Incorporating Cost Risk in Supplier Selection for Long Term Contracts

Abstract

Purchasing firms naturally select the supplier with the lowest total cost. Often overlooked is the impact of the duration of long-term contracts (common in the automotive industry). We provide a method to quantify how risk escalates with contract duration. Our method applies to virtually all cost elements. For clarity, we demonstrate it on logistics costs that can change over a long horizon, resulting in a change in the relative attractiveness of candidate suppliers. We simulate a case study to estimate the likelihood that each supplier will provide the lowest cost over the contract duration. Finally, we quantify the risk reduction afforded by dividing a long contract into a sequence of shorter contracts.

Acknowledgements

We thank an anonymous reviewer for very thoughtful suggestions. We are also grateful to Andrea Budzynski, Michael Harbaugh and Peiling Wu-Smith for their useful insights.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Robert R. Inman

Robert Inman is a Technical Fellow at General Motors. After earning a PhD in Industrial Engineering and Management Sciences from Northwestern University, he taught Industrial Engineering at Auburn University for two years. He joined General Motors in 1989 where he developed and implemented numerous industrial and systems engineering innovations in the areas of manufacturing systems, quality, supply chain, inventory control, and GM’s network of franchised dealers who sell and repair vehicles. While at GM he has also served as a faculty advisor at the University of Michigan Tauber Institute for Global Operations for eleven years.

Maya Bam

Maya Bam is a Staff Researcher at General Motors. She completed her Ph.D. and M.S. in Industrial and Operations Engineering at the University of Michigan and her B.Sc. in Mathematics at Gordon College. She joined General Motors in 2017, where she has been focusing on developing well-performing fast approximation methods for computationally challenging problems that arise in consumer choice modeling and supply chain and logistics optimization.