An innovation funnel process for set-based conceptual design via DOE exploration, DEA selection and computer simulation

Abstract

This paper presents a conceptual design approach including pattern creation from designers, alternative exploration with a DOE matrix, alternative analysis via computer simulation and alternative selection by DEA analysis. Designers possessing domain knowledge create various design patterns to meet the requirements of product performance and customer expectations. Then, based on these design patterns, the alternatives, considered as decision-making units (DMUs), are extracted from various quality level combinations by following the use of the DOE matrix. The nature of the DOE matrix ensures that distinctive representatives are constructed for all design alternatives. The total alternatives (DMUs) consist of the alternatives associated with all the patterns. Computer simulation with ANSYS software is introduced to convert the quality level combination of each alternative (DMU) into simulated outputs, which are further categorised into DEA inputs and DEA outputs for DEA frontier analysis. Four DEA methods, CCR-min input, CCR-max output, BCC-min input and BCC-max output, are used for analysing typical market representatives resulting from market uncertainty. The found efficiencies are used to rank and select the explored alternatives (DMUs) for the next stage of the detailed design. A bike-frame product is chosen as an example to demonstrate the proposed approach. The results clearly show that the proposed approach enables designers to economically select appropriate design alternatives that satisfy performance expectations during the conceptual design stage.

The development of knowledge-shelf to enable an effective set-based concurrent engineering application

“…A suitable knowledge environment is important to have effective application of SBCE. The literature attempted to address knowledge provision through one of the following; database (Al-Ashaab et al, 2014;Essamlali et al, 2017;Wang and Terpenny, 2003;Wasim et al, 2013), knowledge base (Jeang and Liang, 2012;Malak et al, 2009), and case-based reasoning (CBR). A limited amount of research has been conducted on software system development by which to facilitate the application of SBCE.…”

Using Taguchi method to optimise performance and product cost at the conceptual stage of design

“…30 From a review of these techniques, Taguchi's orthogonal array approach was selected as it offered the opportunity to reduce the variance in the average cost. Previous research has shown that how the Taguchi method has been used to improve the quality characteristics of products; [31][32][33][34] this article does not focus on improving the quality of the product; however, the aim was to utilise these techniques to improve the quality of the average cost. Quality in this article is defined as reducing the variance in the cost estimate while optimising the product specification.…”

Using common weights and efficiency invariance principles for resource allocation and target setting

“…Our approach to deal with the resource allocation and target setting problem is based on the data envelopment analysis (DEA) methodology, which has been studied for a long time. DEA, first introduced by Charnes et al (1978) and further developed by Banker et al (1984), has proved to be a preferable method for organization performance evaluations and has been applied to many disciplines since its inception (Emrouznejad et al, 2008;Jeang and Liang, 2012). Although DEA was initially proposed for the purpose of performance evaluation, the empirical analysis of operating status by DEA can provide some valuable insights for resource allocation (Lu and Hung, 2010).…”

“…Beasley (2003) proposed another perspective to allocating the fixed cost, one which maximizes the overall post-allocation efficiency scores with a set of common weights. The underlying logic of common weights is that the traditional DEA methods have used extreme points to compare one DMU to the rest of the sample set (Jeang and Liang, 2012), with weights being chosen to favor the evaluated DMU, while the common weights concept approaches the evaluation process by favoring all DMUs equally. Cook and Zhu (2005) further extended the Cook and Kress (1999) approach to the output orientation and proposed an executable method in the multi-input and multi-output case.…”