A Novel Approach for Data-Driven Automatic Site Recommendation and Selection

TL;DR

This paper introduces a scalable, data-driven method for automatic site selection, effectively handling Big Data and outperforming manual approaches in recommending optimal supermarket locations based on extensive socio-economic and geographical factors.

Contribution

It presents a novel, robust, and scalable automatic site recommendation method that leverages Big Data for improved decision-making in site selection processes.

Findings

86.4% overlap with existing supermarket sites

Recommends 328 new potential store locations

Effective handling of large, complex datasets

Abstract

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.