research-article

Geospatial Customer, Competitor and Supplier Analysis for Site Selection of Supermarkets

Authors:

Sebastian Baumbach,

Christoph Rubel,

Andreas DengelAuthors Info & Claims

ICGDA '19: Proceedings of the 2019 2nd International Conference on Geoinformatics and Data Analysis

Pages 110 - 114

https://doi.org/10.1145/3318236.3318264

Published: 15 March 2019 Publication History

Abstract

Currently, there are rapid changes in the supermarket sector, as more and more companies provide home delivery services of most of their products. Recently, perishable groceries are also directly shipped to people's home, which leads to new logistical challenges. By only using statistical data known as location factors, problems arose for metropolises like Berlin. These cities have several millions of inhabitants, but are only modeled as one geographic entity with a single attributes like average net income. In order to provide insights into cities down to street level, this geodata model was enriched with information of OpenStreetMap (OSM). Additionally, the road network of OSM was used to dynamically calculate catchment areas and combine them with location factors of the geodata model. This combination enables dynamic and data-driven customer, competitor, and supplier analysis. The evaluation was performed on the application scenario of online food delivery models of Edeka, Rewe and Amazon-Fresh. The results indicate, that Edeka's delivery model-currently evaluated by Edeka in Berlin-only reaches 0,28 % of the inhabitants, whereas Amazon fresh achieves nearly 100 % coverage. As a conclusion, the presented data-driven business analysis enables new possibilities for site selection and potential for future applications.

References

[1]

A. Kubis and M. Hartmann, "Analysis of location of large-area shopping centres. A probabilistic gravity model for the halle-leipzig area," Jahrbuch für Regionalwissenschaft, vol. 27, no. 1, pp. 43--57, 2007.

[2]

H. Strotmann, "Entrepreneurial survival," Small business economics, vol. 28, no. 1, pp. 87--104, 2007.

[3]

M. Xu, T. Wang, Z. Wu, J. Zhou, J. Li, and H. Wu, "Demand driven store site selection via multiple spatial-temporal data," in Proceedings of the 24th ACMSIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 40. ACM, 2016.

Digital Library

[4]

P. S. Leeflang, P. C. Verhoef, P. Dahlström, and T. Freundt, "Challenges and solutions for marketing in a digital era," European Management Journal, vol. 32, no. 1, pp. 1--12, 2014.

[5]

M.-H. Hsiao. "Shopping mode choice: Physical store shopping versus e-shopping," Transportation Research Part E: Logistics and Transportation Review, vol. 45, no. 1, pp. 86--95, 2009.

[6]

W. Reinartz, B. Dellaert, M. Krafft, V. Kumar, and R. Varadarajan, "Retailing innovations in a globalizing retail market environment," Journal of Retailing, vol. 87, pp. S53-S66, 2011.

[7]

A. Weber, Über den standort der industrien, translated by cj friedrich (1929). Alfred WeberŠs Theory of the Location of Industries, 1909.

[8]

W. Applebaum, "Methods for determining store trade areas, market penetration, and potential sales," Journal of Marketing Research, pp. 127--141, 1966.

[9]

J. Korpela and M. Tuominen, "A decision aid in warehouse site selection," International Journal of Production Economics, vol. 45, pp. 1--3, pp. 169--180, 1996.

[10]

J. Sánchez-Lozano, M. García-Cascales, and M. Lamata, "Gis-based onshore wind farm site selection using fuzzy multi-criteria decision making methods. Evaluating the case of southeastern spain," Applied Energy, vol. 171, pp. 86--102, 2016.

[11]

M. Uyan, "Gis-based solar farms site selection using analytic hierarchy process (ahp) in karapinar region, konya/turkey," Renewable and Sustainable Energy Reviews, vol. 28, no. 11--17, 2013.

[12]

T. Hernandez and D. Bennison, "The art and science of retail location decisions," International Journal of Retail & Distribution Management, vol. 28, no. 8, pp. 357--367, 2000.

[13]

D. Karamshuk, A. Noulas, S. Scellato, V. Nicosia, and C. Mascolo, "Geo-spotting: Mining online location-based services for optimal retail store placement," in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 793--801, ACM, 2013.

Digital Library

[14]

F. Damanpour and J. A. Damanpour, "E-business e-commerce evolution: Perspective and strategy," Managerial Finance, vol. 27, no. 7, pp. 16--33, 2001.

[15]

S. Cairns, "Delivering supermarket shopping: more or less traffic?" Transport Reviews, vol. 25, no. 1, pp. 51--84, 2005.

[16]

D. Zelenovic, "Location of production systems," The Design of Production Systems, pp. 373--394, 2003.

[17]

M. Haklay and P. Weber, "Openstreetmap: User-generated street maps," IEEE Pervasive Computing, vol. 7, no. 4, pp. 12--18, 2008.

Digital Library

[18]

O. Berman and D. Krass, "The generalized maximal covering location problem," Computers & Operations Research, vol. 29, no. 6, pp. 563--581, 2002.

Digital Library

[19]

P. Jensen, "Network-based predictions of retail store commercial categories and optimal locations," Physical Review E, vol. 74, no. 3, 035101, 2006.

Cited By

Ahmad M(2023)Unleashing Business PotentialData-Driven Intelligent Business Sustainability10.4018/979-8-3693-0049-7.ch013(177-198)Online publication date: 5-Dec-2023
https://doi.org/10.4018/979-8-3693-0049-7.ch013
Lin BLi FFeng JXu S(2023)Research on Location Selection of General Merchandise Store Based on Machine LearningAdvances in Swarm Intelligence10.1007/978-3-031-36625-3_14(168-180)Online publication date: 8-Jul-2023
https://doi.org/10.1007/978-3-031-36625-3_14
Bustamante ASebastia LOnaindia E(2020)BITOUR: A Business Intelligence Platform for Tourism AnalysisISPRS International Journal of Geo-Information10.3390/ijgi91106719:11(671)Online publication date: 12-Nov-2020
https://doi.org/10.3390/ijgi9110671

Index Terms

Geospatial Customer, Competitor and Supplier Analysis for Site Selection of Supermarkets
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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cover image ACM Other conferences

ICGDA '19: Proceedings of the 2019 2nd International Conference on Geoinformatics and Data Analysis

March 2019

156 pages

ISBN:9781450362450

DOI:10.1145/3318236

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Department of Informatics, University of Oslo

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Association for Computing Machinery

New York, NY, United States

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Published: 15 March 2019

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ICGDA 2019

ICGDA 2019: 2019 2nd International Conference on Geoinformatics and Data Analysis & 2019 2nd International Conference on Software and Services Engineering

March 15 - 17, 2019

Prague, Czech Republic

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Cited By

Ahmad M(2023)Unleashing Business PotentialData-Driven Intelligent Business Sustainability10.4018/979-8-3693-0049-7.ch013(177-198)Online publication date: 5-Dec-2023
https://doi.org/10.4018/979-8-3693-0049-7.ch013
Lin BLi FFeng JXu S(2023)Research on Location Selection of General Merchandise Store Based on Machine LearningAdvances in Swarm Intelligence10.1007/978-3-031-36625-3_14(168-180)Online publication date: 8-Jul-2023
https://doi.org/10.1007/978-3-031-36625-3_14
Bustamante ASebastia LOnaindia E(2020)BITOUR: A Business Intelligence Platform for Tourism AnalysisISPRS International Journal of Geo-Information10.3390/ijgi91106719:11(671)Online publication date: 12-Nov-2020
https://doi.org/10.3390/ijgi9110671

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