Designing an AI-enabled bundling generator in an automotive case study

Spreitzenbarth, Jan ; Bode, Christoph ; Stuckenschmidt, Heiner

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URN: urn:nbn:de:bsz:180-madoc-629766
Document Type: Conference or workshop publication
Year of publication: 2023
Book title: Proceedings of the 56th Annual Hawaii International Conference on System Sciences
Page range: 4495-4504
Conference title: HICCS 2023, Hawaii International Conference on System Sciences
Location of the conference venue: Maui, HI
Date of the conference: 03.-06.01.2023
Publisher: Bui, Tung X.
Place of publication: Honolulu, HI
Publishing house: University of Hawaii at Manoa
ISBN: 978-0-9981331-6-4
Related URLs:
Publication language: English
Institution: Business School > Stiftungslehrstuhl für Procurement (Bode 2014-)
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Pre-existing license: Creative Commons Attribution, Non-Commercial, No Derivatives 4.0 International (CC BY-NC-ND 4.0)
Subject: 004 Computer science, internet
650 Management
Keywords (English): Artificial Intelligence , purchasing-marketing interface , procurement , B2B marketing , bundling problem
Abstract: Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness.

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