Abstract:
Warehouses are one of the key components in the supply chain of a firm. An improvement to the operational efficiency and the productivity of warehouses, is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers pre-emptively estimate the overall warehouse performance, which requires an accurate prediction of a warehouse’s key performance indicators (KPIs).This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with low forecasting error in order to precisely measure the overall warehouse performance.Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies16 KPIs. Traditionally, the grey method (GM), GM (1, 1) is used in the literature to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this study presents a novel Particle Swarm Optimization (PSO)-based integrated grey model called PSOGM (1, 1)to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model, GAGM (1, 1), discrete grey model, DGM (1, 1) to assess the performance of the proposed model in terms of the mean absolute percentage error (MAPE). The proposed model outperforms the existing grey models by reducing the MAPE 6-29% for the KPIs of three distinct warehouses, and 23-28% for the pilot data seriesand, in turn, leads to estimate the overall warehouse performance through the forecasting of the KPIs.To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make overall warehouse performance estimations in advance to take control measures regarding warehouse productivity and efficiency.
