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

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Published:25 August 2020Publication History

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.

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

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      cover image ACM Other conferences
      APCORISE '20: Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering
      June 2020
      410 pages
      ISBN:9781450376006
      DOI:10.1145/3400934

      Copyright © 2020 ACM

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      Publication History

      • Published: 25 August 2020

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      APCORISE '20 Paper Acceptance Rate68of110submissions,62%Overall Acceptance Rate68of110submissions,62%
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