Management of Product Configuration Conflicts to Increase the Sustainability of Mass Customization

An important role in product variety management is finding an accurate variety extent to which the product matches the consumer’s expectations. In principle, customers prefer to have more rather than less versions of a product from which to choose. This motivates producers to offer a richer variety of goods. As a consequence, it brings a large amount of manufacturing complexity, and configuration conflicts may frequently occur. In order to avoid a situation in which a customer will select mutually incompatible components, product configurators usually recommend corrective actions for generating valid configurations. Nevertheless, the presence of infeasible configurations in customer options are negatively perceived by customers, and therefore it has an unfavorable impact on the sustainability of mass customization. One way to solve this problem is to eliminate, or at least reduce, mutually incompatible components. When considering the fact that eliminating all incompatible components may cause a rapid decrease in product variety, then the reduction of incompatible components can help to solve the product configuration problem. The proposed method aims to find a trade-off solution between minimizing configuration conflicts and maintaining a sufficient level of mass customization. Moreover, two supplementary methods for the determination of infeasible product configurations are proposed in this paper. The applicability and effectiveness of the proposed methods are demonstrated by two practical examples.

[1]  Alain Bernard,et al.  Product Variety Management , 1998 .

[2]  Michel Aldanondo,et al.  Concurrent product configuration and process planning: Some optimization experimental results , 2014, Comput. Ind..

[3]  Chih-Hsing Chu,et al.  Economical green product design based on simplified computer-aided product structure variation , 2009, Comput. Ind..

[4]  Fabrizio Salvador,et al.  Application support to product variety management , 2008 .

[5]  Yiliu Liu,et al.  Multi-objective product configuration involving new components under uncertainty , 2010 .

[6]  Erwin Rauch,et al.  Trends towards Distributed Manufacturing Systems and modern forms for their design , 2015 .

[7]  Roger Jianxin Jiao,et al.  Integrated Vehicle Configuration System - Connecting the domains of mass customization , 2010, Comput. Ind..

[8]  Michael Funke,et al.  Product Variety and Economic GrowthEmpirical Evidence for the OECD Countries , 2000 .

[9]  Nam P. Suh,et al.  Axiomatic Design Theory for Systems , 1998 .

[10]  Giovani J.C. da Silveira,et al.  Mass customization: Literature review and research directions , 2001 .

[11]  Daniel Mailharro,et al.  A classification and constraint-based framework for configuration , 1998, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[12]  Yoram Koren,et al.  Product variety and manufacturing complexity in assembly systems and supply chains , 2008 .

[13]  Erwin Rauch,et al.  Design of a Network of Scalable Modular Manufacturing Systems to Support Geographically Distributed Production of Mass Customized Goods , 2013 .

[14]  N. Suh Complexity in Engineering , 2005 .

[15]  Vladimir Modrak,et al.  Application of Axiomatic Design-based Complexity Measure in Mass Customization☆ , 2016 .

[16]  Yi Wang,et al.  Industry 4.0: a way from mass customization to mass personalization production , 2017 .

[17]  Gerhard Friedrich,et al.  Uml as Domain Specific Language for the Construction of Knowledge-Based Configuration Systems , 1999, Int. J. Softw. Eng. Knowl. Eng..

[18]  Michel Aldanondo,et al.  Towards an association of product configuration with production planning , 2010 .

[19]  Vladimir Modrak,et al.  Selection and Peer-review under Responsibility of the International Scientific Committee of " 9th Cirp Icme Conference " the Influence of Mass Customization Strategy on Configuration Complexity of Assembly Systems , 2022 .

[20]  Xiang Li,et al.  Automating knowledge acquisition for constraint‐based product configuration , 2008 .

[21]  Lars Hvam,et al.  Formal computer-aided product family architecture design for mass customization , 2015, Comput. Ind..

[22]  Mehmet Polat Saka,et al.  Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution , 2016, Adv. Eng. Softw..

[23]  Gilmore Jh,et al.  The four faces of mass customization. , 1997 .

[24]  Dong Yang,et al.  A constraint satisfaction approach to resolving product configuration conflicts , 2012, Adv. Eng. Informatics.

[25]  Ming Dong,et al.  A dynamic constraint satisfaction approach for configuring structural products under mass customization , 2012, Eng. Appl. Artif. Intell..

[26]  Dao Yin,et al.  State-of-the-art review of customer to business (C2B) model , 2019, Comput. Ind. Eng..

[27]  Yue Maggie Zhou,et al.  Product Variety, Sourcing Complexity, and the Bottleneck of Coordination , 2017 .

[28]  Günther Schuh,et al.  Increasing Collaboration Productivity for Sustainable Production Systems , 2015 .