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Balancing Commonality and Performance within the Concurrent Design of Multiple Products in a Product Family

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Abstract

Product family design involves the concurrent design of multiple products based on a common product platform to satisfy a variety of markets. The success of the resulting product family relies heavily on properly balancing the commonality of the product platform with the individual product performance within the product family. To help resolve this tradeoff, we present the Product Variety Tradeoff Evaluation Method for assessing alternative product platform concepts with varying levels of commonality. Two examples are presented to demonstrate the proposed method at both the detailed and the early stages of design. The redesign of a planetary gear transmission for a family of four cordless drills demonstrates the use of the method in the detailed stages of design, while the design of a family of three General Aviation Aircraft demonstrates the use of the method in the preliminary stages of design. The emphasis in this paper is on the effectiveness of the proposed method in evaluating this tradeoff and not on the results of the examples, per se.
TO CITE THIS WORK:
Simpson, T.W, Seepersad, C.C. and Mistree, F., 2001, Balancing Commonality and
Performance within the Concurrent Design of Multiple Products in a Product Family," Concurrent
Engineering Research and Applications, vol. 9, no. 3, pp. 177-190.
... A metric to assess the individual performance of each variant is important to be defined as well. Within this context, Simpson et al. [81,88] proposed a Product Variety Index which evaluates the trade-off between commonality and individual performance compromises. ...
... Quantifying the performance compromise and potential tradeoffs between benchmark designs and engine variants requires the introduction of two indices, as suggested by Simpson et al. [88]: Non-Commonality Index (NCI) and Performance Deviation Index (PDI). They are considered as the most dominant metrics to evaluate the feasibility of an engine family design. ...
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... These algorithms include GAs (Alexouda and Paparrizos 2001;Balakrishnan et al. 2004;Balakrishnan et al. 2006;Balakrishnan and Jacob 1995;Balakrishnan and Jacob 1996;Steiner and Hruschka 2003), evolutionary algorithms (Alexouda 2004(Alexouda , 2005, ant colony optimization (Albritton and McMullen 2007), PSO (Tsafarakis et al. 2011(Tsafarakis et al. , 2013, SA (Tsafarakis 2016), differential evolution (Tsafarakis et al. 2020), Tabu search , imperialist competitive algorithm (Liu et al. 2021), and clonal selection algorithms (Pantourakis et al. 2021). In the engineering field, most product line design literature deals with costs and flexibility issues in platform management, in which the balance between the commonality of the product platform and the engineering performance of product variants is linchpin (Ali et al. 2021;D'Souza and Simpson 2003;Liu et al. 2011;Simpson et al. 2001;Thevenot et al. 2007;Thevenot and Simpson 2006;Du et al. 2019). ...
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... From the perspective of marketing, marketing managers focus on portfolio optimization considering customer preferences and purchase behaviors with the objective of maximizing market share, profit, or customer utility (Green and Krieger, 1985;. In the engineering field, production managers pay more attention to the costs and flexibility issues associated with product portfolio and aim to reduce the engineering costs at the stage of manufacturing (Simpson et al., 2001;Gauss et al., 2021). Jiao and Zhang (2005) examined the PPP problem with customer and engineering interaction, and formulated a maximizing expected shared surplus model for leveraging both the marketing and engineering concerns. ...
... These algorithms include GAs (Alexouda and Paparrizos, 2001;Balakrishnan et al., 2004;Balakrishnan et al., 2006;Balakrishnan and Jacob, 1995;Balakrishnan and Jacob, 1996;Steiner and Hruschka, 2003), evolutionary algorithms (Alexouda, 2004;Alexouda, 2005), ant colony optimization (Albritton and McMullen, 2007), PSO (Tsafarakis et al., 2011;Tsafarakis et al., 2013), SA (Tsafarakis, 2016), differential evolution (Tsafarakis et al., 2020), Tabu search , imperialist competitive algorithm (Liu et al., 2021), and clonal selection algorithms (Pantourakis et al., 2021). In the engineering field, most product line design literature deals with costs and flexibility issues in platform management, in which the balance between the commonality of the product platform and the engineering performance of product variants is linchpin (Ali et al., 2021;D'Souza and Simpson, 2003;Liu et al., 2011;Simpson et al., 2001;Thevenot et al., 2007;Thevenot and Simpson, 2006;Du et al., 2019). ...
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Product portfolio planning (PPP) is one of the most critical decisions for companies to gain an edge in the competitive market. It seeks for the optimal combination of products and attribute levels offered for customers in the target market, which is an NP-hard combinatorial optimization problem. In this paper, we propose a probability-based discrete particle swarm optimization (PDPSO) algorithm to solve the PPP problem. In PDPSO, the particle is encoded as discrete values, which can be straightforwardly used to represent the product portfolio with discrete attributes. PDPSO adopts a probability-based mechanism to update particles. Specifically, a probability vector is used to decide the probability of three search behaviors, i.e., learning from the personal best position, global best position, or random search. In experiments, we have compared the search performance of PDPSO with that of a genetic algorithm (GA) and a simulated annealing (SA) algorithm on generated PPP problem cases with different sizes. The results indicate that PDPSO obtains significantly better optimization results than GA and SA in most cases and obtains desirable/near-optimal solutions on various PPP problem cases. A case study of notebook computer portfolio planning is also presented to illustrate the efficiency and effectiveness of PDPSO.
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