QMOD-ICQSS

John Oakland

Quality 4.0 – concept definition, principles & practice

Professor Oakland is Chairman of The Oakland Group (www.weareoakland.com) and Head of its Research and Education Division, The Oakland Institute. He is also Emeritus Professor of Business Excellence at Leeds University Business School.

Over more than forty years he has worked with top management in all aspects of quality, business excellence, performance improvement, and more recently in data analytics, in literally thousands of organisations. He has directed several large research projects in Europe, funded by the British Government and EU programmes, and the Chartered Quality Institute, which have brought him into contact with a diverse range of organisations. His work has been widely acknowledged and published.

He is author of several books, including the bestselling: Total Organisational Excellence, Total Quality Management and Operational Excellence 5ed, Total Construction Management and Statistical Process Control & Data Analytics 8ed, all published by Routledge, and Production and Operations Management published by Pearson. He has written literally hundreds of papers, articles and reports on various topics in these fields.

Professor Oakland is a Fellow of the Chartered Quality Institute, Fellow of the Royal Statistical Society, an Honorary Life Member of the Research Quality Association, Member of the American Society for Quality and a Fellow of the Cybernetics Society.

KEYNOTE:

Business and organisations are continuing to evolve out of necessity, responding to an onslaught of disruption, new business models and technology. This continuous change, including that precipitated by the global coronavirus pandemic, is affecting operations at all levels, with customers demanding real-time interactions, regulators applying increasing levels of scrutiny and governance, and stakeholders requiring assurance in this complex and dynamic risk environment. The technology revolution that has been called ‘Industry 4.0’ and ‘Digital Transformation,’ and includes Artificial Intelligence, Machine Learning and robotics, has exposed weaknesses in the traditional approaches to quality management and questions have been raised whether they are still relevant and efficient.

Digitalisation and the highly connected nature of global systems means that organisations now operate in a complex and tightly coupled environment. A single flaw in one part of the system can rapidly cause catastrophic systemic failures. In contrast the impact of the same significant flaws could perhaps more easily be contained in previously complex but loosely coupled systems. Tight coupling is being increased by Industry 4.0 related technologies.

What is clear is that business models will continue to change dramatically over the next three to five years, and business/organisation leaders will need to focus on aggressively seeking out opportunities to innovate within rapidly changing and increasing risks. As the risk landscape becomes more complex and fast-moving, it will be crucial for organizations to identify and respond quickly and effectively to emerging events and risks. If the so called ‘Quality 4.0’ (Q4.0) is to be part of the solution, it needs to be properly understood, defined and developed to play a key role in helping organisations manage during this evolution.

The CQI commissioned a consortium comprising Leeds University Business School and The Oakland Institute for Business Research and Education to carry out research into the concepts and principles that underpin the evolution of Q4.0. The focus was to review existing literature and consult an appropriate sample of industry leader and practitioner views with the objective of developing an understanding of Q4.0 that would form the basis for further research into the practices and tools that quality professionals will need to thrive in this emerging world. This Keynote address will present the initial findings of that research.

Robin Mann

The Power of Benchmarking for Real Business Impact: Improving People, Processes, Products, and the Planet

Dr. Robin Mann is the Head of the Centre for Organisational Excellence Research (COER), New Zealand, Chairman of the Global Benchmarking Network, and a Board Member of the New Zealand Business Excellence Foundation. He is also the Co-Founder of BPIR.com Limited, a leading best practice resource website, and the Founder of both the International Best Practice Competition and the TRADE Best Practice Benchmarking Methodology. Robin leads major benchmarking initiatives, facilitating over 50 benchmarking projects for the Dubai Government in recent years and supporting hundreds of projects worldwide. He has served as a Business Excellence and Productivity Advisor to numerous countries and published over 50 peer reviewed journal papers on business excellence. Originally from the UK, he earned his PhD in Total Quality Management (TQM) from Liverpool University in 1992.

KEYNOTE:

Benchmarking is a powerful tool for driving breakthrough thinking, fostering innovation, and achieving exceptional performance. At its core, benchmarking is about learning from the experiences of others and applying that knowledge to enhance organizational performance.

This paper highlights two major initiatives that showcase the impact of benchmarking from both formal and informal perspectives. The first initiative demonstrates a structured benchmarking approach used by organizations to undertake in-depth benchmarking projects, while the second illustrates how informal benchmarking facilitates the rapid exchange of best practices.

The first initiative focuses on a Dubai Government program that trains government entities in systematic benchmarking. Through a year-long program, participants are mentored in applying the TRADE Best Practice Benchmarking Methodology, which consists of five key stages: Terms of Reference, Review Current State, Acquire Best Practices, Deploy Best Practices, and Evaluate Outcomes. The acronym "TRADE" underscores the importance of strong, two-way partnerships for the mutual exchange of valuable knowledge. On average, each project generates between 50 to 120 ideas and best practices, with the most promising ones selected based on feasibility and expected impact. This paper reviews over 40 TRADE Benchmarking Projects in Dubai, tackling challenges ranging from COVID-19 response to reducing household electricity consumption to creating a next generation airspace. A 2024 impact study revealed that TRADE projects generated an average of US$3.9 million in savings or revenue for the government or society.

The second initiative highlights the International Best Practice Competition (IBPC), a platform for sharing and learning best practices in a simple yet effective format. The IBPC invites organizations of all sizes to present their best practices through a five-page written application, an eight-minute video presentation, and a ten-minute Q&A with a jury. The competition provides a fast, engaging, and structured way to exchange best practices, helping organizations learn from each other while receiving feedback for improvement. Practices are assessed using a star-rating system, with 5-star and above practices recognized as international best practices of potential interest to organizations worldwide. Since 2012, over 800 best practices have been shared through the IBPC. The competition's success has inspired several countries to establish national competitions, and many large organizations have adopted similar internal best practice-sharing frameworks.

By showcasing these initiatives, this paper underscores how both formal and informal benchmarking can drive innovation, efficiency, and performance excellence across organizations, industries, and nations.

Yamada Shu

What should and should not be changed on
Quality Management in the age of AI

Dr. Shu Yamada is a professor at Keio University. He earned his Doctor of Engineering (1993) and Master of Engineering (1989) from Tokyo University of Science under the mentorship of the renowned Prof. Noriaki Kano. Dr Yamada is an internationally recognized expert in quality management, statistical quality control, and social systems engineering. His prolific research has resulted in over 50 peer-reviewed publications and 10 books, with notable recognition including the prestigious Nikkei Quality Control Literature Prizes in 1993, 1997, 1999, and 2005. Additionally, he has served as the Editor in Chief of Total Quality Science and played key editorial roles in respected journals such as the Journal of the Japanese Society for Quality Control (JSQC), Quality Engineering, and Asian Journal on Quality. As a leading authority in his field, Dr. Yamada has contributed to global standard-setting initiatives, serving on committees such as the Deming Prize, ISO TC 176 (Quality Management), and IEC TC 111 (Environmental Conscious Design). His expertise has been instrumental in advancing international quality standards and practices. Beyond academia, Dr. Yamada has collaborated extensively with industry, promoting Total Quality Management (TQM), developing innovative management systems, and providing specialized training tailored to organizational needs.

See more detail Bio >> https://k-ris.keio.ac.jp/html/100011509_en.html

KEYNOTE:

If we consider the proposal of control chart by Shewhart as the origin of Quality Management, approximately 100 years have passed since this proposal was made. In order to adapt to changes in the business environment, Quality Management has developed various basic principles such as customer orientation and process approach. It has also produced a variety of activities such as improvement, all member participation, daily management, policy management and so forth. Recent developments in AI contribute to the evolution of Quality Management in various ways. For example, it is expected to be applied to improvement activities and to the search for potential customer requirements. On the other hand, there are some things that should not be changed in the age of AI. In this keynote speech, it will first be reviewed the history of the development of quality management to date. It will be explained the basic principles, activities, and organizational promotion related to Quality Management that have been developed so far, what should be changed because of the age of AI, and what should not be changed even in the age of AI.

Moontae Lee

Reasoning, Alignment, and Creativity: The Triptych of Superintelligence

Dr. Moontae Lee is a Senior Research Fellow at LG Corporation and the Head of the Superintelligence Laboratory (S Lab) at LG AI Research. Concurrently, he serves as an Assistant Professor in Information and Decision Sciences at the University of Illinois Chicago. Prior to joining LG, he was an invited scholar at Microsoft Research in Redmond from 2019 to 2021.

Over the past five years, Dr. Lee has contributed to the advancement of Generative AI, spanning research from foundational representation learning to cutting-edge architectures and algorithmic innovations. His extensive research includes over 50 influential publications to improve Large Language Models (LLMs), such as text generation, planning, evaluation, and reasoning. Dr. Lee's pioneering work on Retrieval-Augmented Generation and methods to enhance factuality and coverage has been integrated as core agent services across various LG affiliates. He is currently developing Large Reasoning Models to establish a unified formalism capable of tackling text, code, mathematical problems, and other complex domains. Winning at the Amazon Research Symposium in 2017, Dr. Lee has investigated business intelligence and societal implications. His work utilizing causal inference and counterfactual evaluations on interactive systems have provided critical insights into behavioral biases within social processes operations. He addresses crucial challenges such as information monopoly and evaluation fairness to promote meritocracy in online communities. Furthermore, his contributions to the ethics and safety of AI have deepened cultural understanding behind generative processes and the alignment algorithms between human preferences and societal values. This notable impact was recognized with the Social Impact Award from the North American Association of Computational Linguistics in 2024.

Dr. Lee remains actively engaged with the academic community, serving as an Area Chair for prestigious international AI conferences, including ACL, NAACL, EMNLP, and COLM.

Forrest Breyfogle

How to Reduce AI Implementation Risks
: A Smarter Strategy for Business Success

Forrest Breyfogle is a professional engineer, an ASQ fellow, and the CEO of Smarter Solutions, Inc. (https://smartersolutions.com). He has authored or co-authored over fifteen books about enhancements to Lean Six Sigma process improvement techniques, KPI performance metric reporting, and business management techniques.

His books, Management 2.0 and Leadership 2.0, offer an enhanced Integrated Enterprise Excellence (IEE) system, accompanied by free predictive process output metric reporting software, in a novel book format.

His books offer radical management advancements in utilizing and integrating scorecards, strategic planning, and process improvement.

Mr. Breyfogle was named Quality Professional of the Year for 2011 by Quality Magazine and, in 2012, received the Alumni of the Year award from the Missouri University of Science and Technology. He also received the prestigious Crosby Medal from ASQ in 2004 for an earlier book, Implementing Six Sigma, 2nd edition. He received the Leadership Award at the 2013 Lean & Six Sigma World Conference.

Forrest has given numerous keynote presentations on Lean Six Sigma, business systems, and their metrics worldwide.

>> See more details about Smarter Solutions Inc.

KEYNOTE:

As organizations invest heavily in artificial intelligence (AI) to enhance operations and decision-making, many face underwhelming results due to weak implementation foundations. Common pitfalls include siloed projects, inconsistent metrics, and lack of strategic alignment.

Effective AI requires a deep understanding of business processes, goals, and performance indicators. Without this context, AI insights remain fragmented and fail to deliver organization-wide value.

This presentation introduces the Integrated Enterprise Excellence (IEE) System as an ideal foundation for AI Implementation 2.0. IEE offers:

Enterprise-wide process visibility and standardized metrics
Predictive 30,000-foot-level KPI reporting
Alignment of AI and improvement efforts with strategic goals

By embedding AI within the IEE framework, businesses can ensure that AI initiatives are impactful, integrated, and sustainable.

In the race to gain a competitive advantage, organizations are rapidly investing in artificial intelligence (AI) technologies.

From machine learning to natural language processing, AI promises to revolutionize how companies operate, make decisions, and deliver value to their customers. However, many businesses are discovering that implementing AI without a strong foundational framework often leads to siloed projects, disjointed insights, and disappointing returns.

Common Issues with current AI Implementation practices:

Siloed deployments that don’t integrate with enterprise-wide goals
Lack of standardization in processes and data definitions
Inconsistent metric reporting, making enterprise-level decision-making difficult
Reactive AI usage rather than proactive, strategic deployment

For AI to be truly transformative, it must have a deep understanding of the business — its workflows, objectives, constraints, and opportunities. In other words, it needs a business-centric foundation.

AI models are only as powerful as the data and context they are given. When implemented in a vacuum, they generate insights that may improve a piece of the puzzle but not the entire picture.

To avoid these pitfalls, organizations must first build a foundation that:

Maps out (from a system-wide perspective) enterprise processes with their performance metrics
Establishes predictive performance metrics improvement objectives, which have alignment with strategic goals
Provides 24 x7 accessible, real-time insights across the business
Enables AI to recommend improvements that align with big-picture priorities

The Integrated Enterprise Excellence (IEE) System provides an AI Implementation 2.0 (Quality Management System) foundation, offering a modern, strategic, and integrated approach that sets the stage for enterprise-wide success.

The AI Implementation 2.0 foundation of IEE provides:

A system-wide view of processes, goals, and metrics
Predictive 30,000-foot-level reporting for KPI and process output metric responses that identify performance trends with confidence
A clear roadmap for aligning improvement efforts with enterprise objectives
A platform for AI to generate meaningful, organization-wide value