A Novel Approach for Data-Driven Automatic Site Recommendation and Selection
This addresses the need for scalable and objective site selection in business, particularly for supermarkets, though it appears incremental by automating existing economic approaches.
The paper tackles the problem of manual and subjective site selection for companies by proposing a data-driven automatic method, achieving an 86.4% overlap with existing supermarket sites and recommending 328 new sites.
This paper presents a novel, generic, and automatic method for data-driven site selection. Site selection is one of the most crucial and important decisions made by any company. Such a decision depends on various factors of sites, including socio-economic, geographical, ecological, as well as specific requirements of companies. The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data. The presented method for site selection is robust, efficient, scalable, and is capable of handling challenges emerging in Big Data. To assess the effectiveness of the presented method, it is evaluated on real data (collected from Federal Statistical Office of Germany) of around 200 influencing factors which are considered by economists for site selection of Supermarkets in Germany (Lidl, EDEKA, and NP). Evaluation results show that there is a big overlap (86.4 \%) between the sites of existing supermarkets and the sites recommended by the presented method. In addition, the method also recommends many sites (328) for supermarket where a store should be opened.