certification
• About Shainin
• Course Catalog
• Collaboration Platform
• Terms of Use
• Privacy Policy
• Qualiopi Certificate

Dorian Awards Criteria

Team project awards, transaxional business project of the year.

Business processes deliver critical functions. But sometimes processes break, and the reason isn’t clear. TransaXional Business Projects focus on what must go right in a process for it to deliver the desired result every time.  The  TransaXional Business Project of the Year Award recognizes projects that focus on process-based problems. Projects that win this award will demonstrate: 

• The impact of the problem for the company and the team 
• The strategy implemented to complete the project.
• Creative & effective use of the Shainin Transaxional tools  
• The timeline of resolution from initial discovery through project closure

Sergio Lizarraga

Gm mexico, technical dir. latin america.

Sergio serves as our General Manager in Mexico and Technical Director of Latin America with over three decades of experience in manufacturing and engineering. For the past 25 years, Sergio has been using the Shainin methodology to help companies solve complex problems, with a proven track record of success. 

Sergio’s qualifications include a BS in Electronics and Communications Engineering, a Master’s in IT Management, and a Red X Master certification. He has worked in a wide range of industries, including automotive assembly, automotive components, electronics, foundry, mechanical equipment, chemical products, and medical products. 

But what sets Sergio apart is his passion for teaching and coaching. He has traveled to different countries, including Mexico, the USA, Spain, Brazil, Argentina, and Portugal, to teach and coach the Shainin methodology. Additionally, he is a Master Practitioner in Neuro-Linguistic Programming. 

Sergio is known for his charismatic personality and his ability to inspire others. He enjoys playing guitar, spending time with his dogs, and reading in his free time, and is always looking to expand his knowledge and learn new things. 

Sergio’s wealth of experience, dedication to problem-solving, and passion for teaching make him an invaluable asset to any team. Whether you’re looking to tackle a complex manufacturing challenge or improve your team’s problem-solving skills, Sergio has the expertise and drive to help you achieve your goals. 

This category of awards is for project teams who have demonstrated outstanding application of Shainin methodologies in solving complex problems within their company.

To be considered for this award, the submission must meet the following criteria:

• Speed and efficiency of the problem solved
• Technical difficulty and complexity of the problem solved
• Project impact and leverage across the organization
• Creative use of Shainin technologies
• Clarity of the project documentation

RT5 Leadership Award

The Rolling Top 5 Leadership Award is for leaders who have increased the impact of problem solving using Shainin technologies within their organization.

• Demonstrates leadership guidance, structural framework, and effective mentoring of a dynamic problem-solving culture
• Conducts regular reviews to ensure that key projects are solved quickly and efficiently
• Encourages major suppliers and customers to jointly solve problems by using Shainin methods and leveraging the results
• Actively recognizes and rewards outstanding performance in problem- solving
• The number of successful projects and certifications

Master of the year Award

The Master of the Year Award is for leaders who have increased the impact of problem solving using Shainin technologies within their organization.

• Applies leadership skills to expand the impact of Shainin technologies within the organization
• Results include projects completed that made a significant impact to improving business performance
• The number of individuals coached to certification

Craig Hysong

President & ceo.

Craig Hysong is a problem-solving expert who has spent his career transforming how companies approach problems. As the President and CEO of Shainin, Craig places the customer at the center of everything he does and is 100% focused on customer satisfaction. 

With over 24 years of experience at Shainin and 13 years of experience at General Motors and ITT Automotive, Craig is a leader in his field. He holds a Bachelor of Science in Electrical Engineering from the Pennsylvania State University and a Master of Science in Manufacturing Systems Engineering from Lehigh University, where he attended as a General Motors Fellow. Craig is a licensed professional engineer and an American Society for Quality (ASQ) certified Quality Engineer, Reliability Engineer, Quality Auditor, and Manager of Quality & Organizational Excellence.

Craig’s commitment to his work and his customers is unparalleled. He has diversified Shainin’s customer base, implemented a standard sales process, and is working towards establishing the company as the global benchmark in problem-solving. Craig was the recipient of the 2017 Dorian Shainin Medal from ASQ, which recognized him for the development of an innovative conflict detection method. Additionally, his creativity in problem-solving has resulted in four US Patents.

Outside of work, Craig enjoys playing ice hockey, cross country skiing, bicycling, running, and water skiing. He also enjoys working on personal projects around the house and on his cars.

Craig’s passion for problem-solving, exceptional leadership skills, and dedication to customer satisfaction, combined with his down-to-earth and relatable personality, make him an invaluable asset to Shainin and the companies that seek our services.

Richard Shainin

Executive vp - training services.

With over five decades of experience in engineering, operations, marketing, and sales, Richard is a seasoned professional in the field of quality engineering and problem-solving.     A prolific author and lecturer, Richard has published papers in Quality Engineering and Six Sigma Forum and authored the “Multi-Vari Charts” chapter for the Encyclopedia of Statistics in Quality and Reliability. He is a frequent speaker at quality conferences, including the ASQ World Conference, where he shares his insights on solving complex problems.     Richard has received numerous accolades throughout his career, including being named the 2014 Quality Leader of the Year by the ASQ Automotive Division, and the Cecil C. Craig Lifetime Award from ASQ in 2019, in recognition of his publications.     Prior to joining the company in 1991, Richard led high-performance teams at AT&T, where he gained extensive experience in engineering, operations, marketing, and sales. Today, he continues to guide class development and delivery, working closely with customer leaders to develop and implement more effective organizations for solving complex problems.     With a Bachelor of Engineering degree from Stevens Institute of Technology and an MBA from American University, Richard is a graduate of the AT&T Management Development Program. He has trained thousands of engineers and executives in technical problem-solving skills and leadership skills, solving complex technical problems in castings, electro-mechanical systems, electronics, and assemblies across industries ranging from Automotive, Aerospace, and Consumer Electronics.     Richard’s expertise in problem-solving and leadership makes him an asset to the field of quality engineering, and his contributions continue to inspire professionals worldwide. 

John Abrahamian

Executive vp - problem solving.

John Abrahamian is a highly respected problem solver as well as an expert in the field of Lean manufacturing, with a career spanning over three decades. Throughout his career, John has become renowned for his innovative approach to problem-solving and his unwavering dedication to customer satisfaction.     After receiving his BS in Mechanical Engineering from the University of Connecticut in 1985, John began his career as a design and development engineer at Pratt & Whitney. It was during this time that his interest in problem-solving first emerged. By 1994, John had become a Continuous Improvement Manager at the company. During his tenure, John led Pratt & Whitney’s efforts in Lean manufacturing and Value Engineering.     In 1990, John began pursuing his MBA in Operations Management, where he was first introduced to the concept of Lean manufacturing, and this influenced the direction of his career. In 1996, he was encouraged by his Pratt & Whitney team to take Shainin Red X training, building on his Lean manufacturing efforts. This training proved to be a turning point in John’s career, igniting his passion for problem-solving and setting him on a path to becoming one of the industry’s most respected experts.     In 1998, John joined Shainin, where he has spent the last 25 years pursuing his passion for problem-solving. During his time here, John has developed innovative approaches to problem-solving, having received a US Patent for a problem-solving method. He also integrated function analysis into Shainin methods, seeding what would ultimately become Resilient Engineering.      Despite his busy schedule, John still finds time to pursue his hobbies, which include golfing, stand-up paddleboarding, and skeet shooting. He especially enjoys traveling with his wife and spending time with family, including his three grandsons.     Having the opportunity to work in a wide variety of industries, experiencing different cultures and meeting new and interesting people gives John the kind of job satisfaction that makes him grateful to be in this field of work. He truly enjoys creating meaningful relationships with his customers and inspiring ordinary engineers to become extraordinary problem solvers. 

Constantin Berg

Vp of operations.

Constantin Berg is the Vice President and Managing Director for Shainin’s European division. A graduate of the Technical University of Munich with a diploma in technology and management-based business administration, Constantin began his career at Linde gas and oil company where he first became acquainted with Shainin.     Joining Shainin’s European division as a consultant in 2011, Constantin trained extensively with Richard Shainin learning Red X Methodology. Over the next 9 years he rose through the ranks, eventually becoming VP of Operations, where he led the effort to restructure the division, which emerged stronger than ever before.     Constantin’s success earned him the role of Managing Director for Germany, India, and China. His dedication to the company and its vision of healthy growth into new regions has been crucial in shaping the division’s success.     Beyond his professional achievements, Constantin is a devoted family man, a loving father, and husband. He enjoys spending time with his family, gardening, playing soccer in a recreational league, and reading non-fiction books on business, and biographies.     Constantin is also passionate about making a positive impact on the world. He envisions partnering with corporations to help with environmental causes and supporting health organizations in researching root causes of disease. His desire to make the world a better place is inspiring to all who know and work with him.  

Esther Fondermann

Technical director, shainin gmbh.

Esther Fondermann is a highly accomplished technical leader with over a decade of experience. She currently serves as the Technical Director at Shainin GmbH, where she oversees the technical aspects of the company’s services, leads teams of engineers and technicians, and ensures compliance with industry standards and regulations. 

Esther’s drive towards excellence is evident in her ability to find the root cause of complex problems and implement effective solutions. Her commitment to delivering optimal customer experiences has earned her a well-deserved reputation for exceptional performance. She was introduced to the Shainin Red X methodology during her time at Mercedes, where she worked in different positions such as Quality Engineer in engine production, Master Black Belt in Vehicle Development, Manager of the Program Management Department, and as an Executive Assistant to the Vice President. Esther has extensive manufacturing, engineering, quality management and new product launch experience within the automotive and aerospace industries.  

At the age of 29, Esther earned her Six Sigma Master Black Belt certification, which was preceded by achieving her Red X Master certification. She was impressed by the Shainin methodology’s effectiveness in solving complex problems and improving quality, and she later joined Shainin GmbH to continue her commitment to quality engineering and continuous improvement.  

Esther’s career at Shainin GmbH began in 2012 when she joined as a Senior Technical Consultant, where she further honed her analytical skills to solve complex technical problems. She was quickly recognized for her abilities and was promoted to Technical Manager in 2016. In 2018, she was further promoted to Technical Manager for Europe and Delivery Lead for the D-A-CH region, where she continued to excel in her leadership role. Most recently, Esther was promoted to Technical Director and Member of the Board of Management Shainin GmbH in January 2019. 

Through her success as a former professional field hockey player, Esther has demonstrated her relentless pursuit of superior performance. She played over ten years in the 1st German Hockey League and for the German Hockey National Team. Her experience as an athlete and captain has contributed to her skills in teamwork, leadership, and handling pressure, all of which have been valuable assets in her career. 

Resilient Engineering PROJECT OF THE YEAR

The goal in new product or process development is a trouble-free launch. Resilient engineering focuses on the critical factors that impact the success of your design. The Resilient Engineering Project of the Year Award recognizes projects that focus on what must go right to ensure a successful launch. Projects that win this award will demonstrate:  

• Critical factor identification in the design phase 
• Prioritization of the factors to focus on 
• Potential or perceived impact for the customer, the company, and the production line. 
• Effective use of the Shainin Resilient Engineering tools to identify, prioritize, design, test, and control the critical factors.  

Director of Business Development

Tom Smith is the Director of Business Development at Shainin with over 20 years of experience in leadership roles in aerospace manufacturing, operations, engineering, and logistics.    Having been directly responsible for the P&L of multi-site manufacturing businesses with revenues ranging from $20M to $350M+, Tom has championed many iterations of strategically focused Shainin problem solving efforts.  Tom’s ability to quickly assimilate problem solving into operations and engineering functions has proven to be valuable and effective in driving immediate and sustained bottom line improvement.    After calling on Shainin multiple times as a satisfied client throughout his career, Tom decided to join the Shainin team directly in 2019.  His uniquely qualified background and experience have been a source of keen insight and understanding into the needs of Shainin’s customers.    Tom is a certified Shainin RT5 leader and Red-X Journeyman, as well as a Six Sigma Plus Blackbelt, with a deep understanding of OpEx, Lean, Six Sigma, and Red-X problem-solving methods.  He holds a Bachelor of Science in Mechanical Engineering and a Masters of Manufacturing Management, both from The Pennsylvania State University.  Tom is passionate about helping clients achieve their goals by fostering a culture of trust, continuous improvement, and teamwork. 

Diane Schwarzkopf

Chief financial officer/global controller.

Diane Schwarzkopf has been with Shainin since July 2016. Despite starting her career with a focus on Programming/Computer Science and later shifting to an Accounting degree, Diane never anticipated she’d be working for a global organization. Today, she navigates the significant complexities of her role as Global Controller with aplomb, leveraging a diverse knowledge base built on years of experience across various industries.      

Diane’s leadership style is characterized by a commitment to accuracy, collaboration, and responsiveness. Recognizing the importance of these attributes in her field, she strives to create an environment where management and staff feel heard and supported, and her direct reports are afforded ample opportunities for professional growth. She tackles challenges head-on while her forward-thinking approach in anticipating needs is instrumental in the smooth running of Shainin’s global operations.  

Beyond her professional life, Diane is a passionate reader, immersing herself in a variety of genres ranging from suspense and mystery to neuro-linguistic programming and leadership books. She also indulges in crafting activities, particularly knitting and crocheting. Diane and her husband relish outdoor activities, including kayaking and running. She is mother to two adult children and a genuine pet lover who dotes on her cats, Sophie and Ivan, and Truda, her energetic Doberman.  

General Counsel

Gina Rozak is our General Counsel at Shainin. With over a decade of legal expertise, Gina understands the importance of building strong relationships with our customers, ensuring that they’re successful with implementing Shainin methodology while protecting the interests of all parties.

Gina holds a Bachelor of Science degree from Central Michigan University and attended law school, receiving her Juris Doctorate degree from Thomas M. Cooley in Lansing, Michigan. She’s licensed to practice law in Michigan and was admitted to practice in the Eastern District of Michigan Federal Court. With years of experience under her belt, Gina has developed ongoing relationships with our customers, providing her with valuable insights into the legal challenges that businesses like yours face.  

When she’s not working, Gina loves to spend time with her family, bake delicious treats, and keep fit by running. Her weekends are usually filled with getting kids around to their different activities and sometimes she gets to relax by indulging in TV shows ranging from historical fiction to reality, to the many fascinating subjects on the History Channel.  

Pete Shainin

Pete Shainin is a highly qualified and experienced mechanical engineer with over 57 years of experience in engineering, 39 of which he has spent at Shainin. He earned his Mechanical Engineering degree from Stevens Institute of Technology in 1966 and became a Professional Engineer in Washington State in 1970.  

Pete’s career began at Pratt & Whitney Aircraft, where he worked as a Quality Control Engineering Assistant and a Product Development Engineering Assistant. He then moved on to Marine Construction & Design, where he worked as a Marine Deck Machinery Design Engineer. Pete also worked at Standard Screw as an Engineering Assistant to Vernon Roosa, inventor of the Roosamaster Diesel Injector System, before becoming a Marine Deck Machinery Sales Engineer at Skagit Corp.  

After many years of working in the family business and taking on more leadership roles, Pete became CEO of Shainin LLC and eventually transitioned to the role of Chairman in 2010, where he currently serves.  

Throughout his career, Pete has achieved many successes, including building and leading teams that created the Shainin worldwide engineering business starting with his father’s work as a single individual. This is mirrored in his passion for sailing, where he led a crew of eight to victory in the challenging 2300-mile race from Victoria, British Columbia to Maui, Hawaii, in 2006.  

Just like in business, sailing requires teamwork, strategy, and dedication. Pete’s ability to bring his team together and lead them to success in the face of adversity is a testament to his leadership skills both on and off the water. What sets Pete apart is his love of building successful teams and his humility.  

Outside of work, Pete enjoys designing and building mechanisms to solve specific problems, and sailing. His dedication and commitment to excellence have brought him success in all aspects of his life, whether it’s in the boardroom or on the deck of a sailboat.

PLANT MANUFACTURING PROJECT OF THE YEAR

Manufacturing is a world all its own. With the fast-moving pace, the speed of solving problems matters. The Plant Manufacturing Project of the Year Award recognizes projects that resolve complex problems in ongoing production which impact the end user, company bottom line, production quality rates, and the like. Projects that win this award will demonstrate:

• Technical difficulty or complexity in resolving the issue.
• Creative and effective use of the Shainin Red X tools to uncover the root cause.
• Impact of resolving the problem.
• How the solution or information discovered was leveraged.
• Timeline for resolving the issue.

Field Reliability PROJECT OF THE YEAR

Field failures impact more than just your bottom line. This category is dedicated to projects that focus on field issues such as ‘No Trouble Found’, fatigue failures, and other destructive or malfunction events. Projects that win this award will demonstrate:

• Initial impact of the problem.
• Creative and effective use of the Shainin Red X tools to uncover the root cause. 
• Speed and efficiency in resolving the issue.  

Product Development PROJECT OF THE YEAR

This category recognizes technical problems solved during product development and launch. These projects typically have small sample sizes to work with. Product development begins the lifecycle of a product. In the early stages, we want to test products so we can account for and adjust to prevent failures. Projects that win this award will demonstrate: 

• Potential impact of the problem.
• Effective use of the Shainin Red X tools to uncover the root cause.
• The timeline of resolution from initial discovery to solution implemented. 

Bottomline Improvement Company Award

This category of awards is for companies who have succesfully applied Shainin tools within a facility, division, region, or company and can demonstrate impactful improvements based on that development.

• Demonstrates impact of Shainin technology greatly improving key business metrics. 
• Examples include: scrap reduction, yield improvement, throughput, warranty reductions, field failure avoidance, and other technical solutions resulting in savings. 
• Demonstrates use of project selection tools to identify the “vital few” among the “trivial many” (projects that have impact)

Controls the level of style and functionality of the site, a lower fidelity meaning less bandwidth, battery, and CPU usage. Learn more .

Bulletproof Method to Solving Problems

Step 1 : Write down the problem in a message you plan to send to a co-worker.

Most of the time you’ll solve the problem before you’re done with Step 1. However, if you complete Step 1 and still have the problem, continue to Step 2.

Step 2 : Hit the “Send” button.

Shortly after sending, the solution will present itself. I don’t know why this is. I don’t make the rules. But the solution frequently presents itself after you hit “Send” and no longer need the recipient’s help.

Step 3 : Return to message you just sent and follow up with: “Nevermind. Figured it out.”

Ok, ok. This is in jest — a little bit. But it is a good method for getting yourself unstuck.

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Team 24045’s basketball-shooting robot, nicknamed Hoopster, is designed to compete in a free throw competition with a Wildcat basketball team player.

Basketball-Shooting Robot Inspires at Design Day 2024

The atmosphere was electric as thousands of members of the public, local high school science classes and industry judges were wowed at the College of Engineering’s 2024 Craig M. Berge Design Day.

The annual event is an opportunity for engineering seniors to present the efforts of their yearlong capstone class to the public and hundreds of judges. Seniors from majors across the college made up 91 multidisciplinary teams who completed projects requested by industry and university sponsors.

Students had two semesters to design, prototype and test their projects to compete for $49,000 in prizes on April 29. The teams this year persevered through novel challenges and challenging setbacks to find solutions to some of society’s toughest problems.

“It’s about designing, prototyping, testing, failing, trying again, and leaning on your teammates. But ultimately, it’s about making the world a better place. I’m really proud of all our engineers, mentors and sponsors,” said David W. Hahn, Craig M. Berge Dean of the college, to open the award ceremony.

Taking Shots for STEM Education

The $7,500 top prize, the Craig M. Berge Dean’s Award for Most Outstanding Project, went to Team 24045 for Hoopster, a basketball-shooting robot. It was designed to demonstrate engineering concepts in a way that inspires K-12 students to consider careers in the field.

The fully autonomous robot can repeatedly score shots from the free throw line with 90% accuracy. The team continued the work of a group that began the project last year and completely redesigned the plan.

Hoopster automatically detects basketball hoops with the help of an in-house camera and computer vision algorithms. It then calculates the precise trajectory and power needed to land inside the hoop.

But project lead Eric Meyer said the robot has loftier aspirations than just making free throws.

“The purpose is to inspire middle school and high school students in Tucson to become the next generation of engineers and STEM professionals,” said the systems engineering senior.

The team used a video game controller as Hoopster’s navigational tool to make the robot more personable and engaging to young people. They also demonstrated their robot to students at a local school before Craig M. Berge Design Day.

“We worked for the past nine months to create something that’s a high-precision robot and excites students through science and sport,” Meyer said.

Raytheon Technologies sponsored Hoopster to address what project adviser Luke Baer called the “profound purpose” of nurturing an engineering pipeline.

“The team far exceeded all expectations,” said Baer, Javelin applications technical lead at Raytheon and UA electrical and computer engineering alum. “By connecting engineering concepts to sports, they truly have created a means to teach and inspire our next generation of engineers.”

Meyer said his experience leading the project has prepared him for the workforce. He’s interning with Interdisciplinary Capstone program sponsor Northrop Grumman and plans to complete the accelerated master’s program in engineering management .

Team 24052 also completed a project to bolster student success in engineering, winning the $1,000 Mark Brazier Award for Best Biomedical System Design. The MediBrick 2000, or modular biomedical sensor board, is a cost-effective device designed for biomedical engineering students to measure vital signs like oxygen saturation.

Similar devices BME students use now are expensive, averaging $4,000, said project procurement lead Muad Alsayar.

“It’s pretty outdated, hard to fix, and it’s not user-friendly for students. But this [MediBrick 2000] only costs $500, and it’s easier to fix and modify,” said Alsayar, who plans on continuing his education in the UA electrical engineering graduate program.

Creative Problem-Solving

Design Day is the capstone to the college’s  four-year design program . Both are named in honor of Craig M. Berge, an engineering alum and longtime supporter who died in 2017. His wife, Nancy Berge, said Design Day reflected Craig’s passions, as he always loved doing hands-on projects and solving problems.

But some projects this year took more perseverance than others, challenging students to overcome problems they will encounter as they enter the workforce after graduation.

“There was a lot of red tape to cut,” said Daniel Hutton, chemical engineering lead for Team 24011.

Hutton’s team earned first prize for the Bly Family Award for Innovation in Energy Production, Supply or Use. They built a small-scale pyrolysis plant that turns plastic waste into fuel.

“Our project is designed to provide disaster relief to island nations where fuel is scarce, so we are recycling plastic and turning it into a crude, diesel-like fuel that can be used by generators,” said Hutton, who will join the chemical engineering doctoral program in the fall.

However, the pyrolysis process involves heating plastic to 1,000 degrees Fahrenheit. Safety concerns left the team in a fourth-month lull – awaiting approval from university units to prototype their design.

“We have emissions of toxic gas that need to be stabilized, so we had to create an extremely thorough job safety assessment, and we had to work with multiple departments and administrations to get that approved,” said project lead Jamie Holmstrom.

Holmstrom said the experience gave her a new perspective on the industrial engineering process.

“There was problem after problem we had to solve with things I never thought we would do before,” said the chemical engineering senior. “I learned to keep an open mind and be creative.”

Engineering Creates Numerous Solutions

A wide variety of projects garnered awards, including Smart Energy Grid Simulation , an artificial-intelligence-powered energy grid that can automatically redirect power when a line goes down. This project earned Team 24039 the $2,500 BAE Systems Award for Best System Software Design.

Team 24005 walked away with the $1,500 Coherent Award for Best Optical Systems Design for its spectrometer that can identify someone wearing camouflage up to 500 yards away.

View the Design Day booklet and project videos.

Craig M. Berge Design Day 2024 Winners

Craig M. Berge Dean’s Award for Most Outstanding Project – $7,500 

Team 24045: Basketball Shooting Robot Phase 2 

Saul Durazo Martinez, Julia Christine Otto, Katelyn Rees, David William Ruddell, Eric Meyer, Caleb Cook 

Project Sponsor: Raytheon Technologies 

Raytheon Technologies Award for Best Overall Design – $5,000 

Team 24009: Autonomous Multi-Legged Robot for Crop/Turf Management 

Rebekah June Cutler, Gabe Eleazar Maldonado, Kory Pearson, Ziyan Wei, Daniel Folse, Annalisa Minke 

Project Sponsor: UA Biosystems Engineering 

RBC Sargent Aerospace & Defense Voltaire Design Award– $2,500 

Team 24020: Wearable Mechanical Device for the Wrist 

Samuel Gonzalez, Jacob A Mondry, Valeria Maria Villarino, Thomas Zachariah Murickan, Brandon Bounds 

Project Sponsor: UA Biomedical Engineering/UA College of Medicine 

L3Harris Commercial Aviation Solutions Award for Most Robust Systems Engineering – $2,500 

Team 24018: Understanding the Normal Aging Brain So That the Puzzle of Alzheimer’s Can Be Solved Phase 2 

Guntaas Singh Chadha, Max Duque, Jason Freeman, Ritik Makhija, David Polk  

Project Sponsor: McDonald/Watt Projects 

BAE Systems Award for Best System Software Design – $2,500 

Team 24039: Smart Energy Grid Simulation 

Sebastian Govea, Adam Weida Hoffmeister, Ian Johnson, Gloria Romero, Jamie Newhall, Felipe Parra Polanco 

Project Sponsor: Tucson Electric Power 

Bly Family Award for Innovation in Energy Production, Supply or Use (First Prize) – $2,000  

Team 24011: Plastic Recycling, Carbon Capture and Disaster Relief Through Pyrolysis 

Ben Hunt, Daniel Hutton, Reina Kelley, Gracie Reinholz, Jamie J Holmstrom, Celeste R Cortez 

Project Sponsor: PeakView Solutions 

Bly Family Award for Innovation in Energy Production, Supply or Use (Second Prize) – $1,000  

Team 24014: Mechanical Energy Storage System 

Matthew English, Cecil Mrstik, Ryan A Sunga, Jaime Jose Valencia, Connor M Perkins, Gavin Tampa 

Project Sponsor: Resolution Copper 

Roche Tissue Diagnostics Award for Most Innovative Engineering Design – $1,500 

Team 24004: The Development of a Device to Record Spinal Cord Blood Flow During Surgery  

Omar Amanullah, Ryan Chen, Nicholas Elijah Matthews, Yumou Wan, Abhiman Gupta, Preston Joseph Leigh  

Project Sponsor: Neurovascular Research and Design 

School of Mining & Mineral Resources Lundin Award for Innovation in Mining– $1,500 

Team 24076: Phase 3 Pit Design 

Reyna E Clark, Esteban Guerrero Murrieta, Ethan Lathrem, Savannah M Stewart, Joseph A Welch, Shayne Elise Zadro 

Project Sponsor: Capstone Mining/UA Mining & Geological Engineering 

School of Mining & Mineral Resources Lowell Award for Interdisciplinary Solutions for Mining – $1,500 

Rincon Research Award for Best Presentation – $1,500 

Team 24065: WATER-SAFE - PFAS/Microplastic Water Detection System for Environmental and Human Health 

Dani Balicki, Rachel Emily Nehrmeyer, Togzhan Spatayeva, Ashley Tittelbaugh, Evan Brorby, Matthew Martinez 

Project Sponsor: Kidney ADVANCE Project/NIH/UA Center for Accelerated Biomedical Innovation 

Coherent Award for Best Optical Systems Design – $1,500 

Team 24005: Imaging Spectrometer for Defeating Camouflage 

Brian Castellanos, Mason Eves, Lilian Naves, Cole Suddarth, Logan Pawlowski, Ross Margulis 

Project Sponsor: BAE Systems 

W.L. Gore and Associates Award for Lifelong Innovation – $1,250 

Team 24021: PneumaBrace - A Wearable Electronic Carpal Tunnel Relief Brace 

Nawaf Z Almutairi, Zack Amstutz, Michael Cesar-Torres, Christian A Vanasco, Wakil Ur Rahman, Aydin Saucier 

Sharon ONeal Award for Software Development With Emerging Technologies – $1,000 

Team 24040: Model Based Electrical Diagram and TID Generation 

Jesus Garcia, Julia Rima, Chase Craver, Richardo Larez, Rafael Pacheco 

Project Sponsor: Northrop Grumman 

Steve Larimore Award for Perseverance & Recovery – $1,000 

Team 24028: Rotating Detonation Engine Rocket Design and Launch 

Raul Beltran, Henry Chambers, Henry Overbeck, Kevin D Strout, Andrew James Lefcourt, Leah McAdams 

Project Sponsor: Nobel/R3 Aerospace 

Honeywell Award for Excellence in Aerospace Mechanical System Design – $1,000  

Team 24044: Wind Tunnel – Launch Vehicle Aerodynamic Testing 

Oscar Jonathan Lopez, Konnor Benjamin Raskin, AJ Sandler, Savannah A Shah, Alex Kylie Daily 

Honeywell Award for Excellence in Aerospace Electronic System Design – $1,000 

Team 24054: CubeSat Centrifuge Terrarium 

Paul Shaheen Lynch, Hannah N Perez, Neo Stilson, Connor Zell, Jake Daniel Hathaway 

Project Sponsor: NASA 

Larry Head Award for Best Video Capturing the Project Story – $1,000 

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Project Sponsor: UA Aerospace & Mechanical Engineering 

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Project Sponsor: Freeport-McMoRan 

Henry & Suzanne Morgen Award for Best Consideration of The End User – $1,000 

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Project Sponsor: UAPD/FBI – Craig M. Berge Dean’s Fund 

Mark Brazier Award for Best Biomedical System Design – $1,000 

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Project Sponsor: UA Department of Biomedical Engineering 

Coherent Fish Out of Water Award – $750 

Walter Rahmer (Team 24027: Rotating Detonation Engine Aerospace Nozzle Design Optimization) 

Dataforth Corporation Award for Best Utilization of the Internet of Things– $750  

Team 24023: Chemotherapy Port App 

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Project Sponsor: BD 

Phoenix Analysis & Design Technologies Award for Best Use of Prototyping – $750 

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AZ Technica Award for Sustainable Manufacturing Innovation – $500 

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AZ Technica Award for Manufacturing Readiness – $500 

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Lawrence Livermore National Laboratory Award for Impactful Application of Science & Technology – $500 

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Simpson Family Award for Best Simulation and Modeling – $500 

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Eric Bhe, Sidney K Franklin, Marguerite Gilman, Megan Wildridge, Morgan Goz 

Project Sponsor: Honeywell 

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Juan Ruben Cortez (Team 24066: Baseball Swing Rotation Trainer) 

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2024 dorian awards honor teams and leaders at philips, robert bosch mx, baker hughes for problem-solving excellence.

NORTHVILLE, Mich. , May 1, 2024 /PRNewswire/ -- Every year, teams around the world complete problem-solving projects that are worthy of recognition. The Dorian Awards, held annually, honor problem-solving teams, leaders, and companies who have demonstrated exceptional application of Shainin methods and leadership during the previous year. These awards celebrate excellence in both engineering and business processes and recognize the contributions of individuals and teams in pushing the boundaries of innovation.

Awards Presentations Winners were announced in February via Shainin's website and awards presentations are currently in process at the winners' respective facilities.

Project Awards This year's event saw strong competition, with participants from all over the world vying for recognition. The winners of our project-specific awards were tasked with projects of critical importance to our customers, as they helped resolve long-standing issues, improve bottom lines, and enhance operations around the globe.

Leadership Awards For our leadership categories, the winners showed not only improvement for themselves but for their companies and their teams. President and CEO of Shainin, Craig Hysong , shares, "I am continually amazed by the ingenuity and determination of the Dorian Award finalists and winners. They have ushered in a new era of quality improvement that will benefit their companies for years to come. We are honored to recognize their achievements."

2024 Dorian Award Winners

Company Awards

BOTTOM LINE IMPROVEMENT AWARD: Phinia

Auburn Hills MI , US

REUTER- STOKES , A BAKER HUGHES BUSINESS

Twinsburg Ohio , USA

LIEBHERR MACHINES BULLE

Bulle, Switzerland

SCHNEIDER ELECTRIC

Shanghai, China

Project of the Year Awards

PLANT MANUFACTURING PROJECT OF THE YEAR: TE CONNECTIVITY – EVREUX, NORMANDIE, FRANCE Team Leader: Jimmy Huet Team Members: Petr Nečas, Julien Berrenger , Alan Cerciat, Carine Guillo

FIELD RELIABILITY PROJECT OF THE YEAR: DAIMLER TRUCK AG – GAGGENAU, BADEN-WUERTTEMBERG, GERMANY Team Leader: Michael Goetz Team Member: Patrick Herzog

PRODUCT DEVELOPMENT PROJECT OF THE YEAR: PHILIPS PERSONAL HEALTH – DRACHTEN, THE NETHERLANDS Team Leader: Mannold van de Schootbrugge Team Member: Alwin de Vries

RESILIENT ENGINEERING PROJECT OF THE YEAR: WIELAND – PINE HALL, NORTH CAROLINA , USA Team Leader: Daniel Vigil Team Members: Scott Bryant , Eddie Cass

TRANSAXIONAL BUSINESS PROJECT OF THE YEAR: BAKER HUGHES – HOUSTON, TEXAS , USA Team Leader: Aldo Alarcon Team Members: Chantelle Nash , Lawrence Chapman

Leadership Awards

ROLLING TOP 5 LEADERSHIP EXCELLENCE WINNERS:

TOBIAS JUNG Robert Bosch Mexico

CARLOS EDUARDO FRAGA Customer & Product Support LATAM Iveco Latin America, Brazil

STEVEN VICKERS dormakaba Indianapolis, Indiana , USA

DR. SUJATHA NARAYAN  Senior Vice President & India Region Leader Wabtec Corporation, India

RED X MASTER OF THE YEAR:

JUAN PACHECO American Axle & Manufacturing Silao, Guanajuato, Mexico

Shainin, rooted in the pioneering legacy of Dorian Shainin , is a premier problem-solving firm that innovates alongside its customers to address critical business challenges. Our methodologies are designed for a wide range of industries, driving superior quality, fewer defects, and enhanced reliability in products and processes. We are committed to evolving technology into the future and swiftly pinpointing root causes. By combining our rich heritage with continuous innovation, Shainin is dedicated to empowering businesses with solutions that ensure success now and in the future.

Visit us at Shainin.com and follow us on LinkedIn .

View original content to download multimedia: https://www.prnewswire.com/news-releases/2024-dorian-awards-honor-teams-and-leaders-at-philips-robert-bosch-mx-baker-hughes-for-problem-solving-excellence-302132295.html

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Solution of engineering design and truss topology problems with improved forensic-based investigation algorithm based on dynamic oppositional based learning

• Original Article
• Open access
• Published: 02 May 2024

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• Funda Kutlu Onay   ORCID: orcid.org/0000-0002-8531-4054 1  

The forensic-based investigation (FBI) is a metaheuristic algorithm inspired by the criminal investigation process. The collaborative efforts of the investigation and pursuit teams demonstrate the FBI’s involvement during the exploitation and exploration phases. When choosing the promising population, the FBI algorithm’s population selection technique focuses on the same region. This research aims to propose a dynamic population selection method for the original FBI and thereby enhance its convergence performance. To achieve this objective, the FBI may employ dynamic oppositional learning (DOL), a dynamic version of the oppositional learning methodology, to dynamically navigate to local minima in various locations. Therefore, the proposed advanced method is named DOLFBI. The performance of DOLFBI on the CEC2019 and CEC2022 benchmark functions is evaluated by comparing it with several other popular metaheuristics in the literature. As a result, DOLFBI yielded the lowest fitness value in 18 of 22 benchmark problems. Furthermore, DOLFBI has shown promising results in solving real-world engineering problems. It can be argued that DOLFBI exhibits the best convergence performance in cantilever beam design, speed reducer, and tension/compression problems. DOLFBI is often utilized in truss engineering difficulties to determine the minimal weight. Its success is comparable to other competitive MAs in the literature. The Wilcoxon signed-rank and Friedman rank tests further confirmed the study’s stability. Convergence and trajectory analyses validate the superior convergence concept of the proposed method. When the proposed study is compared to essential and enhanced MAs, the results show that DOLFBI has a competitive framework for addressing complex optimization problems due to its robust convergence ability compared to other optimization techniques. As a result, DOLFBI is expected to achieve significant success in various optimization challenges, feature selection, and other complex engineering or real-world problems.

Avoid common mistakes on your manuscript.

1 Introduction

Converging to optimal results for practical and complex optimization problems is a common challenge in real-world problems. In particular, complex and difficult-to-converge engineering and mathematical problems have led to the development of various optimization techniques. Various derivative-based methods are used in the optimization of mathematical equations. Among these methods are Newton-based approach [ 1 ], Broyden–Fletcher–Goldfarb–Shanno Algorithm [ 2 ], Adadelta [ 3 ], AdaGrad [ 4 ], etc. On the other hand, derivative-independent optimization techniques such as population-based, sequential model-based, local optimization hill climbing, and global optimization [ 5 ]. Metaheuristic algorithms (MAs), typically designed and implemented based on population dynamics, yield effective solutions for mathematical and real-world problems. MAs have a rapid and effective convergence strategy due to their ability to handle non-convex problems and their structure, which does not rely on derivatives.

The inspiration of MAs from natural and mathematical phenomena leads to increased cases due to randomness. However, metaheuristic techniques illuminate many issues related to effective search and convergence methods. MAs progress through two stages: exploitation and exploration. The balance of these two phases is required for a metaheuristic technique. If this balance is not maintained, either the local search becomes excessively practical and misses the global optimum point, or the global search becomes excessively practical. Even if the global optimal zone is found, it may not be converged to the local optimum point [ 6 ].

There are four types of MAs: evolution-based, swarm-based, physics/mathematics-based, and human-based. Evolutionary algorithms simulate natural selection and genetic crossover processes. This category includes algorithms such as genetic algorithms (GA) [ 7 ], evolution strategies (ES) [ 8 ], and genetic programming (GP) [ 9 ]. Swarm-based algorithms solve optimization problems by mimicking the behavior of living organisms that naturally move in groups. Algorithms such as particle swarm optimization (PSO) [ 10 , 11 , 12 ], ant colony optimization (ACO) [ 13 ], and artificial bee colony (ABC) [ 14 ] are evaluated in this group. Physics and mathematics-based algorithms are designed to solve problems that require optimization using principles from physics and mathematics. Examples of some physics and mathematics-based metaheuristic algorithms include simulated annealing (SA) [ 15 ], gravitational search (GSA) [ 16 ], and black hole algorithm (BH) [ 17 ]. Human-based metaheuristic algorithms are algorithms inspired by human behavior or interactions. Each human-based algorithm utilizes human social skills to explore and enhance solutions, mimicking various human interactions and sources of information. Each human-based algorithm uses human social skills to examine and improve solutions while mimicking different human interactions or sources of information. Tabu Search (TS) [ 18 ], teaching learning-based optimization (TLB) [ 19 ], and forensic-based investigation algorithm (FBI) [ 20 ] improved in this study are also considered under this category.

MAs may differ in their application areas, performance, advantages, and disadvantages. At this point, MA development, enhancement, and hybridization have recently gained significant attention in the literature as crucial subjects for achieving more effective and efficient optimization solutions. New methods can be proposed by integrating techniques that involve parameter adjustments, changing operators, selection strategies, elitism, and local search, and by enhancing population distribution within the framework of metaheuristic algorithms. Oppositional based learning (OBL) [ 21 ] is a learning paradigm and one of the approaches utilized for improvement. OBL can be used in artificial intelligence to address problems like data mining, classification, prediction, and pattern recognition. OBL improves learning by utilizing the conflicting characteristics of two opposed notions. This situation develops as a means for the metaheuristic algorithm to improve efficiency and performance in reaching the ideal result across multiple solution spaces by picking diverse populations. Dynamic oppositional based learning (DOL) [ 22 ] is a dynamically adapted version of the OBL. In contrast to OBL, DOL seeks to improve results by making the process more flexible and adaptive. With DOL, reacting to changing conditions more efficiently and getting the best results thus far may be feasible. DOL can update features dynamically to meet shifting data distributions and evolving features over time. When new data is added, or old data is discarded, it can recalculate and optimize conflicting notions, emphasizing DOL’s adaptable nature.

The following are some of the uses of the OBL and its derivatives in metaheuristic and usage areas: Balande and Shrimankar created the OBL learning paradigm in collaboration with TLB, a human-based metaheuristic for optimizing the permutation flow-shop scheduling problem [ 23 ]. Izci et al. enhanced the arithmetic optimization algorithm with modified OBL (mOBL-AOA) [ 24 ] and applied it to benchmarks. Elaziz et al. used DOL with atomic orbit search (AOSD) for the feature selection problems [ 25 ]. Sharma et al. introduced DOL-based bald eagle search for global optimization issues, naming it self-adaptive bald eagle search (SABES). Shahrouzi et al. proposed static and dynamic OBL with colliding bodies optimization, a robust optimization technique tested using global optimization benchmarks in numerous engineering applications [ 26 ]. Khaire et al. integrated the OBL and sailfish optimization algorithm to identify the prominent features from a high-dimensional dataset [ 27 ]. Wang et al. have proposed hybrid aquila optimizer and artificial rabbits optimization algorithms with dynamic chaotic OBL (CHAOARO) for some engineering problems [ 28 ]. Yildiz et al. utilized a hybrid flow direction optimizer-dynamic OBL for constrained mechanical design problems [ 29 ].

Although the algorithms stated above are widely employed in numerous sectors in the literature, new methods for MAs are continually being presented. This is because not all optimization issues can be solved by a metaheuristic method. The no free lunch (NFL) theorem provides additional evidence for this [ 30 ]. As a result, when a novel approach is proposed, it is validated against real-world issues and mathematical benchmark functions. This study proposes using the DOL paradigm to create the FBI algorithm. The FBI algorithm includes stages for investigation and pursuit. Here, two distinct demographic groupings are involved in two stages. The goal of the investigation phase is to locate where suspicion is most likely to exist. The uncertainty probability is computed to ascertain this. The location indexes calculated in the original FBI are chosen randomly. In DOLFBI, however, the DOL updated the location and relayed to the chase team. Similar activities are carried out during the pursuit stage, and the location information collected by DOL is transmitted back to the investigation stage. This method is repeated until the optimal location is determined using the probability values.

The primary motivations and contributions of this paper are given as follows:

The present work suggests an enhanced algorithm for forensic-based learning through the use of oppositional based learning (DOLFBI).

The DOL paradigm has been added to the FBI for opposite population selection. The study proposes improving the FBI algorithm’s random population selection process by applying opposite integers because opposed numbers narrow the search space, allowing for more effective scanning and faster convergence.

Benchmark suites (CEC2019 and CEC2022), engineering, and truss topology problems are all being used to evaluate convergence capabilities. In particular, truss topology problems are significant and complex optimization problems specific to civil engineering. In other words, the goal of the truss topology problem is to minimize the weight of distinct structural components produced from different node numbers to determine the optimal sections.

The proposed method has been tested with metaheuristics commonly used in the literature, and its convergence ability has been studied using average and best findings. Furthermore, the Wilcoxon sign and Friedman rank tests were used to validate the results.

The article’s organization is as follows: Sect.  2.1 and their subsections have the working principle, algorithmic structure, and mathematical model of the FBI algorithm. Section  2.2 discusses the DOL paradigm and its impact and added value. Section  3 consists of the working flow of the FBI algorithm developed with DOL and the pseudo-codes of the proposed method DOLFBI. Section  4 includes parameter settings of the proposed and compared algorithms, properties, and experimental results of the CEC2019 and CEC2022 benchmarks, well-known engineering problems, and truss topology optimization problems, supporting the results with statistical tests. Sect.  5 includes an overview of the study and future objectives.

2 Materials and methods

The basic FBI algorithm and the DOL principle, which form the basis of the proposed DOLFBI algorithm, will be explored in detail under subheadings in this section.

2.1 Forensic-based investigation algorithm

This section describes the forensic investigation procedure, the details of the suggested algorithm and its mathematical model. A visual of the investigation process and a flow diagram of the FBI algorithm’s operation are presented for further clarity.

2.1.1 Steps of the forensic investigation process

Forensic investigation is one of the most risky tasks in which law enforcement is frequently involved for any country. For each research, a different path may be required. In some cases, the incident immediately enters the suspect management phase, and in others, the criminal’s name may be disclosed due to multiple investigations. However, research activities tend to be comparable [ 20 ].

Salet [ 31 ] stated that a large-scale forensic investigation by police officers consists of five steps, and Fig.  1 illustrates these five steps. Steps 2, 3 and 4 are defined as a cyclical process.

The phases of the investigation process

These phases, along with their explanations, are outlined as follows:

Open a case: The information discovered by the first police officers on the scene launches the investigation. Team members look into the crime scene, the victim, potential suspects, and background information. They also locate and question potential witnesses.

Interpretation of findings: Team members attempt to gain an overview of all accessible information. The team attempts to connect this knowledge to their present situation perception.

Direction of inquiry: This is the stage at which team members construct distinct hypotheses based on their Interpretation of the findings. Based on these findings, the team approves, modifies, or terminates a new direction or current research recommendations.

Actions: In light of the lines of research and the determined priorities, the team makes decisions on other actions. Priorities are vital at this step, and the most promising research direction is explored first. As a result, new information may be presented, and the team assesses its meaning or implications based on the information available. Changes in research and action may be required to interpret new findings.

Prosecution: This process is repeated until a clear and unambiguous picture of the incident is obtained. It ends when a serious suspect is identified before determining whether or not to prosecute them.

There are no hard and fast rules governing the number of police officers involved in an investigation, and this number is frequently tied to the gravity, difficulty, and complexity of the case.

2.1.2 Mathematical model of the algorithm

The FBI algorithm is a human-based metaheuristic algorithm inspired by police officers’ forensic investigation processes. An investigation can be launched after receiving notification of criminal activity. The investigation involves identifying physical evidence, acquiring information, collecting and preserving evidence, and questioning and interrogating witnesses and suspects [ 32 ]. Based on the information gathered and witness declarations, all probable suspects within the search area are identified, and likely locations are determined. The FBI makes two assumptions: there is a single most sought suspect in an incident, and that individual remains in hiding throughout the investigation. The process results in the capture and arrest of the suspect.

An investigation team is organized to investigate "suspicious places," prospective hiding places for the suspect. After the investigative team has determined the most likely location, a search area is established, and a pursuit team is assembled. All tracking team members travel to the indicated site with team members capable of apprehending the suspect.

The pursuit team proceeds toward the suspected location following the head office’s orders and reports all information regarding the suspected location. The investigation and pursuit teams collaborate closely throughout the investigate-find-approach process. The investigation team directs the tracking team to approach the spots. By periodically reporting the findings of their searches, the pursuing team hopes to update the information and maximize the accuracy of future evaluations.

The algorithm includes two major stages: the investigation stage (Phase A) and the pursuit stage (Phase B). The investigator team runs Phase A and Phase B by the police team. In Phase A, \({X_{{A_i}}}\) shows the i th suspected location, \(i=1,2,...,N_A\) . \(N_A\) indicates the number of the suspected locations to be investigated. In Phase B, \({X_{{B_i}}}\) shows the i th suspected location, \(i=1,2,...,N_B\) . \(N_B\) indicates the number of the suspected locations to be investigated. Here, \(N_A\) and \(N_B\) equal N , such that N shows the population size. Since the forensic investigation is a cyclical process, the process ends when the current iteration count ( t ) reaches the maximum iteration count ( t Max).

Stage A1 The interpretation of the findings portion of the forensic investigation process corresponds to Stage A1. The team analyzes the data and pinpoints any suspect spots. Every conceivable suspect location is investigated in light of other discoveries. First, a new suspicious location named \({X_{{A1_i}}}\) is extracted from \({X_{{A_i}}}\) based on information about \({X_{{A_i}}}\) and other suspicious locations. The general formula of the movement for this study, in which each individual is assumed to act under the influence of other individuals, is as Eq. ( 1 ):

where Dim is the problem size (dimension), R corresponds to a random number in the range [0,1], \({a_1}\) shows the number of individuals which affect the movement of \({X_{{A_{ij}}}}\) and \({a_1}\) \(\in \) \(\{ 1,2,...,n - 1\}\) . Here, as a result of trial and error tests, the best and shortest convergence is observed if the value of \({a_1}\) is 2. Accordingly, the new suspect location \({X_{{A_{i}}}}\) is revised as in Eq. ( 2 ).

where k , h , and i indexes correspond to three suspected locations and \(\{k,h,i\}\) \(\in \) \(\{ 1,2,...,N\}\) . k and h are randomly chosen numbers. N shows the population size and also the number of suspected locations, where Dim is the problem size (dimension), and \(R_1\) is a random number in the range [0,1]. Therefore, the expressions \(({R_1} - 0.5) * 2) \) and \(({R_1} - 0.5) * 2)\) represent the range [−1,1].

Stage A2 corresponds to the direction of the inquiry phase. Investigators compare the probability of each suspicious location with that of the others to determine the most likely suspicious location. The probability of each location is estimated using \(P({X_{{A_i}}})\) , Eq. ( 3 ) and a high \(P({X_{{A_i}}})\) value means a high probability for the location.

where pW is worst (lowest) possibility and pB is the best (highest) possibility. \({p_{{A_i}}}\) indicates the possibility of i th location.

Updating a search location is influenced by the directions of other suspected locations. Instead of updating all directions, randomly selected directions in the updated location are changed. In this stage, the movement of \({X_{{A_i}}}\) depends on the best individual and other random individuals. Like Stage A1, the general formula for motion is in Eq. ( 4 ).

Here, \({X_\textrm{best}}\) represents the best location; \({a_2}\) are number of individuals which affect \({X_{A{2_i}}}\) and \({a_2}\) \(\in \) \(\{ 1,2,...,n - 1\}\) ; c is the effectiveness coefficient of the remaining individuals and c \(\in \) \(\left[ { - 1,1} \right] \) . \({a_c}=3\) has been taken in the experiments. Thus, Eq. ( 5 ) obtains the new suspect position.

where \(R_5\) is the random number in the range [0,1]; and p,q,r, and i are four suspected locations selected 1,..,N. p, q, and r are randomly chosen, and \(j=1,2,...,\) Dim.

Stage B1 can be expressed as the "action" phase. Once the best location information has been received from the investigative team, all agents in the pursuit team must approach the target in a coordinated manner to arrest the suspect. Each agent ( \(B_i\) ) approaches the position with the best probability according to Eq. ( 6 ). An update is made if the newly approached site generates a higher probability than the previous location.

\({R_6}\) and \({R_7}\) are the random numbers in the range [0,1].

Stage B2 is the stage in which the process of "actions" is expanded. Locations are updated according to the probabilities of new locations reported to the headquarters by the police agents in case of any movement. The headquarters commands the tracking team to approach this location. In the process, agent \(B_i\) moves toward the best position, and agent \(B_i\) is influenced by another team member ( \(B_R\) ). Agent \(B_i\) ’s new position is calculated as in Eq. ( 7 ). If the probability of \(B_R\) is better than the probability of \(B_i\) ; otherwise, it is formalized as Eq. ( 8 ). The new-found location is updated if it is more probable than the old one.

Here, \({R_8}\) , \({R_9}\) , \({R_10}\) and \({R_11}\) are the random numbers; R and i represent the two police agents, and they are selected from 1, ...,  N . R is selected randomly in this group.

The optimum location for the suspect will be advised to the investigation team by the pursuit team. They perform this to help them increase the accuracy of their analysis and evaluation. Forensic investigative procedures might repeat themselves. The operating steps of the FBI algorithm are summarized in Fig.  2 .

The flowchart of FBI algorithm

2.2 Dynamic oppositional based learning

Xu et al. [ 33 ] have proposed a method that overcomes the difficulties of opposition-based learning (OBL), quasi-reflection-based learning methods (QRBL), and quasi-opposite-based learning methods (QOBL) and named as dynamic-opposite learning (DOL). QOBL proposed by Rahnamayan et al. [ 34 ], one of the variants of oppositional based learning (OBL), aims to increase the chance of approaching the solution by using quasi-opposite numbers instead of opposite numbers. According to the probability theorem, randomly initialized candidate solutions are further away from the global solution than the opposite prediction. Therefore, opposite numbers can effectively reduce the search space area and increase the convergence speed. The quasi-opposite number is formed from the interval between the median and the opposite number of the current population [ 35 ]. (QRBL) is proposed by Ergezer et al. [ 36 ] to extend the search space between current and central locations. However, these OBL approaches will miss the local optimal point if there is one between the current and opposite values. Thus, a system that dynamically broadens the search space should be considered in this situation. In this instance, DOL prevails. First and foremost, DOL needs to define the opposing point and opposite number. The mathematical expression of DOL is as in Eqs. ( 9 ), ( 10 ).

where lb and ub represent the lower and upper bounds, rand is a random number in the range of [0,1], \(X_i\) refers to a real number used as agent positions in between [ lb ,  ub ], NP denotes the population size, and i is the current agent selected from [1,  NP ]. \({OP_i}\) is obtained based on OBL and \({DO_i}\) corresponds to dynamic opposition number.

3 The proposed DOLFBI algorithm

It has been mentioned in Sect.  2.1.2 that the original FBI algorithm consisted of two main phases: the investigation phase (Phase A) and the pursuit phase (Phase B). In Stage A1, the investigation process is followed by detecting suspicious locations. In Stage A2, on the other hand, the location with the highest probability and the current best location is determined by calculating the suspicion probability of each location. By applying DOL over the best available position \({X{A_2}}\) (from Eq. ( 5 )), the position update for Phase A is performed in the next iteration.

Similar situations exist for Stage B. In Stage B1, also called the action phase, the best location information from the exploration team is received, and the tracking team approaches this location in a coordinated manner. Then, in Stage B2, where the action has been expanded, in case of any movement, the locations are updated according to the probabilities of the new locations reported to the headquarters by the police teams, and headquarters orders the monitoring team to approach this location. If the probability is higher than the old location, the current location becomes the new location.

The pursuit team updates the location with DOL and sends it back to Stage A before informing the investigative team about the suspect’s best location. The process continues in this way until it produces the best result. Since the algorithm has two different population sets (A and B), DOL is applied to both population groups. The algorithm of the developed DOLFBI is included in Algorithms  1 ,  2 , and 3 .

Pseudocode of Investigation Team Process

Pseudocode of Pursuit Team Process

Pseudocode of dynamic oppositional based learning

Pseudocode of DOLFBI

4 Simulation results

The flow in this section is given as follows: First, the values of the specific parameters used in the proposed and compared traditional and advanced methods are expressed. The algorithms are applied to the CEC2019 and CEC2022 test data within these parameters. As a result, the best and average outcomes are tabulated. The performance of the proposed method for known engineering challenges is then compared. Similarly, the proposed strategy is studied for 20, 24, and 72-truss optimization issues. The acquired results are validated using the Wilcoxon sign and Friedman rank tests.

4.1 Parameter settings

The population number and maximum iteration parameters, determined to be 30 and 5000, respectively, have been crucial parameters impacting the original FBI. A DOL strategy is being used to improve the procedure. The jumping rate (Jr) parameter, which is set to 0.25, affects DOL. These criteria serve as the foundation for all comparisons done within the framework of experimental studies. Table 1 shows the parameter settings for the traditional and enhanced methods utilized in the comparison.

4.2 Benchmark test suites

CEC2019 and CEC2022 were employed as benchmarks in this study. The CEC2019 test functions comprise ten multimodal functions listed in Table 22 . The first three CEC2019 functions have 9, 16, and 18 dimensions. Other CEC2019 functions have a dimension of 10. All of the global minimum values converge to 1. The benchmark functions in CEC2022 are unimodal, basic, hybrid, and composition. These are all minimization problems. Table 23 gives their comprehensive descriptions and specifications. The first five functions are shifted and rotated functions [ 37 ]. F11 is unimodal, which means it has a single minimum point. F12–F15 are multimodal, with multiple local minimum points. F16–F18 are hybrid functions developed by combining distinct functions. F16, for example, is derived from the functions of Bent Cigar, HGBat, and Rastrigin. F19 through F22 are composition functions. All functions are tested in the [ \(-100\) ,100] range, each with a global minimum value.

First, Tables 2 and 3 show the comparison results for the CEC2019 benchmark set. As a result, the optimum convergence for the F2, F3, F4, F5, F6, F7, and F8 problems is found using DOLFBI using both the best value and the mean value. Most of the compared methods for the F1 problem, including DOLFBI, converge to the global optimum value of 1, and this convergence value is reached on average; however, when analyzed in terms of standard deviation, SMA, HGS, and AVOA provide full convergence for all runs.

Tables 4 and 5 show the findings achieved compared to the improved approaches. Although the analyses produced similar results for DOLFBI, DOLFBI for F10 converged better than the advanced approaches this time. Except for F9 and F10, DOLFBI is a success for the CEC2019 benchmark set based on the ten challenges (Tables 6 and 7 ).

The CEC2022 set contains 12 problems. Traditional and upgraded approaches used in 2019 are being investigated for 2022. The convergence of DOLFBI is thriving, according to the findings of F11–F20 (for the first 11 problems) in Tables 8 and 9 . Although MFO appears to get the best convergence for F22, it attained this convergence value in DOLFBI but fell below MFO in standard deviation.

Convergence behaviors for both benchmark sets are plotted in Figs.  7 and 8 using the average convergence curve from 30 runs. From this, it is concluded that the drawings agree with the result tables. Here is the DOLFBI curve plotted in red and dotted; only the relationship to conventional methods is considered. The first 1000 iterations are plotted to show the convergence behavior clearly. If it is generally interpreted, it can be deduced that many methods converge to the optimum value for F1. It is possible to observe this similar behavior for F2, F12, F18, F20, and F22. The problems where early convergence is most prominent are F3, F4, F6, F8, F10, and F17. Although the global optimum value of the F10 benchmark function is 1, it usually converges to about 20. The successful convergence in the F10 function determines how many times it converges to 1 in 30 different convergence performances. Thus, it can be seen that DOLFBI often converges to 1 and, therefore, performs better on average than other methods. Considering Figs.  7 and 8 , CEC2019 problems are more challenging than CEC2022.

DOLFBI’s trajectory analysis is in Figs.  3 and 4 . This analysis is performed in 2000 iterations and 100 populations. The first column shows the positions of all individuals in the population at the end of 2000 iterations for dimensions x1 and x2 only. The red dot indicates the global minimum point, while the black dots represent the candidate solutions. It can be seen that at the end of the iteration, the candidate solutions are concentrated around the global optimum. The second column denotes the trajectory for the first dimension. Although the algorithm oscillates sharply at the beginning of the iterations, it converges to the optimum position at the end. The third column represents the average fitness value over 30 different runs, while the fourth column shows the convergence curve. According to the detailed trajectory analysis, it can be said that DOLFBI exhibits a consistent and robust convergence throughout the iterations.

Trajectory analysis of the DOLFBI

Trajectory analysis of the DOLFBI (Cont)

Box plots visualize statistical measures such as standard deviation, mean, minimum maximum, and quartile. Therefore, box-plot analysis is performed between DOLFBI and the compared algorithms. Box-plot analyses are visualized in Figs.  5 and 6 . Generally speaking, it can be seen that DOLFBI converges better with lower standard deviations except for F6, F8, and F18. Some algorithms produce extreme values in the F3, F9, F15, F18, F19, F20, and F21 benchmark functions in 30 runs. DOLFBI achieves an efficient convergence in these functions with a low standard deviation.

Box-plot analysis of the compared algorithms

Box-plot analysis of the compared algorithms (Cont)

The proposed DOLFBI scans the search space utilizing a dynamic oppositional learning strategy, which makes it superior to other compared approaches. This method uses opposite numbers. According to this strategy, opposite numbers play a major role in the generation-to-generation transfer of promising populations. The layout of the FBI algorithm considers two significant population groups (investigation and pursuit teams). Consequently, the DOL’s impact on the FBI became more noticeable Figs. 7 and 8 .

Convergence analysis of the compared algorithms (Cont)

4.3 Engineering problems

This section presents the engineering design problems to which the proposed method is applied and their comparative results. Each problem has its parameters and constraints. Accordingly, the most optimal values and best cost values of these parameters are emphasized for each problem.

ENG1: This problem involves finding the optimal parameters of a cantilever beam. It has one constraint and five variables. Five variables represent five block lengths. Figure  9 a shows the cantilever beam structure. x 1,  x 2,  x 3,  x 4, and x 5 are the height and width values of the square structure.

ENG2: This problem deals with minimizing the pressure vessel design. The problem has four constraints and four variables. Figure  9 b shows the pressure vessel structure. \(T_s\) (thickness of the shell), \(T_h\) (the thickness of the head), R (inner radius), and L (length of the cylindrical section) are the variables of the problem.

ENG3: Tension/compression aims to minimize the weight of a tension/compression spring while adhering to constraints on shear stress, surge frequency, and minimum deflection [ 38 ]. In Fig.  9 c, d represents the wire diameter, D mean coil diameter, and P number of active coils.

ENG4: This engineering problem involves improving the performance and efficiency of a speed reducer or gearbox [ 39 ]. The structure of the speed reducer is given in Fig.  9 d. The variables that need to be optimized are face width \((x_1)\) , module of gear \((x_2)\) , count of gear in the pinion \((x_3)\) , first shaft’s length between bearings \((x_4)\) , second shaft length between bearings \((x_5)\) , first shaft’s diameter \((x_6)\) , and second shaft’s diameter \((x_7)\) .

ENG5: The crashworthiness design problem aims to minimize the vehicle’s weight to enhance its ability to protect occupants during a collision [ 40 ]. The crashworthiness structure is given in Fig.  9 e. The variables of this design are expressed as thicknesses of B-Pillar inner, B-Pillar reinforcement, floor side inner, cross-members, door beam, door beltline reinforcement, and roof rail (x1-x7), materials of B-Pillar inner and floor side inner (x8 and x9), and barrier height and hitting position (x10 and x11).

ENG6: The beam will be optimized to achieve minimum cost by varying the weld and member dimensions. The problem’s constraints include limits on shear stress, bending stress, buckling load, and end deflection. [ 41 ]. Welded beam design consists of four variables: the thickness of weld ( h ), the length of the clamped bar ( l ), the height of the bar ( t ), and the thickness of the bar ( b ). The structure of the welded beam is shown in Fig.  9 f.

• Engineering problems

Here, the convergence performance of DOLFBI for the six engineering problems mentioned above is investigated. It is also interpreted by making comparisons with other pioneering and new metaheuristics. First, the ENG1 (cantilever beam design) results are in Table 10 . Accordingly, it can be interpreted that the DOL approach improves FBI, and DOLFBI converges better than other metaheuristics. DOLFBI converges to the smallest (good) value 1.301205963 with [5.951125854, 4.874066596, 4.464381903, 3.478196725, 2.138494389] ideal parameters [ 42 , 43 , 44 ]. Second, the ENG2 (pressure vessel design) real-world problem is reported in Table 11 . Here it can be seen that DOLFBI lags behind FBI by one thousandth, but comes significantly closer compared to other methods. The best cost value of DOLFBI is obtained as 5885.333014, while the ideal parameters calculated are [0.7782, 0.3846, 40.3196, 199.9999] [ 45 ]. This method is followed most closely by GWO with 5887.323 value. The third problem, ENG3 (tension/compression spring design), is detailed in Table 12 . DOLFBI is the method that gives the best convergence value 0.012665965, followed by HS after FBI. The best cost value of DOLFBI is obtained with the ideal parameters [0.051764678, 0.358539609, 11.18294909]. The fourth problem, ENG4 (speed reducer) result, is reported in Table 13 . Here, DOLFBI and FBI have the same convergence performance with the best cost value of 2993.761765. The GOA follows this result with 2994.4245. The fifth is ENG5 (crashworthiness problem), which is more challenging than the others because it is a problem with many parameters and constraints. According to the results in Table 14 , it can be interpreted that DOLFBI outperformed FBI, and that the DOL approach improved and improved the FBI significantly but still lagged behind SMO and LIACOR by one thousandth. The best cost value is 22.84298 with SMO, while it is 22.84300988 when DOLFBI is used. Finally, for ENG6 (welded beam design), DOLFBI has lagged behind RSA alone. However, it is seen that it gives more effective results than other compared methods (Table 15 ).

When interpreted in general, it would not be wrong to comment that the DOL approach improves the FBI, although DOLFBI falls in the second and third places in the literature for some challenging problems.

4.4 Truss topology optimization

The arrangement and layout of beam members in a structural system are addressed by structural truss topology. A truss is a frame of triangularly interconnected pieces (such as beams, bars, or rods). The cage’s triangular design provides stability, strength, and stiffness. Truss elements can be assembled in various configurations to meet specific design and engineering needs. The optimization of 20, 24, and 72-bar truss systems is examined in this work. The subheadings provide details on each topology (Table 16 ).

20-Bar Truss Problem : This structural problem has nine nodes leading to 14 degrees of freedom and is shown in Fig.  10 . It is given as a benchmark problem by Kaveh and Zolghadr [ 46 ] and Tejani et al. [ 47 ]. The design parameters and constraints of the issues are given in Table 16 .

Ground topology structure of 20-truss problem

24-Bar Truss Problem : The second structural problem is 24-bar truss and shown in Fig.  11 and the constraints are given in Table 16 .

Ground topology structure of 24-truss problem

72-Bar Truss Problem : The last truss problem is 72-bar truss has been previously used by Mohan et al. [ 48 ]. It has been split into and the constraint size is 198 and these are given in Table 16 .

In the 72-bar problem, the elements are clustered in 16 groups. These are C1 (A1−A4), C2 (A5−A12), C3 (A13−A16), C4 (A17−A18), C5 (A19−A22), C6 (A23−A30), C7 (A31−A34), C8 (A35−A36), C9 (A37−A40), C10 (A41−A48), C11 (A49−A52), C12 (A53−A54), C13 (A55−A58), C14 (A59−A66), C15 (A67−A70), and C16 (A71−A72). As it can be seen in Table 12 , some clusters (C3 and C16) have been removed for all algorithms and also removed from the Fig. 12 .

Ground topology structure of 72-truss problem

Table 17 compares DOLFBI with the methods in the literature for 24-bar truss topology optimization. Elements less than zero are not taken into account. In addition, the extracted element in all compared methods is not included in the table, for example, 4, 5, 11, 17, 18. Considering the efficient metaheuristic methods in the literature, DOLFBI ranks third. ITLBO takes first place with a minimum weight of 120.0798. However, it can be said that DOLFBI has a competitive convergence behavior. 20-bar truss topology optimization results are reported in Table 18 . In this problem, DOLFBI and ITLBO converge to the same and lowest weight values as 154.7988. In this problem, where there are 20 total sections, eight are selected as 1, 5, 8, 11, 13, 15, 18, and 20. Table 19 gives the optimum sections and weights of the 72-bar truss structure. 72-bar truss has a structure that can converge to the optimum weight with few elements. After IOWA, DOLFBI is in second place with 450.388 weight value. The optimum topology of 20 and 24-bar truss problems are given in Fig.  13 .

20 and 24-truss topology optimization with DOLFBI

4.5 Statistical tests

In this study, Wilcoxon sign rank (WSR) and Friedman rank statistical tests are used to show the effectiveness and difference of the proposed method. These two tests are nonparametric statistical tests. The Wilcoxon paired-pairs test is a nonparametric hypothesis test that compares the median of two paired groups and determines whether they are the same distributed [ 49 ]. Thus, WSR shows whether there is a significant difference between any two metaheuristic algorithms. In this study, a \(5\%\) significance level research is carried out. Friedman rank test determines a rank value for the proposed algorithm [ 50 ] (Tables 17 , 18 , 19 ).

Table 20 tabulates the WSR results. The p value between DOLFBI and alternative metaheuristic algorithms is displayed in this table. The h value between DOLFBI and methods is 0 for other functions except for FBI in F8 and HBA in F3. This means that DOLFBI differs significantly from the methods compared in the literature. Table 21 reports the Friedman results. According to the Friedman rank results, HGS in F1, HGS, HBA, and AVOA in F19 have the first rank. Considering the function-based rank average of all algorithms, DOLFBI is in the first rank, and then the FBI is in the second rank. In the last ranks, there are SCA and SMA. In CEC2019 and CEC2022 functions, it is clear that DOLFBI precedes other algorithms and exhibits competitive convergence.

4.6 Discussion

The study integrated dynamic oppositional based learning (DOL), an effective population determination method, into one of the newly proposed algorithms, the forensic-based investigation algorithm (FBI). In this case, a more successful convergence was sought by employing DOL, which picks individuals using the opposite approach rather than the original FBI’s random population selection algorithm. With the proposed study, it appears conceivable to improve the convergence outcomes of metaheuristic algorithms, which are regarded to be weak, particularly during the population selection stage.

The paper’s proposed method is compared to basic and enhanced metaheuristic methods. Basic approaches lack the search space’s contrasting strategy. Selecting opposite solution possibilities from the search space reduces the probability of becoming stuck in the local optimum. The enhanced approaches, on the other hand, were likewise developed using the combined opposite selection strategy. However, the FBI exhibits an excellent convergence performance due to the exploitation and exploration capabilities of the Investigation and Pursuit stages.

The impact and contribution of the DOL are evident numerically in the results, especially in the FBI, because two critical population groups are obtained. Additionally, DOLFBI, an enhanced version of FBI, is expected to yield promising results in numerous engineering or challenging real-world scenarios, feature selection with its binary form, and various optimization problems.

5 Conclusions

The DOLFBI presented in this study was created by combining the DOL paradigm with the FBI algorithm. The DOL paradigm, a variant of OBL, promotes efficiency by generating opposing populations and finding the global optimum as quickly and correctly as possible. DOL adaptively and dynamically determines randomly selected populations in traditional FBI for DOLFBI. Unlike primary and complex metaheuristic algorithms, DOLFBI outperforms or lags behind FBI and other algorithms. Twenty-two challenging benchmark problems from CEC2019 and CEC2022 are compared using traditional and sophisticated methodologies.

Compared to standard algorithms, DOLFBI converges to a lower fitness value than other approaches in 18 of 22 benchmark problems (excluding F1, F9, F10, and F22). According to the findings of a comparison with other OBL-based enhanced MAs in the literature, DOLFBI achieved the best convergence for 19 problems except F1, F9, and F22. The Wilcoxon sign and Friedman rank tests validate the results and statistical tests for DOLFBI. Only FBI in the F8 function and \(h=0\) in the F3 function in HBA are generated in WSR; in all other circumstances, \(h=1\) is generated. According to the Friedman test, DOLFBI ranks top in the CEC2019 and CEC2022 comparison problems. As a result, F4, F10, F14, and F17 demonstrate early convergence behavior. This study also includes a trajectory and qualitative analysis for DOLFBI. The trajectory analysis shows that the oscillation of the first dimension toward the last iteration is fixed and approaches an optimum.

DOLFBI has the best convergence in cantilever beam design, speed reducer, and tension/compression problems in terms of engineering challenges. It is ranked second for welded beam design, third for pressure vessel design, and fourth for crashworthiness.

DOLFBI performs successfully not only in mathematical problems but also in real-world problems. Based on an analysis of the tabulated findings, trajectory analyses, convergence curves, and box-whisker plots, it is evident that DOL yields encouraging outcomes for several FBI improvement issues.

Finally, DOLFBI is employed in the design of 20-, 24-, and 72-bar truss topology optimization challenges. As a result, it is compared to other methods in the literature. The results show that it comes in second position behind TLBO, with an optimal weight of 122.057 at 24-bar. They are on par with ITLBO and in first place with a weight value of 154.7988 at 20 bar. Finally, it ranks second after IWOA for the 72-truss issue, with an optimum value of 450.388.

A binary version of the proposed method can be created and used as a feature selection method in future investigations. Furthermore, the suggested method is adaptable to neural networks, extreme learning machines, and deep learning architectures.

Although dynamic OBL enhances convergence performance, the running time for exploring opposing regions can be computed as O(dim*N). In addition, although this change in running time causes DOLFBI to run slower, this does not cause an extra burden in algorithm complexity. DOLFBI, on the other hand, calls the objective function one last time to maintain the existing optimum value. As a result, the overall number of called functions grows.

Data availability

Source codes used in analyzing the datasets are available from the corresponding author upon reasonable request.

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Kutlu Onay, F. Solution of engineering design and truss topology problems with improved forensic-based investigation algorithm based on dynamic oppositional based learning. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09737-4

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Received : 06 October 2023

Accepted : 25 March 2024

Published : 02 May 2024

DOI : https://doi.org/10.1007/s00521-024-09737-4

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