The effect of demand aggregation on spare part supply chain planning: An empirical study of oil company.

Spare parts with intermittent demand cause complexity in forecasting and planning decisions since the demand is zero in some periods. Therefore, the difficulties in planning regarding the demand quantity and arrival time motivate this study to investigate a well-structured strategy that aggregates the demand in low-frequency time intervals to reduce the zero-demand occurrence. In this way, the planning accuracy increases since the forecasting error is reduced by the use of demand aggregation. We fill the research gap by integrating the aggregation and planning model for repairable spare parts to answer the question of what the optimal aggregation level is. In this paper, we develope a planning model for a repairable spare part supply chain (SPSC) to examine the effect of aggregation on cost, stock level, and shortage. An empirical investigation from an Iranian oil company is used to validate the benefit of integrated demand aggregation. The results show that choosing an optimal aggregation level optimizes the cost, stock level, and shortage. It also improves the demand estimation gap. The provided model, measures, and managerial insights help the practitioners make robust decisions since the coefficient of variation decreases due to optimal aggregation level, which leads to improvement in the planning performance.