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Erschienen in: Journal of Intelligent Manufacturing 1/2021

04.05.2020

Module-based product family design: systematic literature review and meta-synthesis

verfasst von: Leandro Gauss, Daniel P. Lacerda, Paulo A. Cauchick Miguel

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 1/2021

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Abstract

Increased demand for a greater variety of products has forced many companies to rethink their strategies to offer more product variants without sacrificing production efficiency. In this context, research has found that such a trade-off can be properly managed by exploiting the module-based product family (MBPF) design. Over the years, active work in developing methods to design MBPFs has been carried out. Nevertheless, many of them have been created, and consequently exist, in isolation from one other. As a result, the adoption of these methods in industry and academy alike is inhibited by the seemingly broad array of material without a coherent organizing structure. To bridge this gap, this paper performs a systematic literature review and a meta-synthesis, wherein 72 methods to design MBPFs and their respective instances are connected in the form of a functional model and structured classes of design problems. These entities together serve as a meta-method for organizing the research on MBPF design, from which it was possible to identify the common underlying structure among the methods developed over the past 20 years. The main contributions of this work include: (1) constructing a functional model that connects the design methods for MBPFs; (2) suggesting structured classes of design problems that complement the functional model by cataloging the techniques meant to execute each sub-function of the model; (3) proposing a construction heuristic to build and assess functional models and classes of design problems.

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Metadaten
Titel
Module-based product family design: systematic literature review and meta-synthesis
verfasst von
Leandro Gauss
Daniel P. Lacerda
Paulo A. Cauchick Miguel
Publikationsdatum
04.05.2020
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 1/2021
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-020-01572-3

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