JOM Special Issue on "Uncertainty in Operations and Supply Chain Management"

I am happy to be involved in a Special Issue for the Journal of Operations Management, one of the top journals in my field. I am excited to be working with a couple of dear, old friends; Jan Holmström (from Aalta University, Finland) and Chris Tang (UCLA, USA), and a couple of new friends; Frits Pil (Pittsburg, USA) and Benn Lawson (Cambridge, UK) on this project.

The special issue calls for papers concerned with coping with known, unknown, and unknowable uncertainties. Much of my bullwhip research has been concerned with known uncertainties – we assume previous demand is known and previous demand is a good predictor of future events. Some of my research as considered distribution free problems and my favorite lecture (on Decision Making Under Uncertainty, inspired by my friend Jack Hayya from Penn State), is concerned with a subset of known uncertainties. However, very little OM and SCM research is concerned with unknowable unknown uncertainties. Given the recent events such as COVID, global geopolitical tensions, climate change, truck driver shortages and gas price hikes, strategies that cope with, avoid, sense and respond, or find new opportunities to benefit from these uncertainties are needed. Our special issue calls for papers that address these issues.

Our call for papers for the JOM special issue can be found here. There is a deadline of the initial manuscript of 30th September 2022. Manuscripts will be evaluated as soon as they are received, with first-round decisions no later than December 2022. Revisions will be due on or before March 31, 2023, and final decisions will be made by June 2023.

If you are working on problems at the frontier of knowledge on known, unknown, and/or unknowable uncertainties, please consider submitting your paper to our special issue.

Stephen Disney
Stephen Disney

My research interests involve the application of control theory and statistical techniques to operations management and supply chain scenarios to investigate their dynamic, stochastic, and economic performance.