How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study

Abstract

Generative Artificial Intelligence (AI) models such as OpenAI’s ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT’s ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

Keywords:

• Artificial intelligence
• innovation engineer
• large-language models
• prompt engineering

Additional information

Notes on contributors

Fadel M. Megahed

Fadel M. Megahed is a Miami University Faculty Scholar and the Endres Associate Professor in the Department of Information Systems & Analytics at Miami University in Oxford, Ohio. He received his Ph.D. and M.S. in Industrial and Systems Engineering from Virginia Tech and a B.S. in Mechanical Engineering from the American University in Cairo. His research interests include statistical process monitoring and applied machine learning with applications in manufacturing, healthcare, and occupational safety. His work in these areas has been funded by the National Institute for Occupational Safety and Health (NIOSH), the National Science Foundation (NSF), the American Society of Safety Professionals (ASSP) Foundation, and several industrial partners. Dr. Megahed is the Editor of the Case Study Section for the Journal of Quality Technology.

Ying-Ju Chen

Ying-Ju (Tessa) Chen is an Assistant Professor in the Department of Mathematics at the University of Dayton, Ohio. Her research interests focus on Statistical Modeling and Data Science applications in manufacturing, healthcare operations, and transportation safety. Specifically, she is dedicated to working on research related to people's daily lives and well-being.

Joshua A. Ferris

Joshua A. Ferris is a Miami University Visiting Assistant Lecturer in the department of Information Systems & Analytics at Miami University in Oxford, Ohio. He enjoys assisting non-profits with technology-related problems such as website development. He received a B.S. in Mathematics from the University of York and an M.S. in Business Analytics from Miami University.

Sven Knoth

Sven Knoth is a Professor of Statistics in the Department of Mathematics and Statistics within the School of Economic and Social Sciences at the Helmut Schmidt University, Hamburg, Germany. Prior to that, he worked as a Senior SPC Engineer at Advanced Mask Technology Center (AMTC) Dresden, Germany, from 2004 to 2009. He is an Associate Editor of Computational Statistics and Editorial Board member of Journal of Quality Technology and Quality Engineering. He received a Diploma (equivalent to M.S.) and a Ph.D. both in Mathematics from the Technical University Chemnitz, Germany.

L. Allison Jones-Farmer

L. Allison Jones-Farmer is the Van Andel Professor of Business Analytics at Miami University in Oxford, Ohio. Her research focuses on developing practical methods for analyzing data in industrial and business settings. She is the current Editor-in-Chief of Journal of Quality Technology; is on the editorial board of Quality Engineering and is a former Associate Editor for Technometrics. In addition to her research in industrial analytics, Allison enjoys helping organizations improve their analytics capability, developing innovative curricula, and teaching data science. Prior to joining Miami University, Allison was a Professor of Statistics and Analytics at Auburn University where she held the C&E Smith chair. She received a B.S. in Mathematics from Birmingham-Southern College, an M.S. in Applied Statistics from the University of Alabama, and a Ph.D. in Applied Statistics from the University of Alabama.