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Conceptual Framework for SDSS Development with an Application in the Retail Industry

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Abstract

Spatial information is becoming crucial for strategic decision making, but accessing and understanding this information is not easy. Dedicated tools can support the decision process in many ways, such as visualization interfaces or data analyses. Numerous Decision Support System (DSS) development methodologies exist along with dedicated Spatial Decision Support System (SDSS). Unfortunately, for multiple reasons, these tools and methodologies are not easily adaptable for the development of another SDSS. This paper proposes a framework for the development of a flexible SDSS that is built on open source software, allowing for low cost implementation. To support the efficiency of our approach, the design of a specific SDSS that is currently in use will be presented. This SDSS was developed for a company that distributes products through various retail networks. The multiple capabilities of the resulting SDSS will be revealed through an explanation of the different development steps. The complete framework is applied to a real data set that will be detailed in a demonstration.

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Notes

  1. Housing starts is an economic indicator that reflects the number of privately owned new houses on which construction has been started in a given period.

References

  • Adejuwon A, Mosavi A (2010) Domain driven data mining: application to business. Int J Comput Sci 7(4):41–44

    Google Scholar 

  • Agrawal S, Gupta RD (2014) Development and comparison of open source based web GIS frameworks on WAMP and apache tomcat web servers. In: ISPRS -international archives of the photogrammetry, remote sensing and spatial information sciences XL-4, pp 1–5

  • Armstrong M, De S, Densham PJ, Lolonis P, Rushton G, Tewari V (1990) A knowledge-based approach for supporting locational decisionmaking. Environ Plan B Plan Design 17(3):341–364

    Article  Google Scholar 

  • Benoit D, Clarke GP (1997) Assessing GIS for retail location planning. J Retail Consum Serv 4(4):239–258

    Article  Google Scholar 

  • Bogorny V, Martins Engel P, Alvares LO (2005) A reuse-based spatial data preparation framework for data mining. In: Proceedings of the 17th international conference on software engineering and knowledge engineering (SEKE’2005), pp 649–652

  • Bootstrap. http://getbootstrap.com/. Accessed 13 July 2016

  • BoundlessGeo. http://boundlessgeo.com/. Accessed 13 July 2016

  • Bradlow ET, Gangwar M, Kopalle P, Voleti S (2017) The role of big data and predictive analytics in retailing. J Retail 93(1):79–95

    Article  Google Scholar 

  • Chart JS. http://www.chartjs.org/. Accessed 13 July 2016

  • Chen J, Maceachren AM, Guo D (2008) Supporting the process of exploring and interpreting space–time multivariate patterns: the visual inquiry toolkit. Cartogr Geogr Inf Sci 35(1):33–50

    Article  Google Scholar 

  • Cios K, Pedrycz W, Swiniarski R, Kurgan L (2007) The knowledge discovery process. In: Data mining: a knowledge discovery approach. Springer Science & Business Media, Berlin, pp 9–24

  • Cliquet G, Fady A, Basset G (2006) Management de la distribution, 2nd edn. Dunod, Paris

    Google Scholar 

  • CMHC. https://www.cmhc-schl.gc.ca/. Accessed 13 July 2016

  • Crossland M, Wynne B, Perkins W (1995) Spatial decision support systems: an overview of technology and a test of efficacy. Decis Support Syst 14(3):219–235

    Article  Google Scholar 

  • Densham P (1991) Spatial decisions support systems. In: Longley P (ed) Geographical information systems: principles and applications. Wiley, Hoboken, pp 403–412

    Google Scholar 

  • Dubelaar C, Bhargava M, Ferrarin D (2002) Measuring retail productivity: What really matters? J Bus Res 55(5):417–426

    Article  Google Scholar 

  • Erskine M, Gregg D, Karimi J, Scott J (2013) Business decision-making using geospatial data: a research framework and literature review. Axioms 3(1):10–30

    Article  Google Scholar 

  • Evans B, Sabel CE (2012) Open-source web-based geographical information system for health exposure assessment. Int J Health Geogr 11:1–11

    Article  Google Scholar 

  • Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–54

    Google Scholar 

  • Flowerdrew R (1991) Spatial data integration. Geogr Inf Syst 1:375–387

    Google Scholar 

  • FORAC. http://www.forac.ulaval.ca/. Accessed 13 July 2016

  • GeoExplorer. http://suite.opengeo.org/opengeo-docs/geoexplorer/. Accessed 13 July 2016

  • GeoServer. http://geoserver.org/. Accessed 13 July 2016

  • Ghaemi P, Swift J, Sister C, Wilson JP, Wolch J (2009) Design and implementation of a web-based platform to support interactive environmental planning. Comput Environ Urban Syst 33(6):482–491

    Article  Google Scholar 

  • Golf-clubs. http://www.legolfquebecois.com/. Accessed 13 July 2016

  • Google. Google maps api. https://developers.google.com/maps/. Accessed 13 July 2016

  • Google. Google maps geocoding api. https://developers.google.com/maps/documentation/geocoding/start. Accessed 13 July 2016

  • Granell C, Díaz L, Gould M (2010) Service-oriented applications for environmental models: reusable geospatial services. Environ Model Softw 25(2):182–198

    Article  Google Scholar 

  • Hernandez T (2007) Enhancing retail location decision support: the development and application of geovisualization. J Retail Consum Serv 14(4):249–258

    Article  Google Scholar 

  • Hess R, Rubin R, West L (2004) Geographic information systems as a marketing information system technology. Decis Support Syst 38(2):197–212

    Article  Google Scholar 

  • Jin X (2011) A supply chain optimization DSS web-services-based for e-retail industry. In: 2011 IEEE power engineering and automation conference (PEAM). IEEE, Wuhan, pp 229–232

  • Jsoup. https://jsoup.org/. Accessed 13 July 2016

  • Keenan P (2004) Using a GIS as a DSS generator. ICFAI University Press, Hyderabad, pp 97–113

    Google Scholar 

  • Keenan P (2006) Spatial decision support systems: a coming of age. Control Cybern 35:9–27

    Google Scholar 

  • Khan M, Khan SS (2011) Data and information visualization methods, and interactive mechanisms: a survey. Int J Comput Appl 34:1–14

    Google Scholar 

  • Knezic S, Mladineo N (2006) GIS-based dss for priority setting in humanitarian mine-action. Int J Geogr Inf Sci 20(5):565–588

    Article  Google Scholar 

  • Lloyd R (1997) Spatial cognition: geographic environments, vol 39. The GeoJournal library. Springer, Dordrecht

    Google Scholar 

  • Loucks DP (1995) Developing and implementing decision support systems: a critique and a challenge. J Am Water Resour Assoc 31(4):571–582

    Article  Google Scholar 

  • MacEachren AM, Kraak M-J (2001) Research challenges in geovisualization. Cartogr Geogr Inf Sci 28(1):3–12

    Article  Google Scholar 

  • Maibec. http://www.maibec.com/. Accessed 13 July 2016

  • Mendes A, Themido I (2004) Multi-outlet retail site location assessment. Int Trans Oper Res 11(1):1–18

    Article  Google Scholar 

  • Mennis J, Guo D (2009) Spatial data mining and geographic knowledge discovery-an introduction. Comput Environ Urban Syst 33(6):403–408

    Article  Google Scholar 

  • Moreno-Sanchez R, Anderson G, Cruz J, Hayden M (2007) The potential for the use of open source software and open specifications in creating web-based cross-border health spatial information systems. Int J Geogr Inf Sci 21(10):1135–1163

    Article  Google Scholar 

  • OpenLayers. http://openlayers.org/. Accessed 13 July 2016

  • Otto B (2011) Data governance. Bus Inf Syst Eng 3(4):241–244

    Article  Google Scholar 

  • PgAdmin. http://www.pgadmin.org/. Accessed 13 July 2016

  • PostGIS. http://postgis.net/. Accessed 13 July 2016

  • PostgreSQL. https://www.postgresql.org/. Accessed 13 July 2016

  • QGIS. http://www.qgis.org/en/site/. Accessed 13 July 2016

  • R. http://www.r-project.org/. Accessed 13 July 2016

  • Reinartz W, Dellaert B, Krafft M, Kumar V, Varadarajan R (2011) Retailing innovations in a globalizing retail market environment. J Retail 87:S53–S66

    Article  Google Scholar 

  • Rey S (2009) Show me the code: spatial analysis and open source. J Geogr Syst 11(2):191–207

    Article  Google Scholar 

  • Rinner C, Keßler C, Andrulis S (2008) The use of web 2.0 concepts to support deliberation in spatial decision-making. Comput Environ Urban Syst 32(5):386–395

    Article  Google Scholar 

  • Roig-Tierno N, Baviera-Puig A, Buitrago-Vera J, Mas-Verdu F (2013) The retail site location decision process using gis and the analytical hierarchy process. Appl Geogr 40:191–198

    Article  Google Scholar 

  • Sikder IU (2009) Knowledge-based spatial decision support systems: an assessment of environmental adaptability of crops. Expert Syst Appl 36(3):5341–5347

    Article  Google Scholar 

  • Ski-resort. http://www.maneige.com/. Accessed 13 July 2016

  • Sprague RH (1980) A framework for the development of decision support systems. MIS Q 4(4):1–26

    Article  Google Scholar 

  • Statistics-Canada. https://www12.statcan.gc.ca/. Accessed 13 July 2016

  • Studio R. https://www.rstudio.com/. Accessed 24 October 2016

  • Sugumaran V (2007) Web-based spatial decision support systems (websdss): evolution, architecture, examples and challenges. Commun Assoc Inf Syst 19:844–875

    Google Scholar 

  • Thompson A, Walker J (2005) Retail network planning: achieving competitive advantage through geographical analysis. J Target Meas Anal Mark 13(3):250–257

    Article  Google Scholar 

  • Tomcat. http://tomcat.apache.org/. Accessed 13 July 2016

  • Vatsavai RR, Shekhar S, Burk TE, Lime S (2006) Umn-mapserver: a high-performance, interoperable, and open source web mapping and geo-spatial analysis system. In: Raubal M, Miller HJ, Frank AU, Goodchild MF (eds) Geographic information science: 4th international conference, GIScience 2006, Munster, Germany, 20–23 Sept, vol 4197. Springer, Berlin, pp 400–417

  • Wanderer T, Herle S (2015) Creating a spatial multi-criteria decision support system for energy related integrated environmental impact assessment. Environ Impact Assess Rev 52:2–8

    Article  Google Scholar 

  • Yeoman. http://yeoman.io/. Accessed 13 July 2016

  • Zhang C, Zhao T, Li W (2010) The framework of a geospatial semantic web-based spatial decision support system for digital earth. Int J Digit Earth 3(2):111–134

    Article  Google Scholar 

  • Zhu X, Healey R, Aspinall R (1998) A knowledge-based systems approach to design of spatial decision support systems for environmental management. Environ Manag 22(1):35–48

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the support offered by the research consortium FORAC (Forest to Customer) (2016) and Maibec (2016) that, in addition to all of the data used in the case study, provided comments and answers to many questions during the project.

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Authors and Affiliations

  1. FORAC, CIRRELT, Département de Mathématiques et Génie Industriel, École Polytechnique de Montréal, Montreal, QC, H3T 1J4, Canada

    Gautier Daras & Bruno Agard

  2. Univ. Grenoble Alpes, CNRS, Grenoble INP, G-SCOP, 38 000, Grenoble, France

    Gautier Daras & Bernard Penz

Authors
  1. Gautier Daras
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  2. Bruno Agard
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  3. Bernard Penz
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Correspondence to Gautier Daras.

Additional information

Accepted after one revision by Prof. Dr. Kliewer.

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Daras, G., Agard, B. & Penz, B. Conceptual Framework for SDSS Development with an Application in the Retail Industry. Bus Inf Syst Eng 61, 357–373 (2019). https://doi.org/10.1007/s12599-018-0548-y

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