Cost-effective propagation paths for multiple change requirements in the product design

Abstract

Design projects have been surrounded by tight schedule and cost overruns. Therefore, it is indispensable to resolve the design changes in an economical way. This work introduces an advanced technique to assess and optimize change propagation paths for multiple change requirements occurring simultaneously during the product development process. A novel multiple change requirement algorithm and a mathematical model considering the overall propagated risk are developed, to explore cost-effective change propagation paths in terms of lead time. The risk is quantified with regard to propagation likelihood and change impact, which results in re-work. Design structure matrix framework is used to capture the dependencies between components. It is revealed that the change requirements execution sequence has a significant effect on the total number of change propagation paths, change steps, distinct change components, and completion time. This approach is illustrated by a case study, which indicates that the proposed methodology can assist the designer in exploring and selecting optimal change propagation paths.

Characteristics of changeable systems across value chains

“…The dependencies between product components are assessed by the methods with the lowest granularity (Clarkson et al, 2004;Koh et al, 2013;Keller et al, 2005;Ullah et al, 2017;Ou-Yang and Cheng, 2003;Lee and Hong, 2015;Ullah et al, 2018;Koh, 2017). Methods with a higher granularity decompose the product into different layers (structure, behaviour, function) (Hamraz and Clarkson, 2015), into product parameters (Masmoudi et al, 2017;Yin et al, 2017) and into degrees of freedom (Schuh et al, 2017).…”

“…One of the most established is the change propagation method (CPM) that identifies indirect change relationships and determines the risk for change propagation (Clarkson et al, 2004;Keller et al, 2005;Koh, 2017;Schuh et al, 2017;Masood et al, 2017a). Similarly, a variety of other algorithms exist that identify the risk of indirect change relationships (Ullah et al, 2018;Li et al, 2017;Shankar et al, 2017). Delta DSMs identify the change propagation risk by overlaying the initial dependency DSM with higher order DSMs.…”

Multi‐objective particle swarm optimisation of complex product change plan considering service performance

“…Compared with other models, the complex product network model can better reflect the coupling strength between different parts [13]. Existing work mainly uses the design structure matrix (DSM) to quantitatively represent the dependencies between parts [14,15]. To address the shortcoming of DSM that is only suitable for some specific product instances, Diagne et al [16] proposed an extended conceptual design semantic matrix (MSX-CDSM).…”

Design change prediction based on social media sentiment analysis

“…Approaches to predict design change and its propagation include analyzing past changes (Suh et al , 2007; Yin et al , 2017) and uncovering existing change dependencies (Clarkson et al , 2004; Xie and Ma, 2016; Lee and Hong, 2017; Chen et al , 2020). The metric used to examine design change include engineering tolerance (Hamraz et al , 2013), workload (Tang et al , 2016), staff affected (Koh et al , 2015), lead time (Ullah et al , 2018), manufacturing (Siddharth and Sarkar, 2017), lifecycle performance (Cardin et al , 2013), and profit margin (Yassine and Khoury, 2021). Endogenous change dependencies between product components can be extracted from Product Data Management systems and engineering databases to support design change analysis (Jarratt et al , 2011).…”