Title
Future Alternatives for Automotive Configuration Management.
Abstract
This research investigates the phenomenon of increasing cost that results from growing product complexity. To explore this phenomenon, interviews with ten senior managers and engineers with long experience in the automotive business were conducted at a car manufacturer. The interviewees agreed that configuring cars becomes more time-consuming and costly with increasing product complexity. In this paper we reason that there are upcoming solutions suitable for complex configurations. As a basis for this, we propose a distinction between limiting and managing product complexity, and stress that these approaches affect internal cost over time differently. If companies choose to limit complexity we suggest optimizing configuration rules, reducing variants or both. Conversely, we propose and contrast two different configuration strategies for managing complexity, 1) the Modular approach, and 2) the Configurable Component (CC) approach. The Modular approach may limit the ability to change. However, only few changes in manufacturing systems are needed. The CC approach is a long-term fully flexible configuration approach prepared for changes. As a drawback, the CC approach may involve high fixed costs due to the need for suitable manufacturing systems. We conclude that both the Modular approach and the CC approach are feasible for managing complexity. In a long-term perspective, it might be necessary to be able to prepare for change and reduce internal cost over time. The choice of limiting or managing complexity might therefore be a demarcation of future competitiveness.
Year
DOI
Venue
2014
10.1016/j.procs.2014.03.014
Procedia Computer Science
Keywords
Field
DocType
Configuration,Product Complexity,Internal Cost,Modular Platform,Flexible Product Architecture
Drawback,Computer science,Risk analysis (engineering),Artificial intelligence,Configuration management,Complexity management,Manufacturing systems,Simulation,Fixed cost,Modular design,Limiting,Machine learning,Automotive industry
Conference
Volume
ISSN
Citations 
28
1877-0509
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jonas Landahl111.04
Dag Bergsjö2143.21
Hans Johannesson3316.02