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Designing a Self-service Analytics System for Supply Base Optimization

Michalczyk, Sven; Nadj, Mario; Beier, Harald; Mädche, Alexander

Abstract (englisch):

Reducing the number of suppliers – a process known as supply base optimization – is crucial for organizations to achieve better quality, higher service levels, and lower prices. The buyers in the role of the business analyst in corporate purchasing departments are responsible for this process and usually consider various selection criteria. Their decisions rely on accessing and analyzing large amounts of data from different source systems, but typically, they lack the necessary technological and analytical knowledge, as well as adequate tools, to do this effectively. In this paper, we present the design and evaluation of a self-service analytics (SSA) system that helps business analysts manage the maintenance, repair, and operations (MRO) supply base. The system recommends shifting purchasing volume between suppliers based on a machine learning (ML) algorithm. The results demonstrate the potential of SSA systems in facilitating ML model consumption by business analysts to perform supply base optimization more effectively.

DOI: 10.1007/978-3-030-79108-7_18
Zitationen: 2
Zitationen: 3
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 28.06.2021
Sprache Englisch
Identifikator ISBN: 978-3-030-79107-0
KITopen-ID: 1000134358
Erschienen in CAiSE '21- 33rd International Conference on Advanced Information Systems Engineering, MELBOURNE, 28 JUNE 2021 - 2 JULY 2021
Veranstaltung 33rd International Conference on Advanced Information Systems Engineering: Intelligent Information Systems (CAiSE 2021), Melbourne, Australien, 28.06.2021 – 02.07.2021
Seiten 154-161
Serie Lecture Notes in Business Information Processing ; 424
Vorab online veröffentlicht am 15.06.2021
Nachgewiesen in Scopus
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