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

Gauss, Leandro; Lacerda, Daniel Pacheco; Miguel, Paulo Augusto Cauchick

doi:10.1007/s10845-020-01572-3

Cited by 56 publications

(56 citation statements)

References 134 publications

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“…Gauss et al [37] classified studies on product family design in the past 20 years and reviewed the methods, algorithms, and technologies used. From the optimization method perspective, the literature on product family design can be divided into two groups: the first group applies exact algorithms and the second applies heuristic algorithms [38].…”

Section: Analysis Of the Optimization Methodsmentioning

confidence: 99%

Service Family Design Optimization Considering a Multi-Server Queue

et al. 2021

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Service firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the service platform, to create a variety of differentiated services. This research considered both the effect of multi-server queues and the heterogeneous service processes in service family design problems to establish a framework of service modularization from three different perspectives-process, activity, and component. To optimize the service family design, a nonlinear integer-programming model was established to determine the optimal configurations of modules and prices for the service family and the optimal number of servers. The model is transformed into a linear form, and thus, can be solved using a commercial optimization software for smallscale problems. An improved genetic algorithm integrated with a neighborhood search was further developed to solve large-scale problems. The correctness of the linearized model and the effectiveness of the metaheuristic algorithm were demonstrated through case studies and numerical experiments.

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Section: Analysis Of the Optimization Methodsmentioning

confidence: 99%

Service Family Design Optimization Considering a Multi-Server Queue

et al. 2021

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“…Knowledge is usually linked to a discipline (Gibbons, Trow, Scott & Schwartzman, 1994;Veit, Lacerda, Camargo, Kipper & Dresch, 2017). Organizing research into classes of problems can help focus efforts and facilitate the development of problem-oriented research (Gauss, Lacerda & Miguel, 2020).…”

Section: Open Accessmentioning

confidence: 99%

Impact beyond impact factor: The Design-science way

Lacerda

Dresch

2020

BAR, Braz. Adm. Rev.

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“…Over the past 30 years, a significant amount of research has been conducted within the area of modularity (Gauss et al, 2020; Jiao et al, 2007; Otto et al, 2016; Pirmoradi et al, 2014). Scholars have developed frameworks and tools for supporting the development of modular products.…”

Section: Introductionmentioning

confidence: 99%

Cross domain modularization tool: Mechanics, electronics, and software

Askhøj

Christensen

Mortensen

2021

Concurrent Engineering

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Modularization is a strategy used for handling the demand for external complexity with less internal complexity, which leads to higher profits and more efficient product development processes. However, modularity is often driven in silos, not crossing into the engineering fields of mechanics, electronics, and software. Therefore, we present the MESA (Mechanics, Electronics, and Software Architecture) tool—a tool that can be used to visualize modular product architectures across mechanics, electronics, and software. The tool demonstrates how a change in one domain affects the rest and how well aligned the modularity in the different domains is. The tool has been tested in two case companies that were used for case application and has helped provide information for making key design decisions in the development of new product families.

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

Cited by 56 publications

References 134 publications

Service Family Design Optimization Considering a Multi-Server Queue

Service Family Design Optimization Considering a Multi-Server Queue

Impact beyond impact factor: The Design-science way

Cross domain modularization tool: Mechanics, electronics, and software

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