doe tools for problem solving

Design of Experiments (DoE) for Engineers PD530932

Topics: Quality, Safety & Maintenance Product development , Manufacturing processes , Design Engineering and Styling

How do you determine the root cause of a problem or identify which variable settings will make the product or process more "robust"? What if you need to gain a better understanding of a complicated system? Can you identify which variables most affect performance and obtain a well-correlated regression equation that explains how those selected system variables and their interactions affect performance?  

Design of Experiments (DOE) is an excellent, statistically based tool used to address and solve these questions in the quickest, least expensive, and most efficient means possible. It's a methodology that includes steps for identifying system variables worthy of study and the ideal experiment type to execute; for setting up an organized, efficient series of tests involving various combinations of selected variables; and for statistically analyzing the collected data to help obtain definitive answers to these problem-solving and optimization challenges. 

DOE is a methodology that includes steps for identifying system variables worthy of study and the ideal experiment type to execute; for setting up an organized, efficient series of tests involving various combinations of selected variables; and for statistically analyzing the collected data to help obtain definitive answers to these problem-solving and optimization challenges. 

This eLearning course utilizes a blend of text, videos, and hands-on activities to help you gain proficiency in executing designed experiments. It explains the pre-work required prior to DOE execution, how to select the appropriate designed experiment to run, and choosing the appropriate factors and their levels. You'll also learn how to execute the experimental tests ("runs") and analyze/interpret the results with the benefit of computer software tools, such as Minitab. 

You'll set up, run, and analyze simple-to-intermediate complexity Full Factorial, Partial Factorial, Taguchi/Robust, and Response Surface experiments both by hand and using computer software. You'll also receive an overview of Mixture experiments and information on how to install and configure a fully functional 30-day trial version of Minitab for completing practice activities and for personal evaluation. You'll gain the most value from this course by running experiments through various class exercises, with answers discussed after you've had the opportunity to execute the DOE on your own.

By participating in this on-demand course, you'll be able to:

• Determine when DOE is the correct tool to solve a given problem or issue
• Select the appropriate DOE experiment type (DOE goal) for a given application
• Set up simple Full Factorial DOEs by hand using cube plots
• Set up and analyze any Full Factorial DOE using Minitab®
• Identify appropriate Partial Factorial design(s) based on one's application
• Set up and analyze Partial Factorial DOEs, simple Robust Design (Taguchi) DOEs, and simple Response Surface DOEs using Minitab®
• Recognize the structured process steps recommended when executing a DOE project

Materials Provided

• 90 days of online  single-user  access  (from date of purchase)  to the seven and a half hour presentation
• Integrated knowledge checks to reinforce key concepts
• Online learning assessment (submit to SAE)
• Glossary of key terms
• Job aids (included in each module of published course)
• Instructions on how to access a 30-day trial of Minitab ®
• Video demonstrations of exercise solutions using Minitab ®
• Follow-up to your content questions
• 1.0 CEUs*/Certificate of Achievement (upon completion of all course content and a score of 70% or higher on the learning assessment)

*SAE International is authorized by  IACET  to offer CEUs for this course.

Is this On Demand Course for You?

This course will benefit engineers involved in problem-solving, such as product design or product formulation (e.g., fluid/material composition, prepared food recipes/preparation, etc.) and/or optimization; process design and/or optimization; quality improvement efforts, such as defect elimination, warranty avoidance or similar initiatives; test engineers who wish to maximize learning of system behavior with a minimum number of tests; and technicians, analysts, and managers who support engineers in the above efforts, so they may be effective participants in DOE activities.

Testimonial

"DOE expertise is a must have for engineers who deal with data all the time, whether it's in a simulation or test, or identifying the factors which have the most influence on the experiment." Raj Chandramohanan Sr. Project Engineer Borg Warner Inc.

"This course helped me to develop a good understanding of the DOE method and to apply it to real-world applications." Usman Asad Senior Research Associate University of Windsor

"Very insightful; it definitely helped me understand the different applications/uses of the DOE techniques." Alberto Aguilar Lead Engineer, EGR system PV&V John Deere Power Systems

For More Details

Email [email protected] , or call 1-877-606-7323 (U.S. and Canada) or 724-776-4970 (outside US and Canada).

"There are no specific course prerequisites; however, participants are expected to have some math background, including the ability to calculate elementary statistics parameters, such as an average and a range. Since the course includes demonstration and hands-on use of Minitab®, participants should have some familiarity with Windows-based personal computer applications. 

Design of Experiments (DOE)

– Design of Experiments –

⇓   Introduction to DOE

⇓   What is DOE

⇓   Why Utilize DOE

⇓   How to Perform a DOE

⇓   DOE Services

Introduction to Design of Experiments (DOE)

If you look at many industries today you see similar products being offered by multiple manufacturers. Many companies today are frequently re-designing their products in an attempt to make their product stand out from the crowd. In addition, a great number of manufacturers are constantly developing new products to gain a foothold in other markets. With new products come new or changed processes. Every time we change a design or process we introduce new content. The amount of new content can be equated to the level of risk in the design or process. Product validation testing and prototype production runs are effective, but costly and in many cases problems are detected late in the development process. Engineers must use various analysis tools and statistical methods to reduce risk in a design or process. They must evaluate every change and how it could affect the process output. If you have multiple changes occurring at one time you could be multiplying your risk. So what can be done to predict how a set of changes will likely affect the process output? Design of Experiments (DOE) is a statistical tool available to engineers that can be used to evaluate single changes or multiple changes to a process at once and predict the resulting change to the output of the process.

What is Design of Experiments (DOE)

Design of Experiments (DOE) is a branch of applied statistics focused on using the scientific method for planning, conducting, analyzing and interpreting data from controlled tests or experiments. DOE is a mathematical methodology used to effectively plan and conduct scientific studies that change input variables (X) together to reveal their effect on a given response or the output variable (Y). In plain, non-statistical language, the DOE allows you to evaluate multiple variables or inputs to a process or design, their interactions with each other and their impact on the output. In addition, if performed and analyzed properly you should be able to determine which variables have the most and least impact on the output. By knowing this you can design a product or process that meets or exceeds quality requirements and satisfies customer needs.

Why Utilize Design of Experiments (DOE)

DOE allows the experimenter to manipulate multiple inputs to determine their effect on the output of the experiment or process. By performing a multi-factorial or “full-factorial” experiment, DOE can reveal critical interactions that are often missed when performing a single or “fractional factorial” experiment. By properly utilizing DOE methodology, the number of trial builds or test runs can be greatly reduced. A robust Design of Experiments can save project time and uncover hidden issues in the process. The hidden issues are generally associated with the interactions of the various factors. In the end, teams will be able to identify which factors impact the process the most and which ones have the least influence on the process output.

When to Utilize Design of Experiments (DOE)

Experimental design or Design of Experiments can be used during a New Product / Process Introduction (NPI) project or during a Kaizen or process improvement exercise. DOE is generally used in two different stages of process improvement projects.

• During the “Analyze” phase of a project, DOE can be used to help identify the Root Cause of a problem. With DOE the team can examine the effects of the various inputs (X) on the output (Y). DOE enables the team to determine which of the Xs impact the Y and which one(s) have the most impact.
• During the “Improve” phase of a project, DOE can be used in the development of a predictive equation, enabling the performance of what-if analysis. The team can then test different ideas to assist in determining the optimum settings for the Xs to achieve the best Y output.

Some knowledge of statistical tools and experimental planning is required to fully understand DOE methodology. While there are several software programs available for DOE analysis, to properly apply DOE you need to possess an understanding of basic statistical concepts.

How to Perform a Design of Experiments (DOE)

A DOE generally consists of the following four main phases, detailed below.

The Experimental Plan

• Include all inputs and outputs of the process
• Define the problem or goal of the experimenter
• Clearly identify any specific questions that you need the experiment to answer
• List the known or expected sources of variability in the experimental units (X)
• Determine how to identify and block the uncontrollable inputs (S)
• Attribute measures (pass / fail) should be avoided
• Measurement Systems Analysis (MSA) should be performed (if not previously completed) on the selected measurement system
• The number of variables is up to the experimenter

Analyze the Results

After completing the experiment and collecting the data, the next step is to analyze the data and determine which input factors (X) or interactions (S) had the most impact on the process output (Y). By analyzing the data the experimenter can optimize the process by determining the combination of variables that produce the most desirable process output (Y).

Determine Appropriate Actions

Once the experiment is complete and the data is analyzed, actions must be identified to improve the process. The experimenter or team should determine any appropriate actions to be taken, assign an owner and a due date for each action.

DOE Example

The example described below is a simple experiment meant only to demonstrate the four steps of a basic Design of Experiments. Using DOE on your processes will most likely involve several input factors (X) and multiple interactions (S). Examining each factor individually would require a tremendous amount of time and resources. Using DOE enables the experimenter to examine multiple factors at once, including the effect of interactions between factors, reducing the required number of runs, thus saving time and valuable resources.

• Let’s say we want to evaluate the inputs of sunlight and water in relation to a plants growth. Use the formula 2n to determine how many tests need to be run. The letter n is equal to the number of factors being examined. For our two factor (sunlight and water) experiment, four runs are required. We will need to represent each factor at its highest and lowest points. The experiments need to be performed in a randomized fashion.
• Controllable Factor A: Water, Levels: 50 to 1.5 cups per day
• Controllable Factor B: Sunlight, Levels: 1 to 3 hours per day
• Uncontrollable Factors could include soil temperature
• For our experiment, the amount of sunlight per day had a greater effect on the plant growth and the effect of the interactions was insignificant

Design of Experiments (DOE) Services

DOE Services from Quality-One include DOE Consulting, DOE Training and DOE Project Support, such as Facilitation, Contract Services and Auditing. Our experienced team of highly trained professionals will provide a customized approach for developing your people and processes based on your unique needs. DOE Consulting will assist with the design of your DOE process, review your needs and available resources to properly plan and implement DOE at your facility. DOE Training will help team members understand and perform DOEs effectively and efficiently.  Hands-on DOE Project Support will help to build and deploy your DOE process throughout the entire organization. By utilizing our experienced Subject Matter Experts (SME) to work with your teams, Quality-One can help you optimize your processes with DOE methodology and promote continuous improvement thinking in your organization.

Learn More About Design of Experiments (DOE)

Quality-One offers Quality and Reliability Support for Product and Process Development through Consulting, Training and Project Support. Quality-One provides Knowledge, Guidance and Direction in Quality and Reliability activities, tailored to your unique wants, needs and desires. Let us help you Discover the Value of DOE Consulting, DOE Training or DOE Project Support.

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(248) 280-4800 | [email protected]

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Back to Course Overview

Design of Experiments

Design of experiments (DOE) is a rigorous methodology that enables scientists and engineers to study the relationship between multiple input variables, or factors , on key output variables, or responses .

In this module, you will learn why designed experiments are better than trial and error and one-factor-at-a-time approaches to gain an understanding of cause and effect relationships and interactions between factors. You will be introduced to several types of designs such as factorial, response surface and custom designs. Finally, you will learn some DOE guidelines and best practices which will help you succeed with experimentation.

Estimated time to complete this module: 3 to 4 hours

Design of Experiments Overview (1:01)

Specific topics covered in this module include:

Introduction to doe.

• What is DOE?
• Conducting Ad Hoc and One-Factor-at-a-Time (OFAT) Experiments
• Why Use DOE?
• Terminology of DOE
• Types of Experimental Designs

Factorial Experiments

• Designing Factorial Experiments
• Analyzing a Replicated Full Factorial
• Analyzing an Unreplicated Full Factorial

Screening Experiments

• Screening for Important Effects
• A Look at Fractional Factorial Designs
• Custom Screening Designs

Response Surface Experiments

• Introduction to Response Surface Designs
• Analyzing Response Surface Experiments
• Creating Custom Response Surface Designs
• Sequential Experimentation

DOE Guidelines

• Introduction to DOE Guidelines
• Defining the Problem and the Objectives
• Identifying the Responses
• Identifying the Factors and Factor Levels
• Identifying Restrictions and Constraints
• Preparing to Conduct the Experiment

Training + Implementation

design of experiment – DOE

DOE is a simple yet effective problem solving methodology. It can be applied to problem resolution & also to problem prevention. DOE supports quality improvement initiatives from primitive to world-class quality levels.

This training programme explains Shainin techniques of design of experiment in a simple step-by-step approach.

This training programme is designed to give a first-hand experience of use of design of experiment tools through simulated classroom exercises.

• Review of quality concepts
• Understand the concept behind design of experiment
• Become familiar with tools used in design of experiment
• Learn & practice design of experiment tools
• Why design of experiment?
• How to measure variation?
• Multivary charts
• Paired comparison
• Component search
• Variable search
• Full factorials
• Scatter plots
• Positrol, SPC, Pre-control

On attending this training programme, the participants will be able to relate to DOE concepts & tools with a view to using them for problem solving & variation reduction.

Who should attend?

• Middle Level
• Senior Level

Methodology

• Multimedia Presentation
• Concept explaination
• Facilitated Discussion
• Group exercise
• Individual exercise
• Study material

Interaction Language

What is the programme duration.

Want to customise the programme to your requirements?

DOE in Problem Solving

Locked lesson.

• Lesson resources Resources
• Quick reference Reference

About this lesson

Exercise files.

Download this lesson’s related exercise files.

Quick reference

In some instances, a DOE can help to identify root causes for a problem. If a problem solution requires a major redesign of a product, process or system, a DOE can be used to improve understanding of the critical control factors and to provide estimates of the level of improvement with the new solution.

When to use

DOEs can be used to identify contributing root causes when a problem is caused by common causes rather than a special cause. In that case the DOE is part of the investigation or measure phase of the project. DOE can also be used to aid in the development of a viable and effective solution when that solution requires a redesign. In that case, the DOE is part of the solution planning or improve phase of the project.

Instructions

Most problem solving projects will not need to do a DOE. The problem is based upon a special cause when that root cause is identified and eliminated or controlled, the problem goes away and normal operations are restored. However, some problem-solving projects may benefit from a DOE

When the root cause of the problem is the interaction of many normal factors in the business, a DOE can be used to create the design space equation for that system. The design space equation can then be solved for various circumstances and through that the combination of effects that create the problem can be discovered. In this case, it is usually best to do a full factorial DOE or at least a factorial with Resolution V or higher to ensure all the first order interaction effects are included. 

If the problem is in a manufacturing process and the concern is that the operator cannot control the process, a Taguchi DOE can analyze that through the inner and outer array. If the outer array – the Taguchi noise factors – are significant contributors to the problem performance, a system change is needed

When the problem solution is a major upgrade to an existing product, process or system, a DOE can help the team that is redesigning the system. At this point it is less about solving the problem and more about creating a new product, process or system. However, at least one of the response factors used needs to directly correlate with the problem so that it is clear that the solution is an improvement. The DOE at this time can generate data that illustrates the magnitude of the change. This data may be needed for a cost benefit analysis of the solution. I have found that using a DOE analysis at this point adds a significant level of confidence to the claims of improved performance due to the statistical rigor behind the DOE.

Hints & tips

• DOE is not well suited to identify complex special cause problems. First, the control factors must be included in the analysis, but you don’t know what the problem is so you don’t know what factor to include. While you could include lots of factors – the test matrix will quickly get large and using a fractional factorial approach will confound interaction effects even though those are likely the most significant. Finally, the DOE will probably use two-level factors which assume linear behavior, but it is likely that the factors are not behaving linearly, so their true behavior will not be correctly modelled.
• DOE is well suited to create the data that shows how the solution will improve the business situation. This will help the business case for the solution implementation.
• Make sure any DOE related to problem solving has response factor that directly correlate with the problem behavior.
• 00:04 Hi, I'm Ray Sheen.
• 00:06 A DOE can also be very helpful when working with a structured problem solving
• 00:10 process, like Lean Six Sigma.
• 00:14 Let's take a look at what DOE can do for us in this situation.
• 00:18 One thing is that the DOE design space equation and sensitivity studies with
• 00:23 multi-level factors can help to identify the limits for
• 00:25 control factors, beyond which the product, process, or
• 00:29 system starts to provide unacceptable performance.
• 00:33 Also, DOE, especially Taguchi DOEs, can identify acceptable levels for
• 00:38 manufacturing tolerance limits.
• 00:40 And don't forget, one of the big advantages of DOE over trial and error
• 00:44 and OFAAT, DOE can evaluate interaction effects between control factors,
• 00:49 the others cannot.
• 00:51 Finally, let's talk a little bit about the slope on the main effects plots and
• 00:55 one of the ways we can use those to our advantage.
• 00:59 A steep slope indicates that the factor
• 01:02 has a very large effect on the performance.
• 01:05 If I put that factor directly under the operator's control with the feedback
• 01:09 system so that they can see how the process is currently performing at a given
• 01:14 time, that control can be used as the primary process or system control.
• 01:19 This will greatly simplify the overall system control methodology.
• 01:23 While the items in the previous slide helps us to understand how to use
• 01:26 DOE to identify ways to control the process and
• 01:29 avoid problems, but what if the problem already exists?
• 01:34 if you already know the value of the response variable that is the problem,
• 01:38 the DOE design space equation can be used to isolate which factors or
• 01:43 interactions could create that value in the response variable.
• 01:47 And given the variability in the response factor, a Taguchi DOE can identify
• 01:52 what types of variances can be expected within the Taguchi noise factors.
• 01:57 This provides a reasonably good insight into process capability and
• 02:01 that variance can then be compared to the allowable specification and
• 02:04 tolerances to calculate the CP and CPK process capability indices.
• 02:11 The DOE design space equation can be used to predict response factor performance for
• 02:17 a variety of control factor settings.
• 02:20 This will indicate which factors have a positive impact,
• 02:23 which have a negative impact, and which have no impact.
• 02:27 Another role of a DOE in problem solving project situations
• 02:31 is the analysis of the proposed solution.
• 02:34 When the solution is a new system, or a significantly modified system,
• 02:38 there are often questions about the fragility of that system.
• 02:42 These are reasonable questions to ask,
• 02:44 since the old system was probably broken, that's why you're trying to fix it.
• 02:48 It's reasonable to ask what problems the new system will have.
• 02:52 The DOE design space equation can answer that question.
• 02:55 It will identify which factors are significant and
• 02:58 therefore need to be controlled.
• 03:00 And the equation can be used to do what if analysis to predict performance under
• 03:04 a variety of conditions.
• 03:06 All of these will serve to answer the question about what types of
• 03:09 problems the solution could have and
• 03:12 what actions should be taken to prevent problems occurring with the solution.
• 03:17 Another way in which I've used DOE on problem-solving projects
• 03:20 is to let the DOE design space equation predict the performance
• 03:25 once the improvement is in place.
• 03:27 The benefit from the improved performance level can be evaluated and
• 03:30 used in the cost benefit analysis that is presented for the different options.
• 03:35 This provides some scientific and statistical rigor into those benefits, and
• 03:40 not just wild guesses.
• 03:42 Through the use of the DOE, you can illustrate the improvement
• 03:45 by using the data from the refining and optimizing studies.
• 03:48 There's nothing more powerful to convince someone of the need for
• 03:52 change than an actual physical example that they can test with their own senses.
• 03:57 A DOE will identify the factor settings to create those examples and
• 04:01 it has the statistical rigor to assert that this is not just a lucky guess,
• 04:06 this now represents the new normal.
• 04:10 The value of using DOE in problem solving is that it gives us statistical confidence
• 04:16 in both problem isolation and the adequacy of the problem solution.

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Design of Experiments (DOE)

Uncover and quantify cause & effect relationships, design of experiments.

Learn the best ways to design trials and experiments to maximise discoveries and minimise effort. 

Design of Experiments - What will you learn?

Do you want to truly understand what drives the performance of your processes?

Do you want to optimise the output of your processes?

Design of Experiments (DOE) is a collection of techniques used to determine the relationship between the factors which influence a process and the output of that process.

DOE uncovers and quantifies the cause-and-effect relationships between process inputs and results.

Once these relationships are known, DOE is used to find the optimum working conditions for your processes and maximise your results.

This 2-day course covers the theory and practice of Design of Experiments.

This training course is designed for non-statisticians!

We cover many real-life case studies from both administrative and production processes .

Our emphasis is on giving you a thorough understanding of the techniques first, before showing you how to perform Design of Experiments with ease using our favoured platform, Minitab.

Following this course, you will immediately know how to set up and run Designed Experiments at your place of work. You will be able to interpret the results of your Designed Experiments and be in a position to implement improvements which optimise process results.

Course Format

The training is delivered via live Zoom / Teams sessions with email support where required.

You will be part of a small select cohort of participants.

A full set of colour printed notes will be provided for you.

A Training Completion Certificate is issued on completion of the module.

Join this course in the knowledge that if you wish to take your data analysis skills to the next level, you can build on this course with a range of other standalone modules. All of our Minitab modules are delivered as 2-day sessions.

Minitab is recognised as a leading platform in the field of data analysis. We use Minitab as our software of choice for DOE training.

Despite its enormous power, Minitab has a friendly user interface that makes it easy for complete beginners to get started and then move on to more sophisticated techniques.

We have been using Minitab for over 20 years in countless data analysis scenarios with more than 100 organisations. It remains our favourite route to understanding data, converting this to valuable information, and making the right business decisions as a result

Analysis of Process Variables - ANOVA - what we cover:

• The development of Design of Experiment techniques
• The sequential process of DOE
• The risks involved in traditional methods like “change one factor at a time”
• Full Factorial Designs
• Replication and Randomisation
• Main Effects and Interaction Effects
• Interpreting Minitab output
• Pareto and Normal Effects Plots
• Regression Analysis & Prediction
• Fractional Factorial Designs
• Screening Designs Plackett-Burman & Definitive Screening Designs
• Response Surface Methods
• Process Optimisation

Who should participate.

Suggested Material:

People who are interested in their own personal development and who would like to learn how make data work for them.

Anyone looking for career development via this certificated programme.

People who want to upskill their problem-solving capabilities and demonstrate what they can achieve within their organisation.

Anyone looking for their first formal training in data-based decision making.

Academic qualifications are not required, but reasonable numeracy and familiarity with using Excel will be useful.

Pricing 2-day Module

UK   £500.00 (plus VAT)

USD   $650.00

Euro  €650.00

If you would like to know more about Design Of Experiments (DOE) please fill in the form below and we'll be back in touch.

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What is the Design of Experiments Tool

• 2 mins to read
• October 14, 2022
• By Reagan Pannell

How to create efficient and accurate experiments with the DOE tool

As a Six Sigma professional or project manager, you may wonder what the Design of Experiments (DOE) tool is and how it can improve your process. The DOE tool is a statistical method that can be used to test multiple variables at once to determine which variable has the biggest impact on the output of a process. In this blog post, we will take a closer look at the DOE tool and how it can be used to improve your process.

What is the DOE Tool?

The DOE tool is a statistical method that can be used to test multiple variables at once to determine which variable has the most significant impact on the output of a process. This tool is often used in Six Sigma and Lean Six Sigma projects to optimize a process.

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The DOE tool works by varying one or more input factors while holding all other inputs constant. This allows you to isolate the effect of each input on the output of the process. For example, if you are trying to optimize a manufacturing process, you could vary the type of material, the speed of the production line, and the temperature. By doing this, you can determine which input has the biggest impact on the output of the process.

Benefits of Using DOE

Using DOE in your Six Sigma or Lean Six Sigma project has many benefits. Some of these benefits include:

– Reduced cost: By testing multiple variables at once, you can save time and money that would otherwise be spent on testing each variable individually.

– Increased accuracy: This method is more accurate than other methods because it eliminates human error. When testing multiple variables at once, it is easy to forget to change one of the variables or to not change all of them by the same amount. This can lead to inaccurate results.

– Increased understanding: This method can help you better understand how different inputs affect your process. This understanding can be used to optimize your process further.

– Improved communication : When using this method, it is important to communicate with your team so that everyone understands what is being tested and why. This helps ensure that everyone is on the same page and understands this method’s importance.

The design of experiments (DOE) tool is a statistical design methodology that can be used to optimize processes or products. DOE allows you to investigate the effect of different variables on a process or product and find the combination of variables that gives the best results.

Conclusion:

So, what is DOE? The Design of Experiments (DOE) tool is a statistical method that can be used to test multiple variables at once to determine which variable has the biggest impact on the output of a process. It is often used in Six Sigma and Lean Six Sigma projects to optimize a process and has many benefits, including reduced cost, increased accuracy, increased understanding, and improved communication. If you are looking for ways to improve your process, consider using DOE.

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Reagan pannell.

Reagan Pannell is a highly accomplished professional with 15 years of experience in building lean management programs for corporate companies. With his expertise in strategy execution, he has established himself as a trusted advisor for numerous organisations seeking to improve their operational efficiency.

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Published: November 7, 2018 by Ken Feldman

The fundamental premise of the Shainin Red X® process is that for any problem, there is a dominant root cause that must be eliminated or mitigated for the process to be improved. In this article, we will define the Red X process, best practices, and how it can be applied to your organization.

Overview: What is Red X? 

The Red X method is based on the key assumption there is always a dominant cause of variation. This statement is based on the application of the Pareto principle to the causes of the variation. Generally, the variation of the output is caused by the variation of several inputs. These inputs (Xs) are categorized by color, with the Red X being the dominant root cause. Shainin defines the desired state of the output as the GreenY®.

Red X Pareto Chart

Instead of the DMAIC methodology of Define-Measure-Analyze-Improve-Control, the Red X approach uses the following structure, called FACTUAL:

Shainin Red X FACTUAL approach to problem-solving

An industry example of Red X 

The problem was Post Burning Blow Holes on automotive batteries. The post burner is an automatic burning machine designed to weld the cylindrical bushing of an automotive battery to a specified depth of burn. If blow holes are seen on the battery post, then the battery is rejected.

Shainin Red X techniques were used to reduce the percentage of reworks from 0.15% to 0.03%. DOE was used as the primary tool along with:

• Multivari analysis
• Variable search
• Paired comparison 
• Component search 
• Product / process search
• Scatter plots

3 best practices when thinking about Red X 

Here are a few tips on using Red X in your organization. 

1. Deep understanding of the process

You must have a deep understanding of the Y and the problem. 

2. Problem solving strategy 

The Red X approach is very diagnostic in nature. It is the identification, analysis and quick zooming in on the root cause. 

3. Measurement System Analysis (MSA) 

You must have confidence in the quality of the data captured by your measurement system. This requires you to do a MSA study to validate your data. 

Frequently Asked Questions (FAQ) about Red X

1. what are some of the common tools used in red x .

Two of the most common tools used in Red X are the Solution Tree™ developed by Shainin, Pareto Chart and full factorial design of experiments . Other tools include the Shainin ISO plot, component search, paired comparison, multi-vari chart and BOB/WOW.

2. What do BOB and WOW mean in Red X?

One of the techniques used in Red X is to examine the extremes to try and identify a possible root cause. You will look at the Best of the Best (BOB) versus the Worst of the Worst (WOW).

3. Who developed the Red X approach? 

Dorian Shainin (1914-2000) was an American quality consultant, aeronautical engineer, and author primarily noted for his contribution in the field of problem-solving, specifically the creation and development of the Red X concept.

Red X in a nutshell

Red X is a problem-solving technique based on the premise there is one dominant root cause for process variation and problems. Using a convergent process of analysis focusing on potential process input variables (X), the end result will be an identification of the primary Red X root cause along with lesser colors of Pink Xs.

About the Author

Ken Feldman

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Nine essential problem solving tools: The ultimate guide to finding a solution

October 26, 2023 by MindManager Blog

Problem solving may unfold differently depending on the industry, or even the department you work in. However, most agree that before you can fix any issue, you need to be clear on what it is, why it’s happening, and what your ideal long-term solution will achieve.

Understanding both the nature and the cause of a problem is the only way to figure out which actions will help you resolve it.

Given that most problem-solving processes are part inspiration and part perspiration, you’ll be more successful if you can reach for a problem solving tool that facilitates collaboration, encourages creative thinking, and makes it easier to implement the fix you devise.

The problem solving tools include three unique categories: problem solving diagrams, problem solving mind maps, and problem solving software solutions.

They include:

• Fishbone diagrams
• Strategy maps
• Mental maps
• Concept maps
• Layered process audit software
• Charting software
• MindManager

In this article, we’ve put together a roundup of versatile problem solving tools and software to help you and your team map out and repair workplace issues as efficiently as possible.

Let’s get started!

Problem solving diagrams

Mapping your way out of a problem is the simplest way to see where you are, and where you need to end up.

Not only do visual problem maps let you plot the most efficient route from Point A (dysfunctional situation) to Point B (flawless process), problem mapping diagrams make it easier to see:

• The root cause of a dilemma.
• The steps, resources, and personnel associated with each possible solution.
• The least time-consuming, most cost-effective options.

A visual problem solving process help to solidify understanding. Furthermore, it’s a great way for you and your team to transform abstract ideas into a practical, reconstructive plan.

Here are three examples of common problem mapping diagrams you can try with your team:

1. Fishbone diagrams

Fishbone diagrams are a common problem solving tool so-named because, once complete, they resemble the skeleton of a fish.

With the possible root causes of an issue (the ribs) branching off from either side of a spine line attached to the head (the problem), dynamic fishbone diagrams let you:

• Lay out a related set of possible reasons for an existing problem
• Investigate each possibility by breaking it out into sub-causes
• See how contributing factors relate to one another

Fishbone diagrams are also known as cause and effect or Ishikawa diagrams.

2. Flowcharts

A flowchart is an easy-to-understand diagram with a variety of applications. But you can use it to outline and examine how the steps of a flawed process connect.

Made up of a few simple symbols linked with arrows indicating workflow direction, flowcharts clearly illustrate what happens at each stage of a process – and how each event impacts other events and decisions.

3. Strategy maps

Frequently used as a strategic planning tool, strategy maps also work well as problem mapping diagrams. Based on a hierarchal system, thoughts and ideas can be arranged on a single page to flesh out a potential resolution.

Once you’ve got a few tactics you feel are worth exploring as possible ways to overcome a challenge, a strategy map will help you establish the best route to your problem-solving goal.

Problem solving mind maps

Problem solving mind maps are especially valuable in visualization. Because they facilitate the brainstorming process that plays a key role in both root cause analysis and the identification of potential solutions, they help make problems more solvable.

Mind maps are diagrams that represent your thinking. Since many people struggle taking or working with hand-written or typed notes, mind maps were designed to let you lay out and structure your thoughts visually so you can play with ideas, concepts, and solutions the same way your brain does.

By starting with a single notion that branches out into greater detail, problem solving mind maps make it easy to:

• Explain unfamiliar problems or processes in less time
• Share and elaborate on novel ideas
• Achieve better group comprehension that can lead to more effective solutions

Mind maps are a valuable problem solving tool because they’re geared toward bringing out the flexible thinking that creative solutions require. Here are three types of problem solving mind maps you can use to facilitate the brainstorming process.

4. Mental maps

A mental map helps you get your thoughts about what might be causing a workplace issue out of your head and onto a shared digital space.

Because mental maps mirror the way our brains take in and analyze new information, using them to describe your theories visually will help you and your team work through and test those thought models.

5. Idea maps

Idea maps let you take advantage of a wide assortment of colors and images to lay down and organize your scattered thought process. Idea maps are ideal brainstorming tools because they allow you to present and explore ideas about the best way to solve a problem collaboratively, and with a shared sense of enthusiasm for outside-the-box thinking.

6. Concept maps

Concept maps are one of the best ways to shape your thoughts around a potential solution because they let you create interlinked, visual representations of intricate concepts.

By laying out your suggested problem-solving process digitally – and using lines to form and define relationship connections – your group will be able to see how each piece of the solution puzzle connects with another.

Problem solving software solutions

Problem solving software is the best way to take advantage of multiple problem solving tools in one platform. While some software programs are geared toward specific industries or processes – like manufacturing or customer relationship management, for example – others, like MindManager , are purpose-built to work across multiple trades, departments, and teams.

Here are three problem-solving software examples.

7. Layered process audit software

Layered process audits (LPAs) help companies oversee production processes and keep an eye on the cost and quality of the goods they create. Dedicated LPA software makes problem solving easier for manufacturers because it helps them see where costly leaks are occurring and allows all levels of management to get involved in repairing those leaks.

8. Charting software

Charting software comes in all shapes and sizes to fit a variety of business sectors. Pareto charts, for example, combine bar charts with line graphs so companies can compare different problems or contributing factors to determine their frequency, cost, and significance. Charting software is often used in marketing, where a variety of bar charts and X-Y axis diagrams make it possible to display and examine competitor profiles, customer segmentation, and sales trends.

9. MindManager

No matter where you work, or what your problem-solving role looks like, MindManager is a problem solving software that will make your team more productive in figuring out why a process, plan, or project isn’t working the way it should.

Once you know why an obstruction, shortfall, or difficulty exists, you can use MindManager’s wide range of brainstorming and problem mapping diagrams to:

• Find the most promising way to correct the situation
• Activate your chosen solution, and
• Conduct regular checks to make sure your repair work is sustainable

MindManager is the ultimate problem solving software.

Not only is it versatile enough to use as your go-to system for puzzling out all types of workplace problems, MindManager’s built-in forecasting tools, timeline charts, and warning indicators let you plan, implement, and monitor your solutions.

By allowing your group to work together more effectively to break down problems, uncover solutions, and rebuild processes and workflows, MindManager’s versatile collection of problem solving tools will help make everyone on your team a more efficient problem solver.

Download a free trial today to get started!

Ready to take the next step?

MindManager helps boost collaboration and productivity among remote and hybrid teams to achieve better results, faster.

Why choose MindManager?

MindManager® helps individuals, teams, and enterprises bring greater clarity and structure to plans, projects, and processes. It provides visual productivity tools and mind mapping software to help take you and your organization to where you want to be.

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Advances in Mechanical Engineering pp 247–256 Cite as

Root Cause Analysis by Shainin Approach in Automotive Manufacturing Sector: A Case Study

• Kartik Thakur 14 ,
• Akhilesh Kumar 14 ,
• Sushanta Jashwara 14 ,
• Kamaljeet Singh 14 &
• Kulwinder Singh 14  
• Conference paper
• First Online: 27 June 2021

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Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Six sigma methodologies are the most popular technique in manufacturing sector to improve the sigma level of quality. Shainin proposed various tools to resolve the quality issues under DMAIC approach. In this paper, product process search tool (PPS) is applied to find out the root cause of variation in the size of main bearing seat. Under this hypothesis test, it is proved that variation is the part of grinding operation rather than lapping operation. To control the variation, tolerance after grinding operation is redesigned and validated by applying B versus C tool. Rejection rate due to main bearing seat is reduced from 0.95 to 0.06 pph for main bearing undersize.

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Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Kartik Thakur, Akhilesh Kumar, Sushanta Jashwara, Kamaljeet Singh & Kulwinder Singh

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Gaurav Manik

Department of Chemistry, Army Cadet College, Indian Military Academy, Dehradun, Uttarakhand, India

Susheel Kalia

CSIR—National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram, Kerala, India

Sushanta Kumar Sahoo

School of Engineering and Technology, Shobhit University, Saharanpur, India

Tarun K. Sharma

Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

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Thakur, K., Kumar, A., Jashwara, S., Singh, K., Singh, K. (2021). Root Cause Analysis by Shainin Approach in Automotive Manufacturing Sector: A Case Study. In: Manik, G., Kalia, S., Sahoo, S.K., Sharma, T.K., Verma, O.P. (eds) Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-0942-8_23

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When NOT to use a DOE

Our video today is about when NOT to use a DOE. But before we talk about when NOT to use a DOE, let’s make sure you have a general idea of what a DOE is.

Video: DOE, Warning! When NOT to Use!

First off, what IS a DOE?

DOEs are awesome tools in the right time and place! If you’re not familiar with the term DOE, it stands for Design Of Experiment. It’s a special experiment designed to analyze variations that may affect your given condition. In our case, it’s typically to identify whether or not we’ve found the root cause of the problem.

In case that sounds complicated, let’s simplify it a bit. Let’s say you have a water leak around a screw in your roof. Sometimes it leaks, sometimes it doesn’t. Now you can run a DOE by screwing in a whole bunch of screws a few different ways until you figure out what the actual problem is. This is an example of a GREAT time to run a DOE!

You can find a lot of good information on the internet that’ll go into further detail on how to run a DOE, if that’s what you’re looking for. But for today I want to explain why I think DOEs are one of the most commonly misused tools in problem-solving!

Misuse of DOEs

There are 2 main reasons for this:

• Most people don’t realize that there’s usually only 1 main root cause to their problem. DOEs can be spectacular if your project actually does have more than 1 main root cause! But are you ready for a surprise? Even if you really do have more than 1 root cause, you can often confirm this without even using a DOE! (I’ll tell you how in just a minute!)
• A lot of people think there’s only 1 way to prove if you have the root cause or not: and that’s to run a DOE.

For DOE-lovers:

Maybe you love DOEs and you feel extra smart when you run them, but I’m about to give you some first-rate reasons to think twice next time before running your DOE.

• Proving your root cause without a DOE will probably be FASTER!!! When you have a big problem costing your company a lot of money, the faster you can prove your root cause and start implementing a solution, the better!
• Proving your root cause without a DOE is likely to save you money!!! DOEs can be very expensive!

So why would anyone in their right mind throw away a bunch of money running some slow, unnecessary tests when there’s often a faster, better way to get the job done without running a DOE?!

And that, my faithful readers, is why we are talking about: When NOT to run a DOE!

Here’s how you know when NOT to run a DOE:

Do not run a doe if you only have 1 root cause candidate.

Really people, this should almost go without saying. Why would anyone think it’s a good idea to run an experiment to analyze different variables… when you’ve already narrowed your relevant variables down to… ONE?

If you really only have 1 valid root cause candidate, just go ahead and make the change. Chart it over time. It is absolutely pointless to squander valuable time and money running a DOE when all you have is 1 potential root cause.

And what I never, ever want to see someone do is to add more possible root causes to a DOE than they actually need, just to justify running it! If 1 is all you need, then forget the DOE and move on!

DO NOT run a DOE if you can run a different type of test that might be more beneficial.

This video discusses some alternatives to running a DOE that can actually be more productive sometimes. For example, if you are able to turn the problem on and off 3 times, then do a process test instead. Compare good processes to bad processes.

DO NOT run a DOE if you can make a chart showing how that root cause is going over time.

Bad habit of problem-solvers.

I’m going to stop for a minute here and bring up a bad habit that many problem-solvers fall into when running DOEs. That is adding unnecessary variables.

“They will add more possible root causes to the DOE than they actually need. Sometimes they do that because they really don’t know what to run, they’re just guessing, and they don’t want to spend the time actually solving the problem the right way.”

A lot of times these guys will run a five variable DOE!  The fact is, really they only had 2 or at the most 3 variables that they needed to run. For whatever reason, they just think it might be a good idea to throw in two extras to see what would happen. In other words, they are throwing away time and money on something completely irrelevant to the problem they’re trying to address!

My Recommendation:

So you’ve gone through this list of reasons NOT to run a DOE and you are still convinced that your project would benefit from a DOE? Here’s my recommendation:

Bring the variables down to two, maybe three at the most , and then run your DOE.

How to Recognize a GREAT Problem-Solver:  

Great problem-solvers think smarter, not harder.

I’m going to save you from making a huge mistake!

Some people think that running a huge DOE will make them look smart! But that’s not the case. Running more than 2-3 variables on a DOE will instead make a problem-solver seem inexperienced or lazy. So be sure to always take the time necessary to understand which 2-3 variables are actually relevant. And then by all means, run your DOE!

To recap…

If you want to solve your projects effectively, efficiently, and lightning fast – and you’re considering using a DOE – remember these steps:

• Evaluate if a DOE is actually necessary or if something else would be even more valuable.
• Make sure you have more than one variable that could be the root cause.
• Once you’ve concluded that a DOE is actually going to benefit your project:
• First go learn more about the root cause.
• Learn more about the project.
• Be sure to narrow down the variables to only 2 or 3 at the most.

If you have experience with running DOEs I would love to hear about it! Comment below to let me know if this video was helpful to you. And let me know what you’d like to hear about next!

Be sure to check out my other videos like:

5S, When NOT to Use

Scrap Reduction, 5 EASY Keys (Fastest Way for Manufacturers to Make More Money!)

Author:  Tom

Raised on a family farm in rural Minnesota, Tom is a strong believer in the merit of honest, hard work. But his passion to work smarter, not just harder, drew him to engineering where he has spent his career as a problem-solver, always looking for yet a faster, better way to make companies more profitable than ever. "Every problem that I have solved I looked back and asked myself, 'how could I have solved it 1 day faster?'” With extensive expertise in various problem-solving methodologies, Tom has dedicated himself to re-inventing problem-solving. He has discovered that, by using his lightning problem-solving methods, up to 90% of all manufacturing problems can, and should, be solved in only 3 days! Tom's years of practical, hands-on experience worldwide, has made him an authority on all things related to problem-solving and lean manufacturing.

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Some students may need support to learn effective problem-solving skills. This resource can assist students to think of and evaluate options to a problem or situation.

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• When the U.S. president makes a disaster area declaration, the IRS can postpone certain tax deadlines for taxpayers in affected areas. Taxpayers in qualified disaster areas do not need to submit an extension electronically or on paper. Information on the most recent tax relief for disaster situations can be found on the Extension of time to file your tax return page.

Find information on free local tax prep

The IRS' Volunteer Income Tax Assistance (VITA) program offers free basic tax return preparation to qualified individuals, including:

• People who generally make $64,000 or less.
• People with disabilities.
• Limited English-speaking taxpayers.

The Tax Counseling for the Elderly (TCE) program also offers free tax help for taxpayers, particularly those age 60 and older.

The VITA/TCE Site Locator can help eligible taxpayers find the nearest community-based site staffed by IRS-trained and certified volunteers. Demand is high for this service, so taxpayers may experience longer wait times for appointments. Taxpayers can use the locator tool to find an available site near them. It’s updated throughout the tax season, so individuals should check back or increase the distance from their location in their search if they don't see a nearby site listed.

MilTax , Military OneSource’s tax service, offers online software for eligible military members, veterans and their families to electronically file a federal return and up to three state returns for free.

Assistive technology options

At the online Alternative Media Center , taxpayers will find a variety of accessible products like screen reading software, refreshable Braille displays and screen magnifying software. These products include tax forms, instructions and publications. Many can be downloaded or viewed online as Section 508 compliant PDF, HTML, eBraille, text and large print.

Having trouble paying? IRS has options to help

For those who owe a payment with their tax return, the IRS has a number of payment options .

For taxpayers that are unable to pay in full by the tax deadline, the IRS recommends they should file their tax return and pay what they can, and apply for an online payment plan . By filing by the deadline, taxpayers will avoid failure to file penalties and interest – even if they’re unable to pay. Taxpayers can explore various payments options; they can receive an immediate response of payment plan acceptance or denial without calling or writing to the IRS. Online payment plan options include:

• Short-term payment plan – The total balance owed is less than $100,000 in combined tax, penalties and interest. Additional time of up to 180 days to pay the balance in full.
• Long-term payment plan – The total balance owed is less than $50,000 in combined tax, penalties and interest. Pay in monthly payments for up to 72 months. Payments may be set up using direct debit (automatic bank withdraw) which eliminates the need to send in a payment each month, saving postage costs and reducing the chance of default. For balances between $25,000 and $50,000, direct debit is required.

Though interest and late-payment penalties continue to accrue on any unpaid taxes after April 15, the failure to pay penalty is cut in half while an installment agreement is in effect. Find more information about the costs of payment plans on the IRS’ Additional information on payment plans webpage.

Adjust withholding to prevent tax "surprises"

Tax filing season is an excellent time for taxpayers to check their withholding to avoid a tax surprise when filing in 2025. Life events like marriage, divorce, having a child or an income change can all impact taxes.

The Tax Withholding Estimator helps people bring the tax they pay closer to what is owed. Employees can assess their income tax, credits, adjustments and deductions, and determine whether they need to change their withholding by submitting a new Form W-4, Employee's Withholding Allowance Certificate to their employer, not the IRS.

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