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Designing a Self-Service Analytics System for Transportation Supplier Selection

Michalczyk, Sven 1; Breitling, Nicolas; Mädche, Alexander 1
1 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)


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 de-partments are responsible for covering transportation demands. For supplier selection, they must allocate hundreds of items to an optimal set of suppli-ers. In interviews, we examined a high dependency on Data Scientists and Data Engineers. Currently, BAs achieve only non-optimal solutions be-cause they lack the required knowledge and adequate tools to perform this analytical process independently. Against this backdrop, we present the de-sign and evaluation of a self-service analytics (SSA) system that helps BAs to select suppliers for transportation demands considering multiple deci-sion criteria. We formulate a linear optimization problem that BAs can par-ametrize and analyze with our SSA system. Our proposed SSA system ena-bles BAs to improve the supplier selection process and showcases the po-tential of SSA systems to utilize optimization models.

Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 06.2022
Sprache Englisch
Identifikator ISBN: 978-3-031-07480-6
ISSN: 1865-1348
KITopen-ID: 1000147612
Erschienen in Intelligent Information Systems : CAiSE Forum 2022, Leuven, Belgium, June 6–10, 2022, Proceedings. Ed.: J. De Weerdt
Veranstaltung 34th International Conference on Advanced Information Systems Engineering (CAISE 2022), Löwen, Belgien, 06.06.2022 – 10.06.2022
Auflage 1
Verlag Springer
Seiten 64–72
Serie Lecture Notes in Business Information Processing ; 452
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