Efficient spare parts management is fundamental for service organizations. Optimal strategies in procurement, stocking, and supply increase the competitive advantage of service-oriented firms. Therefore, a dynamic asset deployment (DAD) strategy should be used to determine what items allocate throughout the geographical hierarchy of company service support locations. That policy should define whether to centralize or decentralize the management of Stock Keeping Units (SKUs), aligning spare parts storage and distribution chain closely with the equipment users’ operations. A successful heuristic strategy should consider individual item criticality while remaining flexible with respect to demand fluctuations. A popular methodology for the hierarchical classification of assets is the multi-criteria ABC analysis, which has been widely applied to plan the optimal supply of spare parts in a single warehouse. This paper compares two heuristic approaches that exploit ABC analysis to solve a multi-item, multi-location problem, indicating how to deploy SKUs in different warehouses and configuring the distribution network. First, a literature approach is shown. Then, considering the first approach, a renewal-based model is derived to overcome the identified weaknesses. Both methodologies are applied to the case study of an Italian trucking company. Using historical data of demand, that occurred in the company is compared with what would have happened using the two approaches. Results prove that both ABC strategies allow rationalizing the use of economic resources, thus achieving benefits in terms of stock levels and number of orders. However, the derived approach is not affected by subjectivity and can be applied to manage thousands of different items. Moreover, the results highlight the differences between the compared approaches in terms of centralization and decentralization.

A novel approach for spare parts dynamic deployment

Leoni L.;
2021-01-01

Abstract

Efficient spare parts management is fundamental for service organizations. Optimal strategies in procurement, stocking, and supply increase the competitive advantage of service-oriented firms. Therefore, a dynamic asset deployment (DAD) strategy should be used to determine what items allocate throughout the geographical hierarchy of company service support locations. That policy should define whether to centralize or decentralize the management of Stock Keeping Units (SKUs), aligning spare parts storage and distribution chain closely with the equipment users’ operations. A successful heuristic strategy should consider individual item criticality while remaining flexible with respect to demand fluctuations. A popular methodology for the hierarchical classification of assets is the multi-criteria ABC analysis, which has been widely applied to plan the optimal supply of spare parts in a single warehouse. This paper compares two heuristic approaches that exploit ABC analysis to solve a multi-item, multi-location problem, indicating how to deploy SKUs in different warehouses and configuring the distribution network. First, a literature approach is shown. Then, considering the first approach, a renewal-based model is derived to overcome the identified weaknesses. Both methodologies are applied to the case study of an Italian trucking company. Using historical data of demand, that occurred in the company is compared with what would have happened using the two approaches. Results prove that both ABC strategies allow rationalizing the use of economic resources, thus achieving benefits in terms of stock levels and number of orders. However, the derived approach is not affected by subjectivity and can be applied to manage thousands of different items. Moreover, the results highlight the differences between the compared approaches in terms of centralization and decentralization.
2021
ABC multi-criteria
Dynamic asset deployment
Spare parts management
Two-echelon network
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/28623
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