Data-Driven Product Family Modeling with Feedback

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

Abstract

In order to become a successful mass customizer, companies must be in control of their product variety. This is to ensure that the product variety is sufficient in order to satisfy the range of customer demands but also to ensure that there is no excess variety, which compromises efficiency in business processes and manufacturing processes. This is often addressed by establishing product family models which represent the variety in a specific product family and any constraints there may be. In this paper, we first present a literature review of the currently existing product family modeling methods, in which it is concluded that most current methods are stand-alone, document-based methods, which largely do not consider integration with other product data systems or feedback from production and products. We then propose a number of new approaches to product family modeling, which utilizes data from other systems such as ERP and PDM, which enables a more fact-based modeling process. Furthermore, the proposed approach enables feedback loops into the product family model, which is possible due to advances in connectivity (IOT applications). The new approach will enable better qualification of decisions regarding product variety management once implemented.

Original languageEnglish
Title of host publicationCustomization 4.0 : Proceedings of the 9th World Mass Customization & Personalization Conference (MCPC 2017), Aachen, Germany, November 20th-21st, 2017
Number of pages9
PublisherSpringer Publishing Company
Publication date21 Jun 2019
Edition1
Pages469-478
ISBN (Print)978-3-319-77555-5
ISBN (Electronic)978-3-319-77556-2
DOIs
Publication statusPublished - 21 Jun 2019
Event9th World Mass Customization & Personalization Conference (MCPC 2017) -
Duration: 20 Nov 201721 Nov 2017

Conference

Conference9th World Mass Customization & Personalization Conference (MCPC 2017)
Period20/11/201721/11/2017
SeriesSpringer Proceedings in Business and Economics
Number1
ISSN2198-7246

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