Mitigating supply disruption with ordering and supply restoration decisions

• Published in: Computers & industrial engineering Vol. 126; pp. 681 - 690
• Main Authors: Hsieh, Chung-Chi, Putera, Rizki Revianto
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2018
• Subjects: Lot sizing; Mitigating strategies; Restoration capacity; Supply disruption; Restoration capacity; Lot sizing; Supply disruption; Mitigating strategies
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2018.10.025

•We consider a setting with one manufacturer and one supplier subject to disruption.•The manufacturer makes mitigation decisions to cope with the supplier’s disruption.•The manufacturer’s decisions consist of the order size and restoration capacity.•We develop an expected cost minimizing procedure to determine these decisions.•We analyze the parametric effects on the manufacturer’s decisions and expected cost. This study examines the manufacturer’s disruption mitigation decisions when sourcing from a supplier subject to random disruption, and producing products to meet the customer’s periodic demands over a planning horizon. To mitigate the risk of supply restoration failure, the manufacturer plans and makes efforts on a regular basis over the planning horizon. Specifically, the manufacturer adjusts the order size, thereby allowing for more reaction time to restore the supply when the supplier is disrupted. Furthermore, the manufacturer exerts efforts to raise the restoration capacity (lower the mean time to restore), which increases the probability of restoring the supply in time to continue production when the supplier is disrupted. The objective of the manufacturer is to minimize the expected cost that accounts for the inventory of the ordered components, the penalty for supply restoration failure, and the supply restoration efforts, by simultaneously determining the order size and the restoration capacity. Our analysis reveals that although the order size and the restoration capacity usually have a negative correlation as model parameters vary, they may move in the same directions as certain cost-related parameters vary. Furthermore, whether the supplier is likely to incur a disruption greatly influences the manufacturer’s choice of the order size and the restoration capacity and the manufacturer’s priorities in terms of cost improvement efforts.