We consider a single-part, two-echelon supply chain problem for spare parts. The network consists of a single manufacturing plant, a set of service centers (SCs) and a set of customers. Both echelons keep spare parts using the base-stock replenishment policy. The plant behaves as an M/M/1 queueing system and has limited production and storage capacity. Demand faced by each SC follows an independent Poisson process. The problem is to determine optimal location-allocation and optimal base-stock levels at both echelons while satisfying the target service levels and customer preferences of SCs. We develop a mixed integer non-linear programming model and use cutting-plane method to optimize the inventory-location decisions. We present an exact solution procedure for the inventory stocking problem and demonstrate the limitations of using traditional inventory models like METRIC-like and Approximate in case of high utilization rates. We show the effectiveness of our proposed cutting-plane algorithm and provide important managerial insights for spare parts management.

We consider a single-part, two-echelon supply chain problem for spare parts. The network consists of a single manufacturing plant, a set of service centers (SCs) and a set of customers. Both echelons keep spare parts using the base-stock replenishment policy. The plant behaves as an M/M/1 queueing system and has limited production and storage capacity. Demand faced by each SC follows an independent Poisson process. The problem is to determine optimal location-allocation and optimal base-stock levels at both echelons while satisfying the target service levels and customer preferences of SCs. We develop a mixed integer non-linear programming model and use cutting-plane method to optimize the inventory-location decisions. We present an exact solution procedure for the inventory stocking problem and demonstrate the limitations of using traditional inventory models like METRIC-like and Approximate in case of high utilization rates. We show the effectiveness of our proposed cutting-plane algorithm and provide important managerial insights for spare parts management.