Dynamic compensation and contingent sourcing strategies for supply disruption

Abstract

Alternative measures to deal with supply disruptions exist. We consider a make-to-order (MTO) supply chain with one manufacturer who sources from a single supplier. When a supply disruption occurs, the manufacturer can choose to satisfy some demand by either maintaining production through safety stocks or through a secondary contingent source, and turn some unmet demand into backorders on the basis of compensation. An optimal control model under consideration of the customers’ dynamic reactions to the joint implementation of these strategies is formulated with the objective of minimising the cost of disruption. Through the application of Pontryagin's Maximum Principle, optimal mitigation strategies are established in closed form. They provide analytical guidance on how to dynamically and jointly adapt the quantity of contingent sourcing, the price of compensation, and the speed of safety inventory consumption. The results indicate how cost and time-related factors impact these strategies. We also demonstrate that pure strategies are only effective in tackling short supply shortages. For long disruptions, it is superior to adopt combined strategies that simultaneously incorporate two countermeasures in certain periods.

KEYWORDS:

• Supply disruption
• contingent sourcing
• compensation
• safety inventory
• control theory

Acknowledgement

This work was done when the first author stayed at the Technical University of Munich as a visiting Ph.D. The authors sincerely thank the editors and three anonymous referees for their numerous insightful comments that greatly improved the paper.

This work is supported by National Nature Science Foundation under Grant (Nos.71771053, 71371003 and 72001113) and the Key Research and Development Plan (Modern Agriculture) of Jiangsu Province (No. BE2018385).

Disclosure statement

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

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71371003, 71771053, 72001113]; Key Research and Development Plan (Modern Agriculture) of Jiangsu Province, China: [Grant Number BE2018385]; Applied Economics of Nanjing Audit University of the Priority Academic Program Development of Jiangsu Higher Education Institutions, China: [Grant Number [2018] 87 (Office of Jiangsu Provincial People’s Government)].

Notes on contributors

Shanshan Li

Shanshan Li received the Ph.D. degree in Management Science and Engineering from the School of Economics and Management, Southeast University (China). She is currently a Lecturer at the School of Finance, Nanjing Audit University. Her research interests include supply chain risk management, operations planning and control, and supply chain financing.

Yong He

Yong He is a professor at School of Economics and Management, Southeast University, China. His research interests include supply chain management, logistics management, marketing/OM interfaces, food supply chain, and service science.

Stefan Minner

Stefan Minner is a Full Professor for Logistics and Supply Chain Management at the School of Management, Technical University of Munich (TUM). Currently, Stefan Minner is the Editor-in-Chief of the International Journal of Production Economics. His research interests are in business analytics, global supply chain design, transportation optimisation and inventory management.