Optimization of retail clusters by improving individual store performance

Abstract

Clustering is a common phenomenon seen all around the world in industries, and the service sector. Clustering is a complicated case in retail, and mainstream literature is populated with studies that define store performance for single stores; however, not much is available when they are in clustering, as the conventional trading boundaries, which form the area in which the store's influence extends, cannot be defined. The present study was conducted to improve the overall performance of the entire cluster, by dealing with individual stores. It was conducted in a large retail cluster dealing exclusively in stationary. The store facilities are analysed using fuzzy linguistic modelling from both, the customer and the retailers stand point. A model of such clusters is then prepared for the current demographic. The model generated aims to provide a holistic approach to grade the facilities available in order to determine returns. This also gives a framework for retailers to upgrade their existing facilities according to the cluster characteristics, thus improving not only individual performance, but also the performance of the cluster.