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

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.

[1]  Thomas Glatte.  Location Strategies: Methods and their methodological limitations , 2015 .

[2]  Aleksandar Rikalovic,et al.  A Fuzzy Expert System for Industrial Location Factor Analysis , 2015 .

[3]  Maria Luisa Hernández-Alcaraz,et al.  Social knowledge-based recommender system. Application to the movies domain , 2012, Expert Syst. Appl..

[4]  Juan Manuel Cueva Lovelle,et al.  Implicit feedback techniques on recommender systems applied to electronic books , 2012, Comput. Hum. Behav..

[5]  Yoon Ho Cho,et al.  Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations , 2010, Inf. Sci..

[6]  Josep-Maria Arauzo-Carod,et al.  Empirical Studies in Industrial Location: An Assessment of Their Methods and Results , 2009 .

[7]  Alexander Felfernig,et al.  Constraint-based recommender systems: technologies and research issues , 2008, ICEC.

[8]  Harald Strotmann.  Entrepreneurial Survival , 2007 .

[9]  M. Badri.  Dimensions of Industrial Location Factors : Review and Exploration , 2007 .

[10]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[11]  A. Sohal,et al.  Supply chain competitiveness: measuring the impact of location factors, uncertainty and manufacturin , 2005 .

[12]  J. Carod.  Determinants of industrial location: An application for Catalan municipalities* , 2005 .

[13]  Herbert Woratschek,et al.  Dienstleistungsmanagement und Standortentscheidungen im internationalen Kontext — Möglichkeiten und Grenzen des Einsatzes betriebswirtschaftlicher Verfahren , 2004 .

[14]  Standortentscheidungen im Handel: Möglichkeiten und Grenzen von Gravitationsmodellen , 2000 .

[15]  H. Greiner.  Standortbewertung im Einzelhandel — Organisation und Durchführung der Standortselektion am Beispiel der REWE-Gruppe , 1997 .

[16]  John P. Blair,et al.  Major Factors in Industrial Location: A Review , 1987 .

[17]  Unternehmerische Standortplanung und regionale Wirtschaftsförderung : eine empirische Analyse des Standortverhaltens industrieller Großunternehmen , 1983 .

[18]  H. Liebmann.  Grundlagen betriebswirtschaftlicher Standortentscheidungen , 1969 .

[19]  Rex Acton,et al.  The Design of Production Systems , 1968 .

[20]  J. McMillan.  Why Manufacturers Choose Plant Locations vs. Determinants of Plant Locations , 1965 .

[21]  Wilhelm Launhardt,et al.  Mathematische Begründung der Volkswirthschaftslehre , 1963 .

[22]  A. Weber,et al.  Alfred Weber's Theory of the Location of Industries , 1930 .

[23]  M. Hosseinpour.  An intelligent fuzzy-based recommendation system for consumer electronic products , 2022 .