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International Journal of Productivity and Performance Management

ISSN : 1741-0401

Article publication date: 21 July 2022

Issue publication date: 2 January 2023

Recently, several areas are successfully applying the Lean Six Sigma methodology, specifically in healthcare, public services, higher education institutions and manufacturing industries. This study aims to present an extensive literature review involving Lean Six Sigma practical applications in the last five years, described in a case studies format.

Design/methodology/approach

A systematic literature review was conducted, and 39 articles were selected and analyzed.

An increase in Lean Six Sigma applications in healthcare and higher education institutions was identified. Furthermore, Lean Six Sigma is effectively applied in several areas and is continuously used in traditional industries. The main critical success factor identified was leadership and management involvement, project management and organizational infrastructure, as well as training and education. Also, the main difficulties found are related to the organization's culture and developing communication with leaders and managers.

Research limitations/implications

The main difficulties found in this research are related to the lack of data presented in some articles analyzed, where only information about how the Lean Six Sigma application was conducted is shown, not mentioning difficulties or success factors identified.

Originality/value

Case studies are fundamental to help popularize Lean Six Sigma applications, showing a real-life scenario of how the methodology is implemented, the main difficulties encountered and critical success factors found. Thus, the value of this study is promoting and developing research involving Lean Six Sigma case study applications to guide new researchers and practitioners on the subject.

• Lean Six Sigma
• Critical success factors
• Systematic literature review
• Case studies

Acknowledgements

This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq), Award No. 88887.522103/2020-00.

Francescatto, M. , Neuenfeldt Júnior, A. , Kubota, F.I. , Guimarães, G. and de Oliveira, B. (2023), "Lean Six Sigma case studies literature overview: critical success factors and difficulties", International Journal of Productivity and Performance Management , Vol. 72 No. 1, pp. 1-23. https://doi.org/10.1108/IJPPM-12-2021-0681

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Lean Six Sigma Project Examples | 17 Full Case Studies

Ready to begin your first Lean Six Sigma project? Looking for examples for inspiration or reference to get you started? Here are some project storyboards from different industries and from home. Remember, Lean Six Sigma can help you with more than just work!

• Reducing Underwriting Resubmits by Over 20%  

Governments

• A Call to Change: Pioneering Lean Six Sigma at Los Angeles County  
• Can Lean Six Sigma Be Applied in County Government?  
• How the City of San Antonio Increased Payments for Street Maintenance Using Lean Six Sigma  
• Reducing Bid Tab Creation Cycle Time by 22%  
• Reducing Cycle Time for Natural Disaster Response by 50%  

Manufacturing

• Increasing First Run Parts From 60% to 90% With Lean Six Sigma  
• Reducing Bent/Scratched/Damaged (BSD) Scrap for Building Envelopes  
• Reducing Lead Time in Customer Replacement Part Orders by 41%  
• Reducing Learning Curve Ramp for Temp Employees by 2 Weeks  
• Reducing Purchase Order Lead Time by 33% Using Lean Six Sigma  
• Herding Cats Using Lean Six Sigma: How to Plan for and Manage the Chaos of Parallel Processes  
• Lean Six Sigma Increases Daily Meat Production by 25%  
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Six Sigma Case Study: Everything You Need to Know

Explore the field of Six Sigma Case Studies in our comprehensive blog. From defining the methodology to real-world applications, our 'Six Sigma Case Study: Everything You Need to Know' blog sheds light on this powerful problem-solving tool. Uncover success stories and learn how Six Sigma can drive efficiency and quality improvements in various industries.

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By analysing such case studies, one can gain insights into the successful application of Six Sigma in various industries and understand its impact on process improvement. Read this blog on Six Sigma Case Study to learn how real-world businesses have achieved remarkable process improvement and cost savings. 

Table of Contents  

1) Understanding Six Sigma Methodology 

2) Six Sigma Case Study 

a) Improving customer service 

b) Improving delivery efficiency 

3) Conclusion 

Understanding Six Sigma Methodology

By applying statistical analysis and data-driven decision-making, Six Sigma helps organisations identify the root cause of problems and implement effective solutions. It emphasises the importance of process standardisation, continuous improvement, and customer satisfaction. With its focus on rigorous measurement and analysis, Six Sigma enables organisations to drive efficiency, reduce waste, and deliver exceptional products and services. The methodology follows a step-by-step process called Define, Measure, Analyse, Improve, and Control (DMAIC). These five phases are briefly explained below: 

a) Define: The project goals and customer requirements are clearly defined in this phase.  

b) Measure: In this phase, data is collected to understand the process's current state and identify improvement areas.  

c) Analyse: This phase focuses on analysing data to determine the root cause of defects or variations.  

d) Improve: This phase involves implementing solutions and making necessary changes to eliminate the identified issues.  

Six Sigma Case Study  

In this section we discuss two Six Sigma Case Study that will help you understand and use it better.  

Case Study 1: Improving customer service  

This Six Sigma Case Study will focus on a telecommunications company facing significant customer service challenges. The issues included long wait times, frequent call transfers, unresolved issues, and many more. The company decided to apply Six Sigma methodologies to enhance customer satisfaction.  

a) Define phase: Using the DMAIC approach, the team began by defining the problem: long wait times and inefficient call handling. They set a goal to reduce average wait time and increase first-call resolution rates.  

b) Measure phase: In this phase, data was collected to analyse call volume, wait times, and reasons for call transfers. This helped identify bottlenecks and areas for improvement.  

c) Analyse phase: During this phase, the team discovered that inadequate training and complex call routing were key contributors to the problems. They also found that certain product issues required better resolution protocols.  

d) Improve phase: In this phase, targeted solutions were introduced and implemented to address these issues. The team revamped the training program, ensuring agents were well-trained and equipped to handle customer inquiries. They simplified call routing and introduced automated prompts for quicker issue resolution.  

e) Control phase: Finally, monitoring systems were established in the control phase to track key metrics and ensure sustained improvements. Regular feedback loops were implemented to identify emerging challenges and make necessary adjustments.  

The results were exceptional. Average wait times were reduced by 40%, and first-call resolution rates increased by 25%. Customer satisfaction scores improved significantly, leading to increased loyalty and positive word-of-mouth.  

This Six Sigma Case Study highlights how Six Sigma methodologies can drive transformative improvements in customer service. By focusing on data analysis, process optimisation, and continuous monitoring, organisations can achieve outstanding outcomes and deliver exceptional customer experiences. 

Understand the in-depth process of Six Sigma with our Six Sigma Yellow Belt Course . Join now!  

Case Study 2: Improving delivery efficiency

a) Define phase: The business used the Voice of the Customer (VoC) tool to understand customer needs and expectations. They identified prompt delivery, correct product selection, and a knowledgeable distribution team as crucial customer requirements. 

b) Measure phase: The team collected data to evaluate the problem of slow delivery. They discovered that their Order Fulfillment Cycle Time (OFCT) was 46% longer than competitors, leading to customer dissatisfaction.  

c) Analyse phase: The team brainstormed potential causes of slow delivery, including accuracy of sales plans, buffer stock issues, vendor delivery performance, and manufacturing schedule delays. They conducted a regression analysis, revealing that inadequate buffer stock for high-demand products was the main issue affecting delivery efficiency.  

d) Improve phase: The distributor implemented a monthly demand review to ensure that in-demand products are readily available. They emphasised ordering and providing customers with the specific products they desired.  

e) Control phase: The team developed plans to monitor sales of the top 20% of bestselling products, avoiding over or under-supply situations. They conducted annual reviews to identify any changes in demand and proactively adjust product offerings.  

By applying Six Sigma Principles , the plumbing product distributor significantly improved its delivery efficiency, addressing the root cause of customer dissatisfaction. Prompt action, data-driven decision-making, and ongoing monitoring allowed them to meet customer expectations, enhance its reputation, and maintain a competitive edge in the industry. This case demonstrates the power of Lean Six Sigma in driving operational excellence and customer-centric improvements. 

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Conclusion  

We hope this blog gives you enough insights into the Six Sigma Case Study. This blog showcased the effectiveness of its methodology in driving transformative improvements. By applying DMAIC and using customer insights and data analysis, organisations have successfully resolved delivery inefficiencies, improving customer satisfaction and operational performance. The blog highlights how Six Sigma can be a powerful framework for organisations seeking excellence and exceptional value. 

Learn the six-sigma methodology to achieve business objectives with our Six Sigma Certification Training today!  

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Application of six sigma and the system thinking approach in COVID-19 operation management: a case study of the victorian aged care response centre (VACRC) in Australia

• Open access
• Published: 07 October 2022
• Volume 16 , pages 531–553, ( 2023 )

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• Sandeep Jadhav 2 ,
• Ahmed Imran   ORCID: orcid.org/0000-0001-8258-3550 1 &
• Marjia Haque   ORCID: orcid.org/0000-0002-2036-7294 1  

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COVID-19 has posed many unique and critical challenges in various contexts and circumstances. This often led the stakeholders and decision-makers to depart from traditional thinking and the business-as-usual processes and to come up with innovative approaches to tackle various mission-critical situations within a short time frame. In this paper, a real-life case study of COVID-19 operation management following a multi-disciplinary, multi-stakeholder novel integrated approach in aged care facilities in Victoria, Australia, is presented which yielded significant and positive outcomes. The purpose of the intervention was to develop an integrated system performance approach through the application of various quality management tools and techniques to achieve organizational excellence at the aged care centers. The case involved the use of mathematical models along with statistical tools and techniques to address the specific problem scenario. A system-wide management plan was proposed, involving various agencies across several residential aged care facilities during the pandemic. A three-step methodological framework was developed, where Six Sigma, a system thinking approach, and a holistic metric were proposed to manage the value chain of the pandemic management system. The experimental result analyses showed significant improvement in the management process, suggesting the validity and potential of this holistic approach to stabilize the situation and subsequently set the conditions for operations excellence within the sectors. The model offers new insight into the existing body of knowledge and offers an efficient approach to achieving operational excellence in any organization or business regardless of its type, shape and complexity, which can help practitioners in managing complex, mission-critical situations like a pandemic.

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Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study

Avoid common mistakes on your manuscript.

1 Introduction

The novel Corona Virus Disease 2019 or COVID-19 has posed a new and unexpected global challenge in this twenty-first century. In many cases, this required innovative and emergency responses to bring the escalating situation under control in different circumstances and contexts. Most sectors in all countries around the globe have been affected by the pandemic. The COVID-19 pandemic has brought about many unprecedented challenges and threats to the world's humanity and the lives and well-being, particularly of older people (Chee 2020 ). For example, Australia stood out as an exemplar for its response during the first few months of the pandemic, however later, approximately 75% of the country’s COVID-19 deaths occurred in residential aged care facilities, especially in the state of Victoria (Cousins 2020 ). Therefore, researchers and practitioners were forced to think differently to deal with the crisis of this sector while being motivated by the saying of Napolina Hill, “In every adversity lies the seed of an equal or greater opportunity”. Doctors, nurses, hospital management systems, and governments of various countries kept no stone unturned to contain the rapid spread of the virus and to save lives. COVID-19 management can be viewed as a classic example of a complex problem that requires collective effort and input from multiple stakeholders within a broader organizational perspective. However, there was a notable absence of efficient management coordination and a lack of appropriate planning across various involved parties which caused much chaos during pandemic management crises in various countries (Imran 2020 ). Thus, a study examining the development of innovative approaches such as an integrated management plan involving related stakeholders or agencies became necessary.

While it is difficult to develop and implement a standard approach for dealing with pandemics like COVID-19, current research demonstrates considerable gaps in this sphere and emphasized the importance of establishing an efficient management plan to contain a crisis like a pandemic (Jadhav et al. 2021 ; Reed 2021 ). Few studies have examined the management of the COVID-19 crisis as a system-wide performance achievement process (Hundal et al.  2021a ), (Kuiper et al.  2021 ), (Hassan et al.  2020 ; Zięba  2021 ). Also, the COVID-19 situation being highly contextual and volatile, a standard framework or “one size fits all” approach for its management and containment appeared to be difficult to develop. The fluid and changing circumstances with a lot of uncertainties also did not allow researchers to address this issue more objectively with detailed and well-planned research. Hence the best practices, real-life cases and experiments became a major source of research and experiment to derive new knowledge in this area.

In practice, most organizations have attempted a significant number of improvement initiatives such as total quality, reengineering, restructuring, and teams, with very mixed success. In many cases, these initiatives have been adopted without being part of an improvement strategy, but as part of a series of ‘Adhoc’ decisions. Against this backdrop, this study, motivated by real-life experiences in implementing an organization-wide management plan for COVID -19 in aged care facilities in Victoria, Australia was chosen to experiment and test the proposition. In this context, achieving operational excellence (OE) became the key to success which is required to be implemented across healthcare sectors and/or hospitals, in order to manage the ongoing crisis. OE methods can be used to protect patient and public health to ensure safety and conquer challenges (McDermott et al. 2021 ). On the other hand, researchers have attempted to implement six-sigma approaches, in handling the COVID-19 crisis across various countries (Salentijn et al.  2021 ), (Raja Mohamed et al.  2021 ). Lean Six Sigma (LSS) has also been applied to mitigate the disruption that occurred in healthcare industries due to the COVID-19 disaster (Hundal et al.  2021b ), (Muhammad et al.  2022 ). However, studies integrating various units responsible for pandemic management, thus ensuring a holistic planning and implementation approach to handle the crisis, are scarce.

This study explored the deeper ‘pre-conditions’ required during emergencies for organizational-make especially when uncertainty and ambiguity are rampant. It then examined the management system and measurement metric to assess organizational wellness and understand variability to obtain sustained performance improvements. A holistic management system, tools, and techniques have been proposed that will not only be applied during emergencies e.g., Tsunami, pandemics, etc. but also applicable to steady-state within wider businesses. This approach to management is different from the ‘Adhoc’ approach, and executives or business leaders can assess and predict risks, which is one of the major novelties of this study.

Aged care is considered one of the important sectors in many countries. Ibrahim ( 2020 ) highlighted the importance of understanding the existing gaps in the aged care sector of Australia and the necessity to bridge these for pandemic management. This paper filled this void across different aged care facilities in Victoria during the COVID-19 containment process by offering unique management processes and performing statistical analyses. In this case of aged care facility management in Victoria, the vast range of potential combinations of variable factors became virtually impossible to manually solve. Statistical tools and analyses in such circumstances have proven to be useful. The Design of Experiments (DoE) (Cox and Reid 2000 ) is a statistical tool that provides an approach for optimizing the inputs of greatest influence, understanding the system-effect of their variability, and addressing any uncertainty, especially in a resource- and time-constrained environment. Accordingly, in the case of COVID-19 control and management operations in residential aged care facilities (RACFs), various quality management techniques and statistical analyses were considered as operation management tools and the efficacy of these tools for achieving OE and capturing the complexity of the issue was tested. As such, the Six Sigma tool and systems thinking approach with a holistic metric were considered as the catalysts to efficient management of the COVID-19 crisis.

Accordingly, guided by Yin’s ( 2017 ) case study approach, most suitable to investigate a complex phenomenon, the paper addresses the following research questions:

How can the COVID-19 outbreak be predicted and prevented, as opposed to detect and prevent approach and how the assumptions can be measured and validated?

How the conditions within Victorian Aged Care Centres can be improved and how the control mechanisms can be established through One Metric That Matters the Most (OMTM)?

To answer these research questions, the paper presents a real-life case study involving 21 agencies and authorities who were directly or indirectly involved in the pandemic management process in aged care facilities in Victoria. The study explored how the leaders across the various involved agencies responded to the COVID-19 crisis in aged care centers and explains how the management frameworks, predictive approaches to solution design, and Six Sigma principles were applied in combination to achieve the desired outcome. Drawing insight from the case study, the objective of this paper was to develop an integrated management system and associated metrics using various 21st-century quality management tools as well as a set of guiding principles that are universal to the achievement of OE. In the study, a performance gap was identified to establish a management framework and OMTM during uncertainty and ambiguous situations of the COVID-19 outbreak in Victorian aged care centres. The final outcome of the study is to propose a strategic framework to organizations and bodies like the Commonwealth of Australia (Department of Health) within the aged care sectors in order to create a culture of organizational excellence using the Six Sigma methodology and system thinking approaches. The framework includes a management system and most importantly develops a single metric to assess enterprise risk or wellness. This will preposition the aged Care Sectors to take timely responses or countermeasures during the future pandemic and also manage the current enterprise-wide risks. Whilst the study is focused on the aged care sector, the theory can be applied to any organization or business regardless of its type, shape and complexity.

The remainder of the paper is organized as follows. Section  2 presents a detailed literature review on the research topic. A description of the particular case under study is presented in Sect.  3 and the detailed research methodology is outlined in Sect.  4 . Section  5 highlights the experimental findings. Finally, the results are discussed, conclusions are drawn, with the limitations of the study are considered in Sect.  6 .

2 Literature review

This section presents an up-to-date literature review with a focus on understanding the current knowledge in the field and identifying potential gaps. The review starts by describing several major quality management tools, for example, Lean, Six Sigma, Lean Six Sigma and the system thinking approach. Then, the review focuses on the application of these tools in practical cases, especially in the healthcare industry. Finally, the application of these tools for the COVID-19 management crisis is examined.

As part of process improvement, continuous improvement (CI) is the process of making ongoing improvements to products, services, programs, or processes that play vital roles in organizations (Lam et al. 2015 ). Among the various CI methodologies found in the literature, Lean and Six Sigma are two powerful CIs that are widely used and capable of evolving organizational needs through the CI process (Sreedharan and Sunder 2018 ).

The Lean concept originated within the Japanese automobile industry following the Second World War and was primarily focused on the elimination of Muda, or waste (Ohno 1988 ). Waste can be defined as any non-value-added activity that does not create value for the end customer (Cudney et al. 2013 ). The focus is on non-value-added waste elimination and the seven wastes of transport, inventory, motion, waiting, overproduction, over-processing, and defects, which are all vital in various organizations, including healthcare. The non-value activities may comprise up to 95% of activities in healthcare operations, meaning there is scope for substantial improvement (Gowen et al. 2012 ). The basic principles of the Lean philosophy are the minimization of waste, increasing the speed of all processes across the enterprise, and improving the organization’s performance. A Lean system consumes fewer resources, brings better results, and provides increased benefits for the business to achieve competitive advantages (Hines et al. 2004 ; Wickramasinghe and Wickramasinghe 2011 ).

In contrast, Six Sigma is a data-driven statistical tool used to reduce errors or defects due to excess variation within processes (Antony 2012 ). Specifically, it is used to reduce and control variation in a process so that the process can be improved to meet its target. By definition, sigma or standard deviation represents the variability in a parameter characteristic (e.g., a process parameter, delivery time, response time, etc.) requiring control. According to the properties of a normal distribution, a Six Sigma level process has controlled variation within one-half of the allowed variation limits. When undesirable variation is removed and natural variation is predictable, the outcome can be planned with certainty, which is implied by Six Sigma. Usually, the Six Sigma methodology employs a ‘Define, Measure, Analyse, Improve, And Control’ (DMAIC) or a ‘Plan, Do, Check, Adjust’ (PDCA) approach to deal with problems with unknown solutions, particularly when the root causes need to be discovered (Antony et al. 2018 ). Six Sigma is a useful tool for quality management processes to achieve business process improvements. It has been implemented successfully by many large manufacturing companies, such as Motorola, GE, and Honeywell. The study of Ismyrlis and Moschidis ( 2018 ) demonstrated that companies implementing Six Sigma outperform companies that do not incorporate it.

LSS is the synergistic use of Lean and Six Sigma. As Lean cannot provide statistical control for a process alone, and Six Sigma cannot improve process speed, an integrated approach combining both can achieve improved results (George 2002 ). The combined effect of Lean and Six Sigma has been highlighted by many researchers over the past few decades (Antony et al. 2018 ). The integration of Lean and Six Sigma can achieve better results than those achieved with each individual system alone. The combined LSS strategy integrates both human aspects (such as leadership, customer focus, and cultural change) and process aspects (such as process capability, process management, and statistical thinking) as a process of CI (Antony 2011 ). LSS is an approach to achieving excellence by adopting systems thinking approach, including inputs, processes, and outputs. Thus, the LSS method using the DMAIC strategy ensures a robust framework to achieve business excellence (Su et al. 2006 ). Although it is beneficial, the adoption of LSS is not common, and many organizations often face challenges in implementing it (Yadav et al. 2021 ). Moosa and Sajid ( 2010 ) critically reviewed Six Sigma from both an academic and application point of view. They explored and analyzed several critical factors in the implementation of Six Sigma in organizations based on real-life practice and an analysis of the available literature. The study concluded that the success or failure of such programs mostly depends on the implementation approach rather than its contents.

Though LSS was initially used only in the manufacturing industry, it has gained popularity as a managerial tool, especially in the healthcare sector, with several applications over the past few decades. For example, Six Sigma and LSS have been widely used in the healthcare sector as management strategies to improve patient quality and safety (Trakulsunti et al. 2020 ). Langabeer et al. ( 2009 ) examined whether the use of Lean and Six Sigma quality improvement initiatives would actually help organizations in the healthcare industry to achieve their goals. This research provided descriptive results based on a cross-sectional analysis of a sample of hospitals. Similarly, the study of Lifvergren et al. ( 2010 ) showed that Six Sigma can be a useful tool to improve healthcare processes. This research was based on an examination of a three-year quality program using Six Sigma in a Swedish hospital group. The authors argued that implementing Six Sigma can confer a 75% higher success rate compared to the effects of other healthcare improvement approaches. In another study, Gowen et al. ( 2012 ) examined how process improvement (PI) initiatives mediate the effect of medical error sources on hospital outcomes. The authors explored three PI initiatives: Continuous Quality Improvement (CQI), Six Sigma Initiatives (SSI), and Lean Management Initiatives (LMI).

Lean principles are widely applied in healthcare operations to manage demand and capacity, improve quality, improve safety, improve supplier relations, and reduce costs, thereby improving processes for patient care (Womack and Jones 2017 ). Several researchers have used LSS tools in hospital management studies. For example, Bhat et al. ( 2019 ) studied various LSS tools and techniques in the context of Indian hospitals and showed that the LSS strategy can be effectively applied even in rural hospitals with minimum resource utilization, achieving significant improvements. Scala et al. ( 2021 ) and Improta et al. ( 2020 ) both implemented an SS methodology based on the DMAIC cycle to reduce the length of hospital stays for patients in a hospital in Italy. As the target of Lean thinking is to reduce waste, whereas Six Sigma aims to reduce variation through statistical analysis and process control, integration of Lean principles with Six Sigma can serve to improve patient satisfaction and outcomes in the healthcare industry (Bhat et al. 2019 ). Trakulsunti et al. ( 2020 ) proposed a roadmap involving the use of the LSS strategy across an organization to reduce medication errors. This roadmap helped healthcare practitioners and professionals to apply LSS in a disciplined, organized, and systematic way to reduce medication errors. The first phase of the roadmap assessed the cultural readiness of the organization to employ LSS. The next phase highlighted the key factors for preparing the organization to implement LSS. The factors included top management commitment, LSS project selection, team formation, and training in the implementation of LSS methodology. In summary, Six Sigma, Lean, and LSS have been used by researchers to achieve OE in the healthcare industry.

Total quality management (TQM) is a management strategy involving top management and other workers within the organization; it is used to achieve a quality focus at all levels of the organization. Systems thinking is considered an important dimension in the implementation of the TQM framework in an organization (Oschman 2017 ), (Talapatra and Uddin  2019 ). It is a concept that uses scientific discoveries and instruments to enable a clear understanding of the integrity of phenomena and the achievement of the desired changes (Skaržauskienė and Carlucci 2010 ), (Talapatra et al.  2019 ). From a traditional or classical viewpoint, a system can be defined as a combination of two or more elements, in which every element influences the behavior of other elements, and the behavior of each element influences the behavior of the whole (Bertalanffy 1969 ; Forrester 1975 ). Thus, this view separates the individual pieces of a system. In contrast, the systems thinking viewpoint emphasizes that a set of elements interact to produce behavior in the whole system of which they are a part of (Skaržauskienė and Carlucci 2010 ), (Talapatra et al.  2018 ). Therefore, the systems thinking approach marks a considerable change in the way an organization is traditionally viewed; it involves a change in the organization’s usual perception in which the combination of different components of an organization is considered as the general system (Ershadi and Eskandari Dehdazzi 2019 ). As such, this approach links various parts of an organization to a single whole in order to organize different activities of an organization into one.

An organization can be viewed as a group of people who work together in a structured way for a shared purpose (Gulick 1937 ). However, working together in an organized way requires a systems approach to achieve the organizational purpose. Existing research demonstrates that many organizations are realizing the value of implementing process improvement standards, frameworks, and enterprise strategies to achieve business excellence under uncertain and changing environments (Porter and Tanner 2004 ; Saleh and Watson 2017 ). There is strong evidence that superior business performance can be achieved through the alignment and integration of different business functions (Chan and Reich 2007 ; Rahman et al. 2020 ). Thus, practitioners and researchers are faced with the challenge of understanding how to effectively implement business strategies in order to achieve value and a competitive edge in the market. The commercial business environment is increasingly driven by stakeholder value, customer loyalty, staff retention, corporate governance, market share, and profit against a push to reduce overall costs (Samson 2008 ). To this end, mathematical model-based experiments are increasingly being used in many organizations to mitigate program risk by identifying early problems in system design or sustainment (Estefan 2007 ). As knowledge inputs to these systems are critical, many organizations seek specialist subject matter expert (SME) advice to address challenges and develop solutions. Effective systems methodology includes the following four foundations of systems thinking (Gharajedaghi 2019 ):

Holistic thinking: where the focus is on the system as a whole; this requires understanding the structure, function, process, and context of the system.

Operational thinking (dynamic thinking): refers to the system's dynamics, which may involve feedback systems, identification of the effect and growth, measuring stock and flow, etc. These principles create additional value for managing an organization, whereas business systems are seen as interdependent.

Interactive design: the art of finding differences among things that seem similar and the science of finding similarities among things that seem different.

Self-organization: this involves movement toward predefined order .

Therefore, the significance of systems thinking can be interpreted as an understanding of interrelations that are not associated with linear cause-effect and the identification of processes of change that are not in static states (Senge 1990 ). For this, a problem should be solved starting from the whole, as one component cannot be affected separately by other components. Systems thinking may help to detect the order in a complex system and to ensure a better understanding of reality. Hence, systems thinking is viewed as a discipline of the ‘structure’ with complex situations (Senge et al. 2007 ). A famous commentator in the field of systems thinking states: “Systems approaches aim to simplify the process of our thinking about and managing complex realities that have been variously described by systems thinkers as messes, the swamp, wicked problems. Systems thinking provides ways of selectively handling the detail that may complicate our thinking in a transparent manner, in order to reveal the underlying features of a situation from a set of explicit perspectives” (Reynolds and Holwell 2010 , p.5). An ideal structure would be to employ a ‘systems thinking approach for management within all sectors with a set of guiding principles that are common to all organizations regardless of their type, shape, size, and complexity. This approach notes that most organizations are typically comprised of closely connected elements, including leadership, strategy, customer engagement, performance management, employee relationship, core business processes, and data management (Jadhav 2019 ). An integrated systems approach to OE is a broader program of improving and sustaining business performance, in which quality management is embedded (Basu 2009 ).

However, as mentioned earlier, few studies have examined operation management techniques used to contain COVID-19 crises. It has been challenging to manage healthcare providers with an appropriate operational plan within the rapidly changing, volatile COVID-19 pandemic context. Coordinating various activities across distinct agencies within an extremely short period of time under limited resources has been the most challenging task for authorities. In the published literature, few studies have examined the use of quality management tools in pandemic management. McDermott et al. ( 2021 ) studied how OE can play a role in protecting the public against COVID-19. A few studies have also used Six Sigma and LSS as management tools during the pandemic. For example, Bañez et al. ( 2020 ) analyzed and identified the factors contributing to the mitigation of COVID-19 transmission in the Philippines using the DMAIC framework. Kuiper et al. ( 2021 ) used Six Sigma to study the situation in the Netherlands during the COVID-19 crisis with respect to process improvement efforts. Hundal et al. ( 2021a , b ) investigated how LSS may help mitigate the impact of COVID-19 within healthcare environments. These authors performed semi-structured interviews and the results revealed that personal safety was the primary concern, followed by process redesign and telemedicine. Bhandar et al. ( 2021 ) explored how the use of LSS could help the healthcare sector be better prepared during the global pandemic. Their research utilized the LSS tool and the DMAIC approach to develop strategies for community-based hospitals in the Midwestern US under COVID-19 pandemic planning.

In addition, Gonella et al. ( 2020 ) examined several methodological approaches that can be used for communication purposes in epidemics. The authors used systems thinking approach to develop a stock-flow diagram for the COVID-19 pandemic. Jackson ( 2020 ) suggested how things might have been different had the ‘critical systems thinking’ view of complexity been employed, and a ‘critical systems practice’ approach adopted when preparing for a possible pandemic and responding to it; this study used the Covid-19 pandemic in the UK as an example. In a recent study, Haley et al. ( 2021 ) argued that to tackle difficult problems like the COVID-19 pandemic, a systemic view with systems thinking ideas should be explored from different systemic perspectives.

However, very few studies have developed a system-wide integrated management plan using quality management tools to tackle the COVID-19 crisis. In addition, there has been little focus on aged care facility planning in Australia during the COVID-19 pandemic. Viray et al. ( 2021 ) studied six RACFs in Victoria and observed residential in-reach (RiR) services within these facilities. RiR services in Victoria typically consist of small teams of senior medical doctors and nurse specialists operating out of each public hospital network. The researchers collected data on the cumulative proportion of residents who tested positive for COVID-19 over 21 days after the index case was identified in the first six RACF outbreaks in the study area. The results indicated that rapid cohorting strategies, availability and adequate use of personal protective equipment (PPE), embedded infection control staff, and adequate outbreak preparedness plans may influence RACF containment and minimization of the spread of COVID-19 amongst residents. A summary of the literature on COVID-19 management using various quality management tools is shown in Table 1 .

To date, to the best of our knowledge, no published studies have addressed the COVID-19 crisis from a system-wide viewpoint and have thus proposed integrated plans using quality management tools to achieve OE. Though many studies have focused on the medical issues of the disease, there are few studies of efficient system management within organizations (i.e., aged care facilities). Thus, this study attempts to address this research gap by examining various OE tools and techniques to contain the COVID-19 spread and to apply these in similar contexts in the future.

3 Case description

COVID-19 can be spread from person to person causing flu-like symptoms, and in severe cases, may cause death. COVID-19 was recognized as a pandemic by the World Health Organization (WHO) in March 2020 and has spread to more than 200 countries and territories as of 24 October 2021 (Worldometer 2021 ). Although Australia was in a good position throughout the initial stage of the pandemic, as compared to other countries, it has recorded over 151,943 cases of COVID-19, including 28,5734 active cases and 1,590 deaths as of October 22, 2021, with the bulk of cases occurring within the state of Victoria. Of these, over 2797 cases have occurred within RACFs (Commonwealth of Australia, Australian Government Department of Health 2021 ). As of October 2021, 776 aged care residents in Australia have died from COVID-19 infection, with 684 of those deaths in aged care residents in Victoria (Department of Health [DoH], Australian Government 2021 ). Hence, the management of COVID-19 outbreaks in RACFs in Victoria, Australia was a significant concern.

The criticality of the COVID-19 outbreak within Victorian RACFs was identified on 22 July 2020. This outbreak went on to claim the lives of many senior Australians within one month (VACRC and the Joint Task Group 629.2 of the Australian Defence Force 2020 ). Soon after, the secretary of the DoH and other senior officials met to develop the best approach for responding to this situation. On 24 July 2020, a response center named the ‘Victorian Aged Care Response Centre’ (VACRC) was formed at extremely short notice to stabilize the situation, and an Executive Officer (EO) was appointed by Director General Emergency Management Australia (DGEMA) with support from the secretaries of the DoH and Home Affairs (Engineers Australia 2021 ).

The EO’s experience in the context of crisis and disaster management was an essential enabler of the prompt assessment of the situation and the application of crisis management principles, including the development of a concept of operations and a high-level organization structure in the VACRC. The complexity of the initial response was further exacerbated by the rapidly evolving crisis and subsequent outbreaks. Therefore, it became apparent to the EO that agility in the response time was critical and the importance of this meant that original expectations regarding governance, authorities, facilities, and systems, although important, were lower priorities.

Next, the VACRC team was formalized to commence the operation. The VACRC encompassed 21 different organizations with multiple skills, backgrounds, values, terminology, routines, and corporate cultures that needed to work together to manage the pandemic crisis in aged care facilities in Victoria. The VACRC was strengthened by a common stakeholder resolve to work to their full capacities. The VACRC quickly identified a lack of coordination between supporting agencies fueled by a lack of clearly defined roles or responsibilities. This was creating confusion across the broader aged care community.

However, COVID-19 was winning the fight. The velocity, capacity, and modes of COVID-19 spread overwhelmed the structures and the required response was beyond the systems and processes that were in place at the VACRC (then-current state). A major contributing factor to this was the lack of agility with the information system; the data were stored in silos and were not integrated across the various datasets. Real-time or near real-time risk analysis of the facilities was not possible. This was further aggravated by a lack of clarity in the process of understanding and identifying key input variables and the outcome.

The first challenge for the VACRC team was to orchestrate and develop a robust intelligence feed and a functional common operating picture. The team needed to fuse multiple streams of data and information with varying levels of coherence to assemble the picture. The team then needed to determine the next course of action to achieve the steady-state operation of the VACRC underpinned by an evidence-based decision-making process. It was necessary to shift the paradigm from Detect-to-Prevent to Predict-to-Prevent to defeat the outbreak of COVID-19 within the aged care sector of Victoria under such ambiguous and uncertain circumstances. This is the point of excellence noted in this case study. However, the volume of data, the variability within it, and the velocity at which it came from each subject matter expert (SME) varied considerably. The following critical problems were identified by the VACRC team:

Identify key COVID-19 management inputs and clarity of process.

Develop a system thinking approach within the aged care sector in Victoria.

Develop a metric for holistic assessment of aged care facilities.

These needed to be addressed immediately to stabilize the situation, which subsequently set the conditions and guiding principles to achieve operational excellence.

4 Methodology

The Operation COVID Assist for the Victorian aged care sector was an exemplary case study that demonstrated a true collaborative approach to predict and prevent the outbreak using the Six Sigma Methodology. The conventional, stereotype thinking with a status quo outlook was challenged and therefore a new thought-process was developed/invented. The section describes the methodology and underpinning threads required to take calculated decisions when there was no data available during ambiguous and uncertain situations (e.g., a fog of war). It details how SME knowledge was leveraged to develop multiple emergency scenarios, create narratives and subsequently transform them into:

a valuable data to generate mathematical models,

understand key input variables and their interactions

predict and prevent the outbreak and

guide the deployed force elements and minimize risk.

The initial theoretical modeling and simulations were then verified and validated with the help of real-life data (actual data from the field). Any anomalies or residues were rectified by adjusting coefficients within the mathematical model. It was also revealed that the full deployment of structured tools such as MAP (Military Appreciation Process) for the planning process was not as effective as it could be. Therefore, short sprints of multiple tools and techniques were initially trailed instead of setting a deliberate approach to tool planning.

However, this research was conducted in phases using a mixed-methods approach; this approach is becoming increasingly popular for addressing complex phenomena. According to Bryman ( 2007 ), “bringing quantitative and qualitative findings together has the potential to offer insights that could not otherwise be gleaned” (p. 9). This study commenced with a broad overview and identification of key issues through qualitative inquiries. This was followed by a focused investigation into the identified key factors using appropriate mathematical analysis tools borrowed from different disciplinary knowledge domains.

The following steps were taken to conduct the research:

Step 1: Data collection

Within the VACRC, several agencies and authorities provided input into the decision-making process. For this study, data were collected from the following 21 stakeholder agencies who were involved in COVID-19 crisis management in Victoria:

Australian Defence Force (ADF)

Australian Medical Assistance Teams (AUSMAT)

Department of Health and Human Services (DHHS)

Multiple third-party service providers

Epidemiologists (from various hospitals)

Group of surgeons (from various hospitals)

Nurses (from various hospitals)

Burnett Institute (an Australian medical research institute)

Data analytics consultants

IT staff and engineers, etc.

For data collection, a section leader from each of the above sectors or agencies was selected as the SME to provide their opinion/input to the study. A checklist (see Appendix: Table 4 for details) was developed for SME compilation; this comprised over 40 entries assessing more than 20 factors. A mixed-methods approach was adopted for data collection in this real-life experimental study, with the view to developing an operational management plan to tackle the COVID-19 spread across aged care facilities in Victoria.

In this first step, a focus group discussion (FGD) following the nominal group technique (NGT) was performed to identify contributing factors and establish multiple hypotheses. The FGD method involves obtaining data from a purposely selected group of individuals, rather than from a statistically representative sample of the broader population. It is a technique where researchers gather a group of individuals to discuss a specific topic, with the aim of obtaining information based on their complex personal experiences, beliefs, perceptions, and attitudes through a moderated interaction (Nyumba et al. 2018 ; Smithson 2000 ). In contrast, the NGT, which is one of the most commonly used formal consensus development methods, involves face-to-face discussions in small groups (Harvey and Holmes 2012 ; McMillan et al. 2016 ). In this study, both FGD and NGT were applied to gain data on the opinions of SMEs to develop the models.

Step 2: Application of Analytical Hierarchical Process (AHP) In this step, the popular multi-criteria decision-making tool, Analytical Hierarchical Process (AHP) (Saaty 1994 ), was used to identify the weights and relative importance of the factors that were identified in the data collection process with SMEs. The details of the weights of the factors can be found in the Appendix (Table 5 ). Thus, the critical factors were selected to conduct the experiments. It should be mentioned that only partial usage of the AHP was deployed for decision analysis and judgment assistance for the Senior Leadership Team (SLT). Sensitivity analysis and further AHP calculations were out of scope for this case study. The following approach was taken during the partial application of the AHP:

Set up the decision hierarchy with the help of SMEs and SLTs

Conducted pairwise comparison of the attributes and alternatives, and finally

Transformed the comparison into weights and checked the consistency of the decision-making and its comparison.

Step 3: Conducting the experiment

In step 3, experiments were conducted using several quality management tools and mathematical modeling and statistical analyses were performed. The models were then validated using real-life case study data. Furthermore, the model results were adjusted with any undesirable reaction. These experiments and the corresponding results are described in detail in the next section.

For better understanding, a flowchart of the methodology conducted in the case study is illustrated in Fig.  1 .

Flow chart of the Methodology

5 Experiments and results

This section presents the experiments and the results of the study using the Six Sigma (DMAIC) approach.

5.1 Identification of key COVID -19 management Inputs and clarity of process

The first step in the experiment was to identify the major inputs responsible for the pandemic management process in aged care facilities in Victoria. This step was achieved with the following three sub-steps:

5.1.1 Phase 1: planning (DEFINE)

To accelerate knowledge discovery and identify key input and output variables involved in the achievement of the required medical effect in aged care facilities, a cross-functional stakeholder engagement activity was conducted. To challenge potential organizational bias from the stakeholder agency SMEs, a non-prescriptive approach was adopted to determine the best approaches for detecting and preventing further outbreaks. The paradigm-shifting methodology is detailed in Fig.  2 .

Detect and prevent approach (Allen 2020 )

A checklist (see Appendix: Table 4 ) was developed, comprising more than 40 entries assessing more than 20 factors. However, this proved to be extremely challenging for the SMEs to provide their knowledge, with approximately 1,080,000 combinations generated by these factors. To overcome this, the approach was refined, as depicted in Fig.  3 , and was applied to SME knowledge and skills to identify key variables and predict their effects.

Predict and prevent approach (EA/CE forum, dated 2 Aug 21)

Using the refined approach and through targeted brainstorming sessions, the following key inputs were identified.

Infection, prevention and control (IPC)

Operations management (OM; e.g., PPE, training, efficiency and effectiveness of extant processes)

Clinical elements within the aged care facility

Next, these factors were ranked using the AHP method, as stated earlier.

From Fig.  4 , it can be seen that IPC had the highest criteria weighting (50.2%), followed by clinical elements (37%), OM (8.1%), and finally, leadership (4.8%). Thus, based on the opinions of SMEs, the most important factors were IPC and clinical elements. These findings can also be depicted as the main effects, as shown in Fig.  5 . The steeper the gradient of the line, the greater the importance of the factor. It can be seen that IPC and clinical elements had the highest gradients, whereas leadership and OM were approximately equal in their gradient scores during the FGD brainstorming process.

Criteria selection with the AHP method

Plot of main effects obtained from the FGD

Once the inputs were identified, measures of system performance or medical effect were developed in order to provide guidelines for the assessment of the output, including measurement, accuracy, and precision. Random replications were created using Taguchi-L8 for the DoE setup. A regression model approach was utilized to illustrate the relationships between the response, that is, the medical effect, and the input variables (i.e., leadership, IPC, OM, and clinical elements). The statistical significance of the input variables was determined using p-values of the regression model and the importance of each input variable was analyzed. In order to develop a regression model based on the significant main effects and interactions, the first step was to determine the regression coefficients.

In addition, SMEs ranked the medical effect of random combinations on a scale of 1–10, as recorded on the response table as shown in Table 2 . It was considered that the unknowns could be answered more efficiently with fewer trials if the responses were repeatable, accurate, and measured on a continuous scale. It should be mentioned here that 16 scenarios were generated from Taguchi L-8, and for each, around 5 SMEs provided their opinions, which were considered as input in the study.

With the responses documented and all combinations of factors considered, the VACRC team developed several mathematical models and performed statistical analyses to develop an effective OM plan for aged care facilities. For this, a regression-based mathematical model was created using ‘Excel’, as presented in Table 3 . This was created to build a predictive approach to the response function, that is, the medical effect. Whilst it was clear from the regression coefficients in Fig.  6 that leadership, IPC, and OM were more important due to their higher coefficients, it was also necessary to examine the interactions between these inputs. However, based on this analysis, it can be concluded that leadership should be considered the most important variable, with a high p-value. This contradicts the initial findings obtained from the AHP and FGD processes, as shown in Figs.  4 and 5 .

Residual charts

Figure  6 presents the residual charts for all critical factors obtained from the regression analyses. The positive values for the residual (on the y-axis) mean that the prediction was too low, and the negative values mean that the prediction was too high; 0 means that the SMEs’ advice was correct. It can be seen that, aside from leadership, all other factors varied significantly between the actual and predicted values.

5.1.2 Phase 2: understanding key interactions (MEASURE)

Whilst engineers, technologists, logisticians, and professionals, in general, tend to analyze the causes of problems, the common failure is a lack of understanding of key performance input variables and, more importantly, their interactions. Therefore, in this phase, the major interactions between the input factors were studied.

To do this, the main effects of the above key inputs were drawn using QuantumXL software, as shown in Fig.  7 . For each, the change in the output medical effect was assessed as a function of change in the input variables. IPC was seen to have the greatest impact on the medical effect, as evidenced by the steepest gradient among the four factors. However, the interaction between leadership, IPC, clinical elements, and OM was a major concern to doctors, nurses, epidemiologists, senior executives, and the whole VACRC team. As such, it required careful attention.

Main effects and interactions among the factors

Thus, plots were drawn to highlight the inputs for which the interaction effects were most important to the process design and optimization study, as shown in Fig.  7 . The most important input variables, their absolute values, and the interactions shown in the Pareto graph indicated that IPC was the most important factor, followed by OM, clinical elements, and leadership.

Figure  8 shows the factors contributing to the COVID-19 outbreak within aged care centers. It can be seen in the Pareto Chart that 80% of the issues were attributed to the internal management of the centers, including the floor plan layout (17%) and bins-related (13.6%). Figure  9 presents the overall Pareto Chart for the centers. It can be seen that 87.5% of the issues within the aged care sector were directly related to leadership and OM.

Pareto chart

OM Pareto chart

Figure  9 indicates approximately 65% of the issues within the Victorian aged care centres. The raw data for this graph was through a checklist which was being audited every day for real-life field data entry. A sample checklist (Table 4 ) is attached in the appendix.

Next, response contour and surface plots were drawn, as shown in Figs.  10 and 11 , respectively. These graphs are useful for establishing desirable response values based on individual operating conditions. In the contour plot, the response surface is viewed as a two-dimensional plane, in which all points that have the same response are connected to produce control lines of constant responses. The surface plot displays a three-dimensional view that may provide a clearer picture of the response. The first-order regression model contains only the main effects and no interaction effects; the face of the fitted responses will be curved rather than straight. The response contour and surface plots (developed using the software Quantum XL by Sigma Zone) for the clinical effects are shown in Figs.  10 and 11 , respectively. Both surface plots help to understand the nature of the relationships between IPC, OM, and clinical elements inputs and the resultant medical effects. It can be seen from the figures that the medical effect increases with increases in IPC and clinical elements, as well as with increases in IPC and OM. These factors were key contributors to enhancing the resultant medical effect.

Clinical effects contour plot

Clinical effects surface plot

For more analysis, Interaction plots and Noise factors Pareto plots of the key input parameters are also derived in the study. For example, plots of parameter ‘operations management’ with others are presented in Fig.  12 . It is clear from the Pareto chart that interaction between input variables leadership and operations management has the least significance or there is no interaction between leadership and operations management.

Interaction Plot and Noise Factor Plot

5.1.3 Phase 3: validation using control charts (ANALYSE)

Whilst charting data provides a simplified manner of conveying information, the use of common bar charts, pie charts, and line graphs and the trending of variables tends to depict the ‘after the fact’ or ‘for information’ data. Of greater importance is the depiction of ‘during the fact’ or ‘near real-time’ data, as this lends itself better to predictive approaches. Hence, decisions based on numerical data (or structured data) alone will rarely provide the appropriate confidence levels. For this reason, the models were validated using SME knowledge and control charts, which were then used to understand the trends, probable outbreaks, and process change within multiple clusters of aged care centers within Victoria. These data feeds were obtained through the checklist data. In this phase, a control chart was drawn using the QI Macros tool to present the average nurse to resident ratio and the control limits, as shown in Fig.  13 . It can be seen that the facilities with the nurse to resident ratios below the average had a high likelihood of an outbreak, and vice versa. This predictive modeling was validated when outbreaks were predicted in at least 13 of the aged care facilities 48 h before they occurred.

Control chart for nurse to resident ratio within aged care centers

In this paper, the developed mathematical model was used to source the data points for Fig.  13 , which presents the nurse to resident ratio, and subsequently, these points were plotted using a control chart (also known as X-bar chart); this is another way to observe the data distribution. It should be noted that, in the chart, the X-axis represents clusters of different aged care centers and the Y-axis represents the nurse to the resident ratio (various constants). From the figure, the UCL (upper control limit) and LCL (lower control limit) indicate values three times the standard deviation from the mean value (which is the center of the line). Thus, 99.75% of the data will fall between these limits. In this situation, anything above average was most desirable, as it reflects higher patient care, whereas the below-average ratio values should be considered as unsafe conditions. Thus, the aged care centers that fell within those unsafe conditions (as highlighted with circles in the graph) were classified as high-risk for potential COVID-19 outbreak.

The control chart became a combat multiplier as it allowed potential outbreak points to be anticipated; that is, the times and places where outbreaks could possibly occur were predicted. This then allowed the force element applied to the region to be adjusted to analyze how much change was required to achieve the required clinical effect. In turn, it permitted the balancing of staff numbers within individual aged care facilities as the COVID-19 data changed rapidly. Whilst this modeling was not fully precise due to the rapidly changing pandemic and the questionable data quality, the methodology provided actionable data and analysis backed by a logical thought process that provided clarity in decision-making.

5.2 Integrating the systems thinking approach in the aged care sector (IMPROVE)

The next step in the experiment was to develop an integrated system thinking approach for the aged care sector. When considering the integration of systems thinking approach elements in the context of Victorian aged care facilities, the cost drivers responsible for their implementation created increased pressure on individual aged care centers to become more efficient and effective. An organization may choose either a single framework or a combination of frameworks or tools to standardize its systems approach. Sound combinations predetermine a structured approach to identify problems, validate and verify problems, and analyze and solve them. In order to understand any of these frameworks, it is extremely important to understand the individual tools and techniques of each. As DMAIC is the most widely used framework and was extensively employed for LSS projects, it was used to develop the strategic excellence framework for the VACRC.

In this study, an integrated systems approach was developed to achieve OE, with an alignment to quality improvement initiatives such as LSS, as shown in Fig.  14 . This may be positively complemented within the approach in the mentioned case study. In order to help leaders to build their organizations and provide customer focus excellence, the systems approach requires hard wiring of the organization context/profile (the background of organization existence), leadership, and six core elements (customer, strategy, process, employee, results), as shown in Fig.  14 . The approach also consists of automatic feedback and learning loops for continuous improvement and innovation. Leadership within the organization plays a pivotal role in achieving sustainable excellence. Thus, the system commences with leadership. Next, leaders must understand customer requirements and employ strategies to meet them. End-to-end business processes must be designed for efficient and effective business outcomes. People within an organization generate results for the whole system. From these results, organizations must gain knowledge to provide feedback to the employee, help manage and improve processes, inform strategic outlooks, maintain customer relations, and help leaders to drive the business. The organizational system operates under the direction of senior leadership. As new information comes to light, feedback and learning mechanisms must distribute it promptly on a ‘need-to know’ basis. The systems approach requires leaders to understand all aspects of their business, especially through an organizational context which includes customers and products, delivery systems, employees, and governance systems, as well as competitive and strategic situations. The OE system is an underlying thread that provides a ‘conditioning and umbrella effect’ to LSS methodology.

Systems approach to excellence within aged care centres

5.3 Holistic assessment using a metric (Control)

The last step was to introduce a holistic assessment as a metric to measure the system performance of aged care facilities. To survive and grow, an organization must meet its stated objectives. This measure can be used to identify whether an organization is meeting its objectives or not.

As business organizations, aged care facilities need to frequently measure their performance in order to evaluate their past performance, identify where the gaps are, and determine how to improve those gaps. Historically, as supply gears up to meet the rising demand for a consumer product, the productivity of an organization becomes the most important performance measure. As the gap between supply and demand decreases and competition increases, customers have better choices, and they begin demanding more features and better service. Customer demand traditionally propels companies to innovate and diversify in order to grow their businesses which, in turn, become more complex. Thus, productivity alone is an insufficient measure of business performance. Global integration and increasing competition also create challenging business dynamics. In addition to the changes brought by the COVID-19 pandemic, organizations such as aged care centers face an ever-evolving landscape of frequent collapse, mergers, acquisitions, competition from parallel and substitute products, and threats from buyers and suppliers. Excelling in this environment requires a comprehensive reporting system that can accurately read the business dynamics (Gupta 2006 ). Hence, a metric that can provide a holistic view of the organization and timely feedback for monitoring and improvement purposes is required.

With the current and anticipated unsettling environment (pre and post-COVID-19), aged care centers require a performance measure that is robust and that can address various aspects of the organization, including leadership, strategy, customer, operations, and processes (as detailed in Fig.  14 ) to provide a holistic view of organizational wellness, performance gaps, and associated risks. Therefore, an index was developed to measure the above aspects within aged care centers to assess the opportunities for improvements. The index was developed using weighted average scores for the 10 leading indicators (as shown in the figure) and their significance levels. This was performed using a consultative approach with the key stakeholders, e.g., surgeons, nurses, doctors, ADF officers, CEOs of the aged care sector, emergency services such as ambulances, etc. The higher the index, the lower the risk, and vice versa. Moreover, this index was subsequently mapped on the Six Sigma scale to understand the goodness/wellness of the organizations and to set the conditions for excellence.

An example of this index was developed using MS Excel, as shown in Fig.  15 . As can be seen, there was a positive linear correlation between the wellness index and the performance of the centers. It can also be observed that a higher aged care facility wellness index means a lower risk facility risk in terms of the COVID-19 outbreak. The index threshold for aged care centers was recommended at 50 for low-performance organizations/centers.

Facility wellness index and trend

The developed models along with the related analyses discussed above were successfully implemented in the VACRC to manage the COVID-19 crisis using LSS, systems thinking, and other relevant approaches. In this case study, several additional issues were identified that may further aggravate the situation of non-excellence. For instance: non-extant data collection regime for measurement analysis and knowledge management, a lack of relationship between operations focuses on employee and key result areas, loose connection between workforce planning and strategic planning, non-alignment of IT frameworks with business policies, etc.

6 Discussion

The case study described in this paper operated under a complex, ambiguous and uncertain environment with real-world consequences for any action and, more importantly, non-action during COVID-19 crisis across Victoria. Any delay within the decision-making processes could have not only cost the lives of elderly Australians but also endangered the team’s effort. In order to stabilize the situation, a predictive approach was developed to understand the processes involved and their associated data. The predictive thinking approach, deploying LSS tools, and utilizing SME knowledge to develop robust solutions in a data-poor environment, were key contributing factors to the success of the system. In this study, qualitative data were used to develop the predictive mathematical model to manage the crisis situation. While a range of accurate quantitative data is critical, qualitative data through interviews and engaging with subjective judgment on a personal basis can be useful to complement or in the absence of numerical data. During the COVID-19 outbreak, stakeholders within the VACRC, such as medical doctors, surgeons, defense force officers, nurses, IT staff, and engineers were interviewed using FGD and NGT approaches to obtain qualitative data which were subsequently converted into more meaningful quantitative data for the purpose of further analysis. Next, the AHP was adopted to rank the criteria and several quality management tools (checklist, Six Sigma, system thinking approach, etc.) and statistical analyses (regression analysis, control chart, Pareto chart, etc.) were performed to obtain OE in the system. From this real-life experiment, it is clear that sufficient operations management measures are critical to tackling complex problems such as the COVID-19 pandemic in the aged care sector in Victoria. There is a broad consensus and clear evidence within the aged care sector context that the alignment of management functions can produce significant improvements.

6.1 Study implications

The major contributions or implications of this study are two-fold.

Theoretical implications

The study made significant theoretical contributions to the existing body of knowledge by;

offering a holistic management system, tools and techniques integrating a system thinking approach with various quality management tools for improving organizational performance in the case of challenging situations like pandemics,

providing additional insight into the development of mathematical models and the use of statistical analyses to achieve OE across the system,

filling the void and absence of efficient management system to deal with a complex situation with multi-stakeholder involvement and

offering a single metric to assess enterprise risk and wellness.

As discussed in the literature review section and highlighted in Table 1 , only a handful of research have been found applying different tools and techniques to enhance operational performances in managing wicked problems like COVID-19. However, most of these studies focused on a single tool or measure in addressing the problem scenarios. Such as Six Sigma (Bañez et al.  2020 ; Kuiper et al.  2021 ), LSS (McDermott et al.  2021 ; Hundal et al.  2021a , b ; Bhandar et al.  2021 ), system thinking (Gonella et al.  2020 ; Haley et al.  2021 ) etc. have been used as operation or quality management techniques on different contexts in isolation. Hence, to the best of our knowledge, this research is the first formal study performed on a real-life case study integrating various OE tools and techniques (e.g., Six Sigma, systems thinking, checklist, etc.) and performing statistical analyses (regression analysis, control chart, Pareto chart, etc.) to develop an effective systemwide operational plan to manage critical and complex situations like COVID-19.

Inspired by the suggestions from Moxham and Kauppi ( 2014 ) and Haldorsson et al. ( 2015 ), this research used a combination of various theories and methods to solve a real-life problem by exploring some new dimensions of the operations management practice beyond the traditional OM subject areas and concepts. Thus, the study fills a gap between practice and existing theories, i.e. lack of theory-grounded research (Ketchen and Craighead  2020 ) in operations management, which offered a unique opportunity to understand the wicked complexity posed by COVID-19. This study is also a response to the call for theoretical concepts from other disciplines (Samson and Kalchschmidt  2019 ).

Practical implications

The study has significant implications for operations and management practitioners to tackle real-world complex, messy problems realistically. It provides an opportunity to draw lessons from the experience of VACRC, where the application of a single framework or methodology was ineffective during the COVID-19 outbreak within the aged care sector. Whilst the methodology was deployed for the Victorian aged care sector, its application in organizations could be versatile.

Taking insights from the study, practitioners can trial and deploy a combination of frameworks and methodologies in many other contexts for gaining operational efficiency. This approach departs from the 'Adhoc' and business as usual (BAU) approach, where executives or business leaders can assess and predict risks. This is one of the significant advantages of this approach. The approach and set of guiding principles for management are mostly common to organizations regardless of their type, shape, size and complexity, as all elements, including leadership, strategy, customer engagement, performance management, employee relationship, core business processes and data management are closely connected. Hence, the principles, findings, and techniques outlined in this paper can be applicable and contextualized in multiple areas.

The study will help directly the government bodies like the Department of Health of the Commonwealth of Australia to adopt and formulate a strategic framework to create a culture of organizational excellence using the Six Sigma Methodology. This is likely to help aged care sectors to take timely responses or countermeasures during the future pandemic and manage the current enterprise-wide risks. The application of this approach can be extended to profit/non-profit, education sector, hospitals, military and government organizations.

6.2 Limitations and future research directions

The study should admit its limitations. First, it was beyond the scope of this study to trial the approach within other areas of business or industry sectors. Second, the study was conducted under a limited data availability situation and limited time frame. Hence, utilizing more data and information could make the results more robust.

While the approach detailed in this paper was deployed to the COVID-19 outbreak situation within the Victorian aged care centers, its applicability and scalability cut across other emergencies and future pandemics. Future research can be expanded by applying this approach at the strategic management level by considering all relevant aspects. Since the underlying principles, constants of management, and threads are common to any organization, the approach can be applied widely for organizational performance or excellence. In the face of constant change driven by digital transformation in organizations, more detailed studies regarding IT service architecture, technology stack, data highway and digital inequality may be undertaken using the framework detailed in this paper. The methodology of this study could also be applied in other disaster management areas as a collective decision-making frame with poor data-driven scenarios.

7 Conclusion

The objective of this study was to develop an effective integrated management and measurement system to tackle the COVID-19 crisis using some contemporary quality management tools and techniques. Based on a real-life case study of several aged care facilities in Victoria, Australia, the study revealed that organizations struggled to strike a balance between a sound management system and its associated measurement systems. The study was conducted in the VACRC, where Victoria's pandemic situation was worst. The study revealed that the management system was fragmented, and the measurement system was not aligned and integrated. Thus, the approach deployed in this paper ensured the correct orchestration and synchronization of the management system and measurement system. It also 'gelled' practice and performance areas within the centers with 'hard-wiring' of the context or purpose of the organization. Thus, employing a system thinking approach in aged care centers using a single metric helped to improve organizational efficiency and effectiveness as well as compliance improvement, helping to achieve the elusive goal of OE. The key elements discussed within the paper include alignment, distributed leadership, integration, time-based, OMTM, structured thinking, customer value, predictive analytics and poor data-driven decision-making. Their strength of existence correlates strongly with superior organizational performance and risk mitigation. Executives, CEOs, and military commanders must assess, measure and improve their organizations to achieve superior performance and wellness. It is expected that once an organization has firmly established the above elements, initiatives such as quality improvement will have a solid foundational base and will likely be sustainable and successful.

In a nutshell, by developing a management system and corresponding metrics, this study explored the deeper 'pre-conditions' that explain the variance in the success of aged care centers and the resulting performance changes. This unique approach could be applied to various organizations regardless of their type, shape, and complexity. The study thus provides new insights on integrating multiple strategies that can be used in several other circumstances to achieve better outcomes.

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Jadhav, S., Imran, A. & Haque, M. Application of six sigma and the system thinking approach in COVID-19 operation management: a case study of the victorian aged care response centre (VACRC) in Australia. Oper Manag Res 16 , 531–553 (2023). https://doi.org/10.1007/s12063-022-00323-2

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Top Six Sigma Case Study 2024

Home Blog Quality Top Six Sigma Case Study 2024

Six Sigma is an array of methods and resources for enhancing corporate operations. When Bill Smith was an engineer at Motorola, he introduced it in 1986 to find and eliminate mistakes and defects, reduce variance, and improve quality and efficiency. Six Sigma was first used in manufacturing as a quality control tool. When long-term defect levels are less than 3.4 defects per million opportunities (DPMO), Six Sigma quality is reached.

Six Sigma case study   offers a glimpse into how various companies have harnessed the five distinct phases: defining, measuring, analyzing, improving, and controlling, principles of Six Sigma to overcome challenges, streamline processes, and improve across diverse industries.

What Are Six Sigma Case Studies, and Why Are They Important?

Six Sigma case studies examples   show how Six Sigma techniques have been used in businesses to solve issues or enhance operations. For practitioners and companies pondering enforcing Six Sigma concepts, these case studies are an invaluable resource to learn the advantages and efficacy of Six Sigma adoption.

Here are the reasons why six sigma case study is important:

Success Illustration: Case studies demonstrate how Six Sigma projects generate tangible advantages like better productivity, fewer defects, and more customer satisfaction while providing unambiguous evidence of their efficacy.

Learning Opportunities:  They deliver vital insights to use Six Sigma tools and processes realistically and allow others to learn from successful approaches and avoid common errors.

ROI Demonstration:  Case studies provide quantitative data to show the return on investment from Six Sigma projects, which helps justify resources and get support for future initiatives.

Promoting Adoption:  They cultivate a continuous improvement culture and show how Six Sigma concepts can be used in different situations and sectors, which encourages other businesses to embrace the methodology.

Become a Six Sigma Certified Professional and lead process improvement teams to success. Learn how to streamline processes and drive organizational growth in any industry. Join our Lean 6 Sigma training courses and transform your career trajectory with valuable skills and industry recognition.

Six Sigma Case Studies

Let us discuss some real-world case study on six sigma   examples of successful Six Sigma undertakings through case studies:

1. Six Sigma Success: Catalent Pharma Solutions

Do you know how Six Sigma techniques turned things around for Catalent Pharma Solutions?

Six Sigma methodologies, initially presented by Motorola in 1986 and prominently used by General Electric during CEO Jack Welch's leadership, are essential for enhancing customer contentment via defect minimization. Catalent Pharma Solutions, a top pharmaceutical development business, employed Six Sigma to address high mistake rates in its Zydis product line. By applying statistical analysis and automation, training employees to various belt levels, and implementing Six Sigma procedures, Catalent was able to maintain product batches and boost production. This case study illustrates how Six Sigma approaches are beneficial for businesses across all industries as they can improve processes, prevent losses, and aid in cost reduction.

2. TDLR's Record Management: A Six Sigma Success Story

The Texas Department of Licensing and Regulation (TDLR) faced escalating costs due to the storage of records, prompting a Six Sigma initiative led by Alaric Robertson. By implementing Six Sigma methodologies, process mapping, and systematic review, TDLR successfully reduced storage costs and streamlined record management processes. With a team effort and strategic changes, TDLR has achieved significant cost savings and improved efficiency. The project also led to the establishment of a robust records management department within TDLR.

3. Six Sigma Environmental Success: Baxter Manufacturing

Baxter Manufacturing utilized Six Sigma principles to enhance its environmental performance and aim for greater efficiency. Through the implementation of Lean manufacturing and accurate data collection, Baxter reduced waste generation while doubling revenue and maintaining waste levels. With a cross-functional team trained in Six Sigma, the company achieved significant water and cost savings without major investments in technology. It led to promotions for team leaders and showcased the effectiveness of Six Sigma in improving environmental sustainability.

4. Aerospace Manufacturer Boosts Efficiency With Six Sigma

Have you heard about how Six Sigma principles transformed an aerospace parts manufacturer? Here is the 6 Sigma case study   for aerospace parts manufacturer

A small aerospace parts manufacturer used Six Sigma to cut machining cycle time, reducing costs. Key engineers obtained Six Sigma certification and led the project, involving management and operators. Using DMAIC, they analyzed data, identified root causes, and implemented lean solutions. The process yielded a 46% reduction in cycle time and an 80% decrease in variation, enhanced productivity and profitability. The case highlights how Six Sigma principles can benefit businesses of all sizes and emphasizes the importance of training for successful implementation.

Enroll in the  Lean Six Sigma Green Belt certification online training to advance your career! Gain expertise in process improvement and organizational transformation with expert-led training and real-world case studies. Start now to become a certified professional in quality management.

5. Ford Motors: Driving Success

This is a   case study on Six Sigma  i ncorporated by Ford Motors to streamline processes, improve quality, significantly reduce costs, and reduce environmental impact. Initially met with skepticism, Ford's implementation overcame challenges, achieving remarkable results: $2.19 billion in waste reduction, $1 billion in savings, and a five-point increase in customer satisfaction. Ford's Consumer-driven Six Sigma initiative set a benchmark in the automotive industry and proved the efficacy of data-driven problem-solving. Despite obstacles, Ford's Six Sigma exemplifies transformative success in process improvement and customer satisfaction enhancement.

6. 3M's Pollution Prevention Six Sigma Success

Have you checked out how 3M tackled pollution with Six Sigma? It's pretty remarkable. 3M leveraged Six Sigma to pioneer pollution prevention, saving $1 billion and averting 2.6 million pounds of pollutants over 31 years. With 55,000 employees trained and 45,000 Lean Six Sigma projects completed, they focused on waste reduction and energy efficiency. Results included a 61% decrease in volatile air emissions and a 64% reduction in EPA Toxic Release Inventory. Surpassing goals, they doubled Pollution Prevention Pays projects and showcased Six Sigma's prowess in cost-saving measures.

7. Microsoft Sigma Story Lean Six Sigma

By using Lean Six Sigma case studies, Microsoft increased customer interactions and profitability through waste removal and process optimization. They concentrated on improving the quality of the current process and reducing problems by utilizing the DMAIC technique. Eight areas were the focus of waste elimination: motion, inventory, non-value-added procedures, waiting periods, overproduction, defects, and underutilized staff talent. Microsoft streamlined processes and encouraged innovation, which allowed them to maintain productivity and client satisfaction even as technology changed.

8. Xerox's Lean Six Sigma Success Story Six Sigma

It is another important case study of the Six Sigma project. When Xerox implemented Lean Six Sigma in 2003, the organization underwent a significant transformation. They reduced variance and eliminated waste as they painstakingly optimized internal operations. It improved their operational effectiveness and raised the caliber of their goods and services. Through extensive training programs for staff members, Xerox enabled its employees to spearhead projects aimed at improving different departments and functions. The organization saw significant improvements in customer satisfaction and service performance.

9. A Green Belt Project Six Sigma Case Study

It is one of the best examples of a Six Sigma case study. Anne Cesarone's Green Belt project successfully reduced router configuration time by 16 minutes, a remarkable 55% improvement. Anne maintained router inventory, made improvements to documentation and configuration files, and started router requests sooner by resolving last-minute requests and setup mistakes. The initiative resulted in less router programming time from 29 to 13 minutes, an increase in router order lead time of 11 days, and a 60% drop in incorrect configurations. These raised customer happiness and increased operational effectiveness while proving the benefits of process improvement initiatives.

10. Improving Street Maintenance Payments with Lean Six Sigma

Jessica Shirley-Saenz, a Black Belt at the City of San Antonio, used Lean Six Sigma to address delays in street maintenance payments Lean Six Sigma case study examples. Contractors were experiencing extended payment times, risking project delays and city infrastructure integrity. Root causes included payment rejections and delayed invoicing. By implementing quantity tolerance thresholds, centralizing documentation processes, and updating payment workflows, monthly payment requests increased from 97 to 116. Rejected payments decreased from 17 to 12, reducing the rejection percentage from 58% to 42%, saving $6.6 million.

 Six Sigma's effectiveness spans industries, from healthcare to technology. Case studies demonstrate its ability to optimize processes and improve outcomes. From healthcare facilities streamlining patient care to tech companies enhancing software development, Six Sigma offers adaptable solutions for diverse challenges. These real-world examples illustrate how its methodologies drive efficiency, quality, and customer satisfaction. Professionals can learn valuable lessons from using Six Sigma in healthcare studies, identify strategies to overcome obstacles and facilitate continuous improvement. Organizations can emulate best practices and implement similar initiatives to achieve measurable results by studying successful implementations.

Ready to enhance your skills and advance your career with Six Sigma certification? Join our comprehensive KnowledgeHut's best lean Six Sigma courses to master Six Sigma principles and methodologies. Become a sought-after professional in IT, Manufacturing, Healthcare, Finance, and more industries. Enroll now to accelerate your career growth!

Frequently Asked Questions (FAQs)

Six Sigma case studies are available in various formats and places, such as books, academic journals, professional publications, and Internet sites. Many companies that have effectively adopted Six Sigma publish their case studies on their websites or at industry exhibitions and conferences.

Six Sigma case studies provide insightful information on how businesses have addressed certain issues, enhanced procedures, and produced noticeable outcomes. Professionals gain knowledge about best practices, prevalent errors to avoid, and creative problem-solving methods in several industries and circumstances.

Professionals can share their Six Sigma case studies through industry forums, professional networking platforms, blogs, and social media. They can submit their case studies to publications or at conferences and workshops to reach a wider audience within the Six Sigma community.

Shivender Sharma

Shivendra Sharma, an accomplished author of the international bestseller 'Being Yogi,' is a multifaceted professional. With an MBA in HR and a Lean Six Sigma Master Black Belt, he boasts 15 years of experience in business and digital transformation, strategy consulting, and process improvement. As a member of the Technical Committee of the International Association of Six Sigma Certification (IASSC), he has led multi-million dollar savings through organization-wide transformation projects. Shivendra's expertise lies in deploying Lean and Six Sigma tools across global stakeholders in EMEA, North America, and APAC, achieving remarkable business results. 

Avail your free 1:1 mentorship session.

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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DOI : https://doi.org/10.1038/s41467-024-46346-0

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