About

Doug Thomas is the Academic Director of the Sands Institute for Lifelong Learning and the Henry E. McWane Professor of Business Administration at the Darden School of Business. He holds a B.S. in operations research from Cornell University, and both an M.S. and Ph.D. in industrial engineering from the Georgia Institute of Technology. Prior to joining Darden, he was a professor of supply chain management at Penn State's Smeal College of Business, where he also served as the faculty director of the MBA program from 2011 to 2014. He has served as a visiting faculty member at INSEAD, the Johnson Graduate School of Management at Cornell University, and the Darden School at the University of Virginia. His teaching areas include supply chain management and quantitative modeling across MBA, Executive MBA, and Ph.D. programs. Thomas is a co-founder and chief scientist of Plan2Execute, a firm providing supply chain software and consulting solutions. His research interests focus on coordinating production and inventory planning across the extended enterprise and connecting decision models to logistics performance measurement. He has published in academic and practitioner journals such as Management Science, Manufacturing and Service Operations Management, and Production and Operations Management, and serves as a senior editor for Production and Operations Management.

Research topics

• Computer Science
• Microeconomics
• Business
• Economics
• Industrial organization
• Marketing
• Mathematics
• Econometrics
• Finance
• Statistics
• Operations research

Selected publications

• Ecommerce Brands: Digital Scale

  SSRN Electronic Journal · 2023-01-01

  articleOpen access
  PublisherDOI

• Counterfeits and E-Commerce: Why Even Build it?

  SSRN Electronic Journal · 2022-01-01

  articleOpen access
  PublisherDOI

• Blue Apron: Has the Supply Chain Disrupter Been Disrupted?

  SSRN Electronic Journal · 2022-01-01

  articleOpen access
  PublisherDOI

• Regulations and Standards: Electronics Supply Chain

  SSRN Electronic Journal · 2022-01-01

  articleOpen access
  PublisherDOI

• Fairphone (A): Can a Start-Up Change an Industry?

  SSRN Electronic Journal · 2022-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• A nonparametric approach for setting safety stock levels

  Production and Operations Management · 2022-11-27 · 10 citations

  articleSenior author

  In practice, lead time demand (LTD) can be skewed, multi‐modal or highly variable, and these factors compromise the validity of typical approaches used for setting safety stock levels. Motivated by encountering this problem at our industry partner, we develop an approach for setting safety stock levels using the bootstrap, a widely used statistical procedure. Existing bootstrap approaches for inventory management either operate directly on observed LTD or assume deterministic lead times, permitting direct application of the bootstrap approach for univariate quantile estimation. As LTD is a convolution of multiple random demands over a random lead time, a multivariate bootstrap approach is required. As we demonstrate, when lead times are stochastic, our multivariate approach provides improved safety stock estimates. We develop a multivariate central limit theorem for the bootstrap mean and bootstrap quantile—components of the safety stock calculation—highlighting why the generalization of these bootstrap methods is critical for inventory management. These results provide a theoretical underpinning for the bootstrap estimator of safety stock and permit the construction of confidence intervals for safety stock estimates, allowing decision makers to understand the reliability with which the desired service level will be achieved. Building on our theoretical results, and supported by numerical experiments, we provide insights on the behavior of the bootstrap for various LTD distributions, which our results demonstrate are critical when employing the bootstrap method. Implementation of our approach with our industry partner resulted in an inventory investment reduction of $1.17 million combined with an overall increase in service level. Our approach is general and can be implemented without modification in other settings.

  PublisherDOI

• Electronics Supply Chain Overview

  SSRN Electronic Journal · 2022-01-01

  articleOpen access
  PublisherDOI

• Retailer Inventory Sharing in Two-Tier Supply Chains: An Experimental Investigation

  Management Science · 2022 · 26 citations

  Senior authorCorresponding
  • Computer Science
  • Business
  • Industrial organization

  When multiple retailers hold inventory to satisfy random demand, retailer inventory-sharing strategies can potentially reduce the supply-demand mismatch and increase overall supply chain performance. In this paper, we experimentally investigate alternative inventory-sharing strategies in a two-tier supply chain with an upstream manufacturer and two downstream retailers. In one setting, retailers act as if they are centralized and use a single quantity to fulfill joint demand. In the other, retailers are decentralized and face separate demands, but they can transfer inventory after demands are realized. In this latter decentralized scenario, we also consider whether the manufacturer or retailers have decision authority over the inventory transfer price. One key result is that when the retailers are decentralized and the manufacturer sets the transfer price, both retailers and the manufacturer earn higher profits than in the centralized retailer strategy, which runs counter to theory. We also find that when retailers are decentralized and set their own transfer price, the most equitable distribution of profits is achieved. In an effort to account for these results, we find that a model of fairness captures decisions well. Overall, by investigating how different inventory-sharing strategies affect the distribution of profits in a two-tier supply chain, our results provide guidance to firms considering how, if at all, they should enter such arrangements. This paper was accepted by Jay Swaminathan, operations management. Funding: The authors acknowledge financial support from Cornell University and the University of Virginia. Supplemental Material: The data files and electronic companion are available at https://doi.org/10.1287/mnsc.2022.4323 .

  PublisherDOI

• Backcountry.Com and Leveraging Data Analytics

  SSRN Electronic Journal · 2022-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Food Supply and COVID-19: Breaking the Chain

  SSRN Electronic Journal · 2021-01-01

  articleOpen access
  PublisherDOI

Frequent coauthors

• John E. Tyworth

  11 shared

• Edward A. Silver

  University of Michigan–Ann Arbor

  9 shared

• David F. Pyke

  University of San Diego

  9 shared

• Christopher W. Craighead

  University of Tennessee at Knoxville

  7 shared

• Stephen E. Maiden

  6 shared

• Michael Freimer

  6 shared

• Vidya Mani

  University of Virginia

  6 shared

• Timothy M. Laseter

  Universidad de Navarra

  5 shared