A Methodology for Clustering Items with Seasonal and Non-seasonal Demand Patterns for Inventory Management

Abstract

Item clustering has become one of the most important topics in terms of effective inventory management in supply chains. Classification of items in terms of their features, sales or consumption volume and variation is a prerequisite to determine differentiated inventory policies as well as parameters, most common of which is service levels. Volume classification is easily obtained by well-known Pareto approach while coefficient of variance is usu-ally used for variation dimension. Hence, it is not always applicable to classify items under different product families with different demand patterns in terms of variation. In this paper, we propose two algorithms, one based on statistical analysis and the other an unsupervised machine learning algorithm using K-means clustering, both of which differ-entiate seasonal and non-seasonal products where an item’s variation is evaluated with respect to seasonality of the product group it belongs to. We then calculate the efficiency of two proposed approaches by standard deviation within each cluster and absolute difference of percentage of volume and item numbers. We also compare the outputs of two algorithms with the methodology which is based on coefficient of variance and is currently in use at the company which is a leading major domestic appliance manufacturer. The results show that the statistical method we propose generates superior outputs than the other two for both seasonal and non-seasonal demand patterns.

Keywords

• Inventory control
• item classification
• K-means clustering
• supply chain management
• seasonality

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