4.7 Article

Optimal Lot-Sizing Decisions for a Remanufacturing Production System under Spare Parts Supply Disruption

Journal

MATHEMATICS
Volume 11, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/math11194053

Keywords

remanufacturing; spare parts; recovery; disruption; lot size

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Remanufacturing is a way to satisfy customer demand and maintain production flow, but disruptions such as spare parts shortage can lead to delays and sales loss. This study developed a cost-minimization model to analyze the recovery schedule after disruptions in the sourcing of spare parts for a remanufacturer. The results showed that the optimal recovery schedule and number of recovery cycles depend on disruption time, lost sales, and backorder costs. Spare part costs, overall recovery costs, and supplier readiness also influence a manufacturer's response to disruptions.
Remanufacturing is one of the ways forward for product recovery initiatives and for maintaining sufficient production flow to satisfy customer demand by providing high-quality goods with a combination of new and return parts through a circular economy. Recently, manufacturers have been progressively incorporating remanufacturing processes, making their supply chains vulnerable to disruptions. One of the main disruptions that occurs in remanufacturing systems is the shortage of spare parts supply, which results in unexpected delays in the remanufacturing process and could eventually result in a possible loss of sales. In the event of such potential disruptions, remanufacturing facilities must manage their supply chains in an effective and optimal manner such that the negative impact of disruptions to their business can be minimised. In this study, a two-stage production-inventory system was analysed by developing a cost-minimisation model that focuses on the recovery schedule after the occurrence of a disruption in sourcing spare parts for a remanufacturer's production cycle. The developed model was solved using the branch-and-bound algorithm, where the experimental results demonstrated that the model provides effective solutions. Through numerical experiments, results indicated that the optimal recovery schedule and the number of recovery cycles are considerably dependent on the disruption time, lost sales and backorder costs. A sensitivity analysis showed that the lost sales option seems to be more effective than the backorder sales option in optimising the system's overall cost due to unmet demand, which becomes lost sales when serviceable items are reduced, thereby shortening recovery time. Furthermore, a case study revealed that a manufacturer's response to disruption is highly influenced by the spare part costs and overall recovery costs as well as the supplier's readiness level. The proposed model could assist managers in deciding the optimal production strategy whilst providing interesting managerial insights into vital spare parts recovery issues when disruption strikes.

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