About

John Fowler is the Motorola Professor of Supply Chain Management in the W.P. Carey School of Business at Arizona State University. He has served as the department chair of supply chain management from 2011 to 2016 and previously held the position of Avnet Professor of Industrial Engineering at ASU. His research interests include discrete event simulation, deterministic scheduling, and multi-criteria decision making. Professor Fowler has published more than 120 journal articles and over 100 conference papers, contributing significantly to the fields of operations research, healthcare systems engineering, and semiconductor manufacturing systems. He has held editorial roles such as editor-in-chief for IIE Transactions on Healthcare Systems Engineering and continues as the department editor for Healthcare Operations Management, along with other editorial positions. He is a fellow of the Institute of Industrial and Systems Engineers and has served in leadership roles including vice president for continuing education, INFORMS vice president, and member of the Winter Simulation Conference board of directors. His academic background includes a Ph.D., M.S., and B.S. in Industrial Engineering from Texas A&M University.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Mathematical optimization
• Engineering
• Industrial engineering
• Distributed computing
• Operating system
• Risk analysis (engineering)
• Algorithm
• Business
• Data science
• Management science
• Operations management
• Medicine
• Operations research

Selected publications

• EVALUATING POOLED PROCUREMENT AND INVENTORY MANAGEMENT FOR ESSENTIAL MEDICINES IN NIGERIA

  2025-03-20

  articleOpen access

  This study examines how pooled procurement and a shared stock system across four states in an African country can improve access to essential medicines. Our analysis begins with a review of existing practices and introduces a pooled procurement approach integrated with a Continuous Review Policy (CRP). By comparing the current inventory policies of states with CRP model, we explore potential benefits, including cost reduction and improved stock management. We then designed two simulation scenarios: states following the CRP model independently, and states following CRP with access to shared stock. Both scenarios were tested under normal demand and a 50% surge in demand in one state. The results demonstrate the significant advantages of pooled procurement and shared stock systems, particularly in mitigating the impact of demand spikes and improving inventory availability.

  PublisherDOI

• A Foundational Framework for Generative Simulation Models: Pathway to Generative Digital Twins for Supply Chain

  2025-12-07

  article
  PublisherDOI

• Simulation in Semiconductor Manufacturing Between 2000 and 2050 Lessons Learned and Future Expectations

  2025-12-07

  article1st authorCorresponding
  PublisherDOI

• Operationalizing entropy in logistics to mitigate congestion: application to a semiconductor wafer fabrication facility layout

  International Journal of Production Research · 2025-07-08

  articleSenior authorCorresponding
  PublisherDOI

• EPH112 Prevalent Comorbidities and Disease-Related Conditions in Heavily Pre-Treated Patients With Multiple Myeloma: A Real-World Retrospective Database Analysis

  Value in Health · 2024-06-01

  articleOpen access
  PublisherOA PDFDOI

• When Complexity Meets Complexity: <scp>COVID</scp>‐19‐Induced Supply Chain Disruptions and Strategy Portfolio Efficiency

  Journal of Operations Management · 2024-12-29 · 14 citations

  articleOpen access

  ABSTRACT Due to the COVID‐19 pandemic, global supply chains have experienced sustained impacts from unprecedented complex disruptions in different combinations and at different times. From an efficiency perspective, do these complex supply chain disruptions call for more complex risk management strategies? To answer this, we built an empirically grounded discrete event simulation model, the results of which were analyzed using data envelopment analysis. Results show that with unprecedented complex disruption patterns, a multi‐strategy portfolio approach is usually less efficient than a single‐strategy or a do‐nothing approach unless the strategy portfolio has certain characteristics. The most efficient strategy portfolios typically consist of a moderate number of diverse strategies. Too many strategies in a portfolio can be problematic, leading to increased costs that outpace improvement in revenue and service level. Results illustrate that even a strategy that generally performs poorly can be part of a very good strategy portfolio and vice versa. This study provides nuanced and novel findings that contribute to the resolution of the literature debate about the value of multi‐strategy portfolios in addressing complex disruption patterns. Highlighting the value of a strategy portfolio view, these insights help firms better prepare for the next complex and sustained global supply chain disruptions.

  PublisherOA PDFDOI

• Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap

  Journal of Industrial Information Integration · 2024-03-12 · 63 citations

  reviewOpen access

  Production Scheduling (PS) is an essential paradigm within supply and manufacturing systems and an important element of sustainable development. PS, mainly known for its horizontal effects within the operational decision level, directly impacts both tactical and strategical levels of decision-making. In other words, an optimally designed and utilized PS module could bring efficiency towards the whole supply chain network of many manufacturing systems. Simulation Optimization (SO), as a growing Decision Support Tool (DST), provides a methodology required to drastically improve the efficiency of industrial systems. Thus, in this article, we review the existing research on SO Applied to PS (SOAPS), within the context of wider adaption of Industry 4.0 (known as the fourth industrial revolution). Firstly, relevant articles are examined and reviewed to position the research and develop research questions that enable the highlighting of research gaps. Then, a methodology was created based on: the studied PS problem features, proposed optimization frameworks, executed simulation tools, the SO architectures and the experimentation and validation strategies used. Finally, we investigate how Industry 4.0 could enhance the existing research on SOAPS to provide real-time and efficient SO-based DSTs for PS modules within modern manufacturing systems.

  PublisherDOI

• Devising and implementing the learning plan

  2024-09-12

  book-chapter1st authorCorresponding

  Chapter 7 explained how feedback could be provided to an individual learner following observation of clinical practice. In this chapter how best to bring about improved consultation performance from the perspective of the educator and the institution is considered. It is aimed at teachers, but all readers will find it useful to understand the principles described.

  PublisherDOI

• Guest Editorial Special Section on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing

  IEEE Transactions on Semiconductor Manufacturing · 2023-10-30 · 1 citations

  editorialOpen access1st authorCorresponding

  The increasing availability of data, advances in computational and storage capacities of IT systems, and algorithmic advances in Artificial Intelligence (AI), especially Machine Learning (ML) combine to enable significant improvements in the efficiency, operations and throughput of manufacturing systems at the production level. The semiconductor industry is one of the most data-intensive industries and has seen increased use of AI-based technologies over the last few years. In order to develop effective AI-based technologies in the semiconductor manufacturing industry several issues have to be taken into account, including scalability, heterogeneity of data, and the need for interpretability.

  PublisherOA PDFDOI

• Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains

  IEEE Transactions on Semiconductor Manufacturing · 2023-10-13 · 10 citations

  article

  This is a panel paper that discusses the use of Artificial Intelligence (AI) technologies to address production and supply chain level problems in semiconductor manufacturing. We have gathered a group of expert semiconductor researchers and practitioners from around the world who have developed AI solutions for various semiconductor problems. This paper aims to provide their answers to an initial set of questions and provide an overview of the AI developments and empirical studies to make suggestions for future directions in this arena.

  PublisherDOI

Recent grants

• Collaborative Research: Optimization of the Design and Operation of Surgery Delivery Systems

  NSF · $120k · 2006–2010

Frequent coauthors

• Lars Mönch

  49 shared

• Scott J. Mason

  Amazon (United States)

  38 shared

• Michele E. Pfund

  38 shared

• Gerald T. Mackulak

  Arizona State University

  32 shared

• Esma S. Gel

  University of Nebraska–Lincoln

  26 shared

• Barry L. Nelson

  Northwestern University

  16 shared

• Özgür M. Araz

  University of Nebraska–Lincoln

  16 shared

• Dan Shunk

  CETYS Universidad

  16 shared

Education

• Ph.D.

  Texas A&M University

  1990

• M.S.

  Texas A&M University

  1986

• B.S.

  Texas A&M University

  1982

Awards & honors

• Fellow of the Institute of Industrial and Systems Engineers