Multi-Criteria Inventory Classification Models Selection Based on Inventory Performance With Consideration of Forecast Downsides

Abstract

Companies often have to deal with many different products as inventory in their storage or warehouse. Inventory classification have an important role to helping companies manage their inventory. According to academic literature, a number of inventory researcher have developed multi-criteria inventory classification (MCIC) models to overcome inventory classification problems. This study focuses to analyze the inventory performance of these MCIC models by evaluating each model inventory performance related to the cost and the service with consideration of the forecast downsides present in the analysis. Forecast error becomes important aspect in this study because in reality there will be forecast error in the forecast and the forecast error can vary in size. There are two scenarios related to forecast error: (1) forecast upsides, when the actual customer demand becomes higher than the forecast and (2) forecast downsides, when actual customer demand becomes lower than the forecast. This study focuses on the latter ones because this scenario of forecast error has a risk of having higher inventory cost with the surplus of the inventory.