Article ID: | iaor200972174 |
Country: | United Kingdom |
Volume: | 19 |
Issue: | 4 |
Start Page Number: | 379 |
End Page Number: | 396 |
Publication Date: | Jun 2008 |
Journal: | Production Planning and Control |
Authors: | Cavalieri S, Garetti M, Macchi M, Pinto R |
Keywords: | artificial intelligence: decision support, inventory |
Despite the huge body of academic literature on inventory management of maintenance spare parts, few companies seem to deliberately use the proper approaches and tools for a factual and quantitative assessment. Detaining or not stocks of a spare item, deciding upon the right level of inventory, forecasting its sporadic consumption are just some of the evidences of the complexity and criticality underlying the daily decisions the management of a company has to undertake. The objective of the paper is to provide a stepwise decision-making path in order to orienteer an industrial manager on how to pragmatically handle the management of maintenance spare parts in a company. The framework is structured into five sequential steps: part coding, part classification, part demand forecasting, stock management policy and policy test and validation. Its applicability is demonstrated by making use of a real business case where it has been successfully adopted.