Operational planning in service control towers - heuristics and case study

We study performance improvement in multi-echelon, closed loop spare part supply chains using operational interventions based on real-time status information. Our objective is to minimize the total cost relevant costs, consisting of intervention costs and the backorder costs. In this paper, we focus on proactive interventions, aiming to avoid stockouts. We assume that all reactive interventions are fixed. Proactive interventions that we study include lateral transshipments, emergency shipments, stock reservations, expediting part repairs, and early new buys of parts. These interventions are invoked by using alert generation, when the supply chain status deviates from the plan. We propose heuristic rules to generate alerts. We also develop heuristic rules for the choice of operational interventions. We model and test our heuristics in a simulation test bed, based on data of a global IT company by using the case data in Germany. Numerical experiments reveal the following key insights: (i) downstream interventions - proactive lateral and emergency shipments - have most impact in reducing costs, (ii) communicating losses in the supply chain (no returns, failed repairs) for early new buys has positive impact on fill rates at negligible costs, and (iii) expedite repair and stock reservations using the proposed rules is not profitable. (C) 2022 The Author(s). Published by Elsevier B.V.

Automated summary

This study aims to improve the performance of multi-echelon, closed loop spare part supply chains through operational interventions based on real-time status information. Proactive interventions, including lateral transshipments, emergency shipments, stock reservations, expediting part repairs, and early new buys of parts, are found to have the most impact in reducing costs. Communicating losses in the supply chain for early new buys has a positive impact on fill rates at negligible costs. Expedite repair and stock reservations using the proposed rules are not profitable. The results are based on simulation experiments using data from a global IT company in Germany.