Graph-based similarity analysis of BOM data to identify unnecessary inner product variance

DS 87-1 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 1: Resource Sensitive Design, Design Research Applications and Case Studies, Vancouver, Canada, 21-25.08.2017

Year: 2017
Editor: Anja Maier, Stanko Škec, Harrison Kim, Michael Kokkolaras, Josef Oehmen, Georges Fadel, Filippo Salustri, Mike Van der Loos
Author: Schmidt, Michael; Gehring, Benedikt; Gerber, Jan-Sebastian; Stocker, Johannes Michael; Kreimeyer, Matthias; Lienkamp, Markus
Series: ICED
Institution: 1: Technical University of Munich, Germany; 2: MAN Truck & Bus AG, Germany
Section: Resource Sensitive Design, Design Research Applications and Case Studies
Page(s): 489-498
ISBN: 978-1-904670-89-6
ISSN: 2220-4342


This paper contributes to the fields of variant management and product family design. The focus lies on analysing historically grown product portfolios in order to reduce unnecessary inner variety. Such inner variety adds no value to the customer, yet it induces complexity costs within the whole company. Increasing transparency in documented product variants is key when applying standardisation or modularisation methods as part of variant management. Studies of literature and industrial practice at a major German truck manufacturer show that analysing product structure information from BOM data yields the potential to point out promising candidates in companies’ portfolios for effective standardization or modularisation. For modelling and analysing highly variant and complex product structures, we employ graph-based modelling of BOM data in combination with a state-of-the-art tree matching algorithm for similarity calculations. Actual product data of a truck manufacturer serves as a case study. Thereby, we propose a generally applicable approach that enables intuitive handling of large amounts of product family data and that effectively supports variety reduction efforts.

Keywords: Product families, Product structuring, Complexity, Data analysis, Data visualization


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