About

Jan A. Van Mieghem is the A. C. Buehler Professor and Professor of Operations at the Kellogg School of Management of Northwestern University. He received his Ph.D. in Business and MS in Electrical Engineering from Stanford University, and holds an electrical engineering degree from the University KU Leuven, Belgium. His research focuses on product, service, and supply chain operations, studying both strategy and execution. His methodologies include mathematical modeling, stochastic analysis, optimization, financial valuation, empirical estimation, and verification. Current research areas include the operations research of human interaction and collaboration, healthcare data-driven forecasting, and flexibility in dual sourcing. Van Mieghem teaches courses in operations management and operations strategy across MBA, Ph.D., and executive programs, and advises firms on these topics. He has authored over 50 academic articles published in leading international journals and has written two books on operations management and operations strategy. He has held various academic positions, including Deputy Dean since summer 2023, and has served as Director of the PhD program in operations and of non-degree executive programs. His professional experience includes consulting on global strategic sourcing and operations management for firms such as Moen, McKinsey & Company, and Career Builder. Van Mieghem is a distinguished fellow of the Manufacturing and Service Operations Management Society and a member of the Royal Flemish Academy of Sciences and Arts of Belgium. His numerous awards include best paper recognitions, the Wickham Skinner Award, and the Flexibility Excellence Award.

Research topics

• Computer Science
• Mathematics
• Economics
• Artificial Intelligence
• Operations management
• Operations research
• Marketing
• Mathematical optimization
• Finance
• Engineering
• Econometrics
• Microeconomics
• Industrial organization
• Algorithm
• Business

Selected publications

• Nonprofit vs. For-Profit: Allocation of Beds and Access to Care in U.S. Nursing Homes

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• AI in Inventory Management: The Disruptive Era of DRL and Beyond

  Springer series in supply chain management · 2025-09-25 · 1 citations

  book-chapter
  PublisherDOI

• AI in Inventory Management: The Disruptive Era of DRL and Beyond*

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen access
  PublisherDOI

• Planning with Supply Yield Uncertainty: On the Optimality of Linear Policies

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Nonprofit vs. For-Profit: Allocation of Beds and Access to Care in U.S. Nursing Homes

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Last Time Buys during Product Rollovers: Manufacturer & Supplier Equilibria

  Production and Operations Management · 2024-02-05 · 7 citations

  articleSenior author

  Durable goods customers expect long-term availability of spare parts to maintain their product. This puts a strain on supply chains. Part suppliers want to obsolete older generations of parts with declining demand and reassign resources to new, growing generations. Meanwhile, durable goods manufacturers that purchase and sell spare parts to customers want suppliers to keep producing old parts. When suppliers offer a “last time buy” opportunity for legacy parts in anticipation of their retirement, manufacturers push back. We study the strategic interaction and equilibria of a supplier and a manufacturer during the rollover between a legacy part and its successor in a durable good supply chain. Evidence from our interviews shows that manufacturers attempt to use the future business of the new part to delay a supplier’s last time buy of the old part. However, this tactic is often ineffective in practice. We propose a two-stage noncooperative game that reproduces the behaviors observed in interviews. Our analysis of this game distills the conditions under which a manufacturer can leverage the future business to delay a last time buy. There exist only six strategies that achieve this delay. We present a necessary and sufficient condition for these six strategies to be subgame perfect Nash equilibria. The condition reduces to a closed-form threshold on the new part volume. We conclude with practical insights that can guide managers during a rollover discussion.

  PublisherDOI

• Feature-Driven Priority Queuing

  SSRN Electronic Journal · 2024-01-01 · 2 citations

  preprintOpen access
  PublisherDOI

• Dual-sourcing, dual-mode dynamic stochastic inventory models

  Edward Elgar Publishing eBooks · 2023-08-15 · 11 citations

  book-chapterSenior author

  We review academic research on dual-sourcing (or dual-mode) dynamic stochastic inventory models, which study the replenishment of inventory from two sources (or two transportation modes) in the presence of demand uncertainty. The classic assumption, also adopted here, is that the two sources (modes) differ by replenishment lead times and costs. We cover both discrete- and continuous-time models. A feature distinguishing our review from others is that ours is more technically detailed and provides self-contained proofs of several fundamental results in the dual-sourcing literature. We also highlight theory advanced recently by using asymptotic analysis.

  PublisherDOI

• Optimal robust inventory management with volume flexibility: Matching capacity and demand with the lookahead peak‐shaving policy

  Production and Operations Management · 2023-09-11 · 4 citations

  articleOpen accessSenior author

  We study inventory control with volume flexibility: A firm can replenish using period‐dependent base capacity at regular sourcing costs and access additional supply at a premium. The optimal replenishment policy is characterized by two period‐dependent base‐stock levels but determining their values is not trivial, especially for nonstationary and correlated demand. We propose the Lookahead Peak‐Shaving policy that anticipates and peak shaves orders from future peak‐demand periods to the current period, thereby matching capacity and demand. Peak shaving anticipates future order peaks and partially shifts them forward. This contrasts with conventional smoothing, which recovers the inventory deficit resulting from demand peaks by increasing later orders. Our contribution is threefold. First, we use a novel iterative approach to prove the robust optimality of the Lookahead Peak‐Shaving policy. Second, we provide explicit expressions of the period‐dependent base‐stock levels and analyze the amount of peak shaving. Finally, we demonstrate how our policy outperforms other heuristics in stochastic systems. Most cost savings occur when demand is nonstationary and negatively correlated, and base capacities fluctuate around the mean demand. Our insights apply to several practical settings, including production systems with overtime, sourcing from multiple capacitated suppliers, or transportation planning with a spot market. Applying our model to data from a manufacturer reduces inventory and sourcing costs by 6.7%, compared to the manufacturer's policy without peak shaving.

  PublisherOA PDFDOI

• Geographic Virtual Pooling of Hospital Resources: Data-Driven Trade-off Between Waiting and Traveling

  Manufacturing & Service Operations Management · 2023-05-17 · 8 citations

  articleSenior author

  Problem definition: Patient-level data from 72 magnetic resonance imaging (MRI) hospitals in Ontario, Canada from 2013 to 2017 show that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the patient fraction exceeding (FET) target for MRI services through geographic virtual resource-sharing while limiting incremental driving time. We present a data-driven method to solve the geographic pooling problem of partitioning 72 hospitals with heterogeneous patients with different wait time targets located in a two-dimensional region into a set of clusters. Methodology/results: We propose an “augmented-priority rule,” which is a sequencing rule that balances the patient’s initial priority class and the number of days until her wait time target. We then use neural networks to predict patient arrival and service times. We combine this predicted information and the sequencing rule to implement “advance scheduling,” which informs the patient of her treatment day and location when requesting an MRI scan. We then optimize the number of geographic resource pools among the 72 hospitals using genetic algorithms. Our resource-pooling model lowers the FET from 66% to 36% while constraining the average incremental travel time below three hours. In addition, our model shows that only 10 additional scanners are needed to achieve 10% FET, whereas 50 additional scanners would be needed without resource sharing. Over 70% of the hospitals are not worse off financially. Each individual hospital, measured over at least two weeks, achieves a higher machine utilization and a lower FET. Managerial implications: Our paper provides a practical, data-driven geographical resource-sharing model that hospitals can readily implement. Our method achieves a near-optimal solution with low computational complexity. Using smart data-driven scheduling, a little extra capacity placed at the right location is all we need to achieve the desired FET under geographic resource-sharing. Funding: This paper is supported by the following grant: Canadian Institutes of Health Research (CIHR) [Grant CIHR-950-231935]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1225 .

  PublisherDOI

Frequent coauthors

• Robert Boute

  47 shared

• Itai Gurvich

  Northwestern University

  30 shared

• Joren Gijsbrechts

  26 shared

• Stephen Michael Disney

  University of Exeter

  19 shared

• Daniel Diermeier

  Vanderbilt University

  14 shared

• Dennis Zhang

  Washington University in St. Louis

  11 shared

• Gad Allon

  University of Pennsylvania

  9 shared

• Lauren Xiaoyuan Lu

  8 shared

Labs

• OperationsPI

Education

• PhD, Graduate School of Business

  Stanford University

  1995

• MS, Electrical Engineering

  Stanford University

  1990

• Burgerlijk Ingenieur, Electrical Engineering

  KU Leuven

  1989

Awards & honors

• First MSOM Best Paper Award (2007)
• Wickham Skinner Award for Best Paper Published in POM (2014)
• NU Excellence in Research (2012)
• 2023 Student Paper Competition First Place, College of Suppl…
• 2020 Flexibility Excellence Award, Global Institute of Flexi…