Abstract
Inventory prediction and management is a key issue in a retail store. There are a number of inventory prediction techniques. However, it is difficult to identify a time series prediction model for inventory forecasting that provides uniformly good results for all the products in a store. This paper uses data from a small retail store to demonstrate the variability of results for different modeling techniques and different products. We demonstrate inadequacy of a generic inventory model. Stability and seasonality analysis of the time series is used to identify groups of products (local groups) exhibiting similar sales patterns. Different clustering techniques are applied to determine reasonable local groups. With the help of Mean absolute percentage error (MAPE), the effectiveness of dataset partitioning for better inventory management is demonstrated. Appropriate inventory management strategies are proposed based on the stability and seasonality analysis.
© de Gruyter 2011
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