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The location of a retail store plays a pivotal role in its success. It is an integral and a crucial part of the retail strategy because it directly influences the merchandising mix and the store layout. Once a store is opened, a retailer can take strategic steps towards adjusting prices, changing the merchandising mix, improving services, but it becomes very difficult to change the location. Therefore, recent trends have observed a lot of research being done before finalizing the location, be it penetrating a new city, new customer segment or a new location within a city.
Retailers have a vast range of analytical techniques for their use to support their location decision. However, in the past a great proportion of retailers have preferred instinct and personal experience, considering the process as an ‘art’ (Simkin et al., 1985)., using very subjective and emotional methods. Most locations were fixed based on rules of thumb and intuition (Rogers, 1987).
However, over the last two decades, retailers have faced increasing pressure to adopt more rigorous and holistic approaches (Bowlby et al., 1984). due to tightening of planning controls on new development. The availability of computing data and capacity, and rapid reduction in cost of IT software and hardware has spurred an immense growth in the volume of data available for analysis (both primary and secondary) (Hernandez et al., 1995). The methods for retail location decision making that have been widely used can be broadly classified into comparative, predictive and knowledge based. These methods have been described further in this report.
This particular study uses an integrated Decision Making Trial and Evaluation (DEMATEL) - Analytical Network Process (ANP) model. After establishing the fundamental criteria required for selecting a location, DEMATEL is used to determine their effects on each other. Then, ANP is used to assign weights to each criterion and eventually, to the alternative locations being considered.