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Designing a Self-service Analytics System for Transportation Supplier Selection

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Intelligent Information Systems (CAiSE 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 452))

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Abstract

Today, the selection of suppliers is expanded from a pure cost-oriented view to consider multiple criteria such as delivery time, quality, and risks. Buyers in the business analyst’s role (BA) in global logistic departments are responsible for covering transportation demands. For supplier selection, they must allocate hundreds of items to an optimal set of suppliers. In interviews, we examined a high dependency on Data Scientists and Data Engineers. Currently, BAs achieve only non-optimal solutions because they lack the required knowledge and adequate tools to perform this analytical process independently. Against this backdrop, we present the design and evaluation of a self-service analytics (SSA) system that helps BAs to select suppliers for transportation demands considering multiple decision criteria. We formulate a linear optimization problem that BAs can parametrize and analyze with our SSA system. Our proposed SSA system enables BAs to improve the supplier selection process and showcases the potential of SSA systems to utilize optimization models.

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References

  1. Ho, W., Xu, X., Dey, P.K.: Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur. J. Oper. Res. 202, 16–24 (2010)

    Article  MATH  Google Scholar 

  2. Aouadni, S., Aouadni, I., Rebaï, A.: A systematic review on supplier selection and order allocation problems. J. Ind. Eng. Int. 15(1), 267–289 (2019). https://doi.org/10.1007/s40092-019-00334-y

    Article  MATH  Google Scholar 

  3. Che, Z.H., Wang, H.S.: Supplier selection and supply quantity allocation of common and non-common parts with multiple criteria under multiple products. Comput. Ind. Eng. 55, 110–133 (2008)

    Article  Google Scholar 

  4. Narasimhan, R., Talluri, S., Mahapatra, S.K.: Multiproduct, Multicriteria model for supplier selection with product life-cycle considerations. Decis. Sci. 37, 577–603 (2006)

    Article  Google Scholar 

  5. Talluri, S.: A buyer–seller game model for selection and negotiation of purchasing bids. Eur. J. Oper. Res. 143, 171–180 (2002)

    Article  MATH  Google Scholar 

  6. Forrester Opportunity Snapshot – “Mathematical Optimization and Machine Learning: Your Perfect AI Tech Team”, 15 Apr 2022. https://www.gurobi.com/resource/forrester-opportunity-snapshot-mathematical-optimization-and-machine-learning-your-perfect-ai-tech-team/

  7. Ocampo, L.A., Abad, G.K.M., Cabusas, K.G.L., Padon, M.L.A., Sevilla, N.C.: Recent approaches to supplier selection: a review of literature within 2006–2016. Int. J. Integr. Supply Manag. 12, 22–68 (2018)

    Article  Google Scholar 

  8. Schuff, D., Corral, K., St. Louis, R.D., Schymik, G.: Enabling self-service BI: a methodology and a case study for a model management warehouse. Inf. Syst. Front. 20, 275–288 (2018)

    Google Scholar 

  9. Michalczyk, S., Nadj, M., Beier, H., Maedche, A.: Designing a self-service analytics system for supply base optimization. In: Nurcan, S., Korthaus, A. (eds.) CAiSE 2021. LNBIP, vol. 424, pp. 154–161. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79108-7_18

    Chapter  Google Scholar 

  10. Alpar, P., Schulz, M.: Self-service business intelligence. Bus. Inf. Syst. Eng. 58(2), 151–155 (2016). https://doi.org/10.1007/s12599-016-0424-6

    Article  Google Scholar 

  11. Michalczyk, S., Nadj, M., Maedche, A., Gröger, C.: Demystifying job roles in data science: a text mining approach. In: ECIS 2021 Research Papers (2021)

    Google Scholar 

  12. Seaman, C.B.: Qualitative methods in empirical studies of software engineering. IEEE Trans. Softw. Eng. 25, 557–572 (1999)

    Article  Google Scholar 

  13. Nielsen, J.: Thinking Aloud: The #1 Usability Tool, 22 Mar 2018. https://www.nngroup.com/articles/thinking-aloud-the-1-usability-tool/

  14. Nickel, S., Stein, O., Waldmann, K.-H.: Operations Research. Springer Gabler, Berlin (2014)

    Google Scholar 

  15. Ruiz-Torres, A.J., Mahmoodi, F.: The optimal number of suppliers considering the costs of individual supplier failures. Omega 35, 104–115 (2007)

    Article  Google Scholar 

  16. What’sBest! Excel Add-In for Modeling and Optimization, 17 Apr 2022. https://www.lindo.com/index.php/products/what-sbest-and-excel-optimization

  17. OR-Tools Google Developers, 15 Apr 2022. https://developers.google.com/optimization

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Acknowledgment

We thank Mario Nadj for the fruitful conceptual discussions, and Pablo Alonso Sanandres, for supporting our work at Robert Bosch GmbH.

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Authors and Affiliations

  1. Institute of Information Systems and Marketing (IISM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Sven Michalczyk, Nicolas Breitling & Alexander Maedche

Authors
  1. Sven Michalczyk
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  2. Nicolas Breitling
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  3. Alexander Maedche
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Corresponding author

Correspondence to Sven Michalczyk .

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Editors and Affiliations

  1. KU Leuven Research Center for Information Systems Engineering (LIRIS), Leuven, Belgium

    Jochen De Weerdt

  2. The University of Melbourne, Carlton, VIC, Australia

    Artem Polyvyanyy

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Michalczyk, S., Breitling, N., Maedche, A. (2022). Designing a Self-service Analytics System for Transportation Supplier Selection. In: De Weerdt, J., Polyvyanyy, A. (eds) Intelligent Information Systems. CAiSE 2022. Lecture Notes in Business Information Processing, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-031-07481-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-07481-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07480-6

  • Online ISBN: 978-3-031-07481-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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