IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v61y2019i3d10.1007_s12599-018-0548-y.html
   My bibliography  Save this article

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

Author

Listed:
  • Gautier Daras

    (École Polytechnique de Montréal
    Univ. Grenoble Alpes, CNRS, Grenoble INP, G-SCOP)

  • Bruno Agard

    (École Polytechnique de Montréal)

  • Bernard Penz

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, G-SCOP)

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.

Suggested Citation

  • Gautier Daras & Bruno Agard & Bernard Penz, 2019. "Conceptual Framework for SDSS Development with an Application in the Retail Industry," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 357-373, June.
  • Handle: RePEc:spr:binfse:v:61:y:2019:i:3:d:10.1007_s12599-018-0548-y
    DOI: 10.1007/s12599-018-0548-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-018-0548-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12599-018-0548-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Reinartz, Werner & Dellaert, Benedict & Krafft, Manfred & Kumar, V. & Varadarajan, Rajan, 2011. "Retailing Innovations in a Globalizing Retail Market Environment," Journal of Retailing, Elsevier, vol. 87(S1), pages 53-66.
    2. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    3. M P Armstrong & S De & P J Densham & P Lolonis & G Rushton & V K Tewari, 1990. "A Knowledge-Based Approach for Supporting Locational Decisionmaking," Environment and Planning B, , vol. 17(3), pages 341-364, September.
    4. Sergio Rey, 2009. "Show me the code: spatial analysis and open source," Journal of Geographical Systems, Springer, vol. 11(2), pages 191-207, June.
    5. Gérard Cliquet & André Fady & Guy Basset, 2006. "Management de la distribution," Post-Print halshs-00241555, HAL.
    6. Dubelaar, Chris & Bhargava, Mukesh & Ferrarin, David, 2002. "Measuring retail productivity: what really matters?," Journal of Business Research, Elsevier, vol. 55(5), pages 417-426, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dekimpe, Marnik G., 2020. "Retailing and retailing research in the age of big data analytics," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 3-14.
    2. Gupta, Shaphali & Ramachandran, Divya, 2021. "Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach," Journal of Retailing, Elsevier, vol. 97(4), pages 597-620.
    3. Saarijärvi, Hannu & Mitronen, Lasse & Yrjölä, Mika, 2014. "From selling to supporting – Leveraging mobile services in the context of food retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 21(1), pages 26-36.
    4. Kumar, V. & Sunder, Sarang & Sharma, Amalesh, 2015. "Leveraging Distribution to Maximize Firm Performance in Emerging Markets," Journal of Retailing, Elsevier, vol. 91(4), pages 627-643.
    5. Namin, Aidin & Soysal, Gonca P. & Ratchford, Brian T., 2022. "Alleviating demand uncertainty for seasonal goods: An analysis of attribute-based markdown policy for fashion retailers," Journal of Business Research, Elsevier, vol. 145(C), pages 671-681.
    6. Gilboa, Shaked & Mitchell, Vince, 2020. "The role of culture and purchasing power parity in shaping mall-shoppers’ profiles," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    7. Antonio Páez, 2021. "Open spatial sciences: an introduction," Journal of Geographical Systems, Springer, vol. 23(4), pages 467-476, October.
    8. Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    9. Ye, Xinyue & Yue, Wenze, 2014. "Comparative analysis of regional development: Exploratory space-time data analysis and open source implementation," Economics Discussion Papers 2014-20, Kiel Institute for the World Economy (IfW Kiel).
    10. Ron Berman & Ayelet Israeli, 2022. "The Value of Descriptive Analytics: Evidence from Online Retailers," Marketing Science, INFORMS, vol. 41(6), pages 1074-1096, November.
    11. Madeleine Besson & Bernard Bourdon, 2015. "Can multimarket competition theory explain why manufacturers are reluctant to adopt e-commerce ? The case of the French household appliances’ manufacturers," Grenoble Ecole de Management (Post-Print) hal-02387337, HAL.
    12. Park, Timothy A., 2014. "Assessing Performance Impacts in Food Retail Distribution Systems: A Stochastic Frontier Model Correcting for Sample Selection," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 0, pages 1-17.
    13. Agrawal, Shiv Ratan & Mittal, Divya, 2022. "Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    14. Nath, Pravin & Kirca, Ahmet H. & Kim, Saejoon & Andras, Trina Larsen, 2019. "The Effects of Retail Banner Standardization on the Performance of Global Retailers," Journal of Retailing, Elsevier, vol. 95(3), pages 30-46.
    15. Romero, Jaime & Cruz-Roche, Ignacio & Charron, Jean-Philippe, 2020. "The myth of price convergence under economic integration: A proposed explanation for the difference in food prices across European countries," European Management Journal, Elsevier, vol. 38(2), pages 267-276.
    16. Bharadwaj Kadiyala & Özalp Özer & A. Serdar Şimşek, 2021. "Data‐Driven Approaches to Targeting Promotion E‐mails: The Case of Delayed Incentives," Production and Operations Management, Production and Operations Management Society, vol. 30(3), pages 766-782, March.
    17. Pallant, Jason I. & Pallant, Jessica L. & Sands, Sean J. & Ferraro, Carla R. & Afifi, Eslam, 2022. "When and how consumers are willing to exchange data with retailers: An exploratory segmentation," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    18. repec:asg:wpaper:1008 is not listed on IDEAS
    19. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    20. repec:rri:wpaper:201301 is not listed on IDEAS
    21. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    22. Christophe Bezes & Bertrand Belvaux, 2012. "Which elements of differentiation for commercial websites? A transmitted image approach [Quels éléments de différenciation pour les sites web marchands ? Une approche par l'image transmise]," Post-Print hal-02086743, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:binfse:v:61:y:2019:i:3:d:10.1007_s12599-018-0548-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.