A hybrid decision support system for storage location assignment in the fast-fashion industry

In the highly competitive market of the fast-fashion industry, the fashion trend keeps on changing rapidly while customers always chase after new fashionable products. To meet customers' expectation and to cater for seasonal demand fluctuation, the product life cycle becomes shorter. The result of this is that frequent delivery of new products from the distribution center to the fashion retailers is necessary. This puts extra pressure on the warehouse of the fashion retailer, in which a large number of fashion products of different sizes have to be handled within a limited period of time. Due to the lack of an appropriate storage location assignment strategy, fashion products are always stored randomly, which increases the difficulties of picking the designated products. This results in low warehouse operations efficiency. In this paper, a decision support system for facilitating hybrid storage location assignment decisions is proposed to improve the warehouse operations by considering the fashion product characteristics and the order picking patterns. By integrating the class-based storage assignment policy and association rule mining, the fashion products are classified into different classes and stored according to the correlation between products. Through conducting a pilot study in a retailer store, it was found that the systematic storage location assignment strategy improves the order picking efficiency in terms of shorter travel distance.