Integrating multi criteria analysis and clustering techniques for the segmentation of after-sale spare part inventory
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Date
2014
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Abstract
Timely and cost effective supply of spare parts is a vital issue in after sales service. If the demand for spare parts is overestimated, holding costs increase. Underestimation of demand results in lost goodwill of the customers. In order to manage the spare part inventories effectively, companies generally determine the importance level of each spare part and apply a suitable inventory control policy. In this study, we integrate clustering techniques and analytic hierarchy process (AHP) to determine the importance levels of spare parts kept by a television manufacturer. First, spare parts are grouped into 3 main categories using clustering algorithms. Then AHP is used to determine importance level of each group by considering several criteria including frequency, criticality, total monthly usage, lead time, availability, substitutability and tendency of obsolescence. Finally, suitable inventory policies are suggested for each spare part group.
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Keywords
Analytic hierarchy process , Cost effectiveness , Hierarchical systems , Inventory control , Manufacture , Obsolescence , After-sales services , Analytic hierarchy process (ahp) , Clustering , Clustering techniques , Inventory control policies , Inventory policies , Multi Criteria Analysis , Spare parts , Clustering algorithms