Concepts of change propagation analysis in engineering design

Abstract

Interest in change propagation analysis for engineering design has increased rapidly since the topic gained prominence in the late 1990s. Although there are now many approaches and models, there is a smaller number of underlying key concepts. This article contributes a literature review and organising framework that summarises and relates these key concepts. Approaches that have been taken to address each key concept are collected and discussed. A visual analysis of the literature is presented to uncover some trends and gaps. The article thereby provides a thematic analysis of state-of-the-art in design change propagation analysis, and highlights opportunities for further work.

Incorporating changeability for value-robust product-service systems: an integrative review

“…Performing change propagation analysis (CPA) enables the development team to understand how change spreads through and affects the system, further supporting the system to be designed to minimize the risks associated with change propagation (Clarkson, Simons & Eckert 2004). CPA entails a four-step process: mapping system and subsystem interdependencies, populating the models, analyzing change propagation and visualizing the results (Brahma & Wynn 2023). Mapping interdependencies involves bringing attention to the direct and indirect connections among different components within the system.…”

“…However, the core of CPA is to embed changeability into the system, rendering it more resilient and indifferent to changes. The data required to populate the propagation model can be generated by analysis, workshops, expert opinions or historical change data (Clarkson et al 2004; Brahma & Wynn 2023). Once the model is populated, it can be analyzed via several techniques such as network analysis, Monte Carlo, manual tracing, and so forth, and the results can be visualized (Brahma & Wynn 2023).…”

“…The data required to populate the propagation model can be generated by analysis, workshops, expert opinions or historical change data (Clarkson et al 2004; Brahma & Wynn 2023). Once the model is populated, it can be analyzed via several techniques such as network analysis, Monte Carlo, manual tracing, and so forth, and the results can be visualized (Brahma & Wynn 2023). For instance, the change prediction method (Clarkson et al 2004) is a classical network analysis method that highlights the combined likelihood and impact of change to estimate the risk associated with a specific change option.…”

A tradespace exploration approach for changeability assessment from a system-of-systems perspective: application from the construction machinery industry

“…However, for quantifying system changeability, change options need to be identified. A recent literature review details several methods and tools to identify appropriate change candidates because the effect of change propagate (Brahma and Wynn, 2023). In early design stages, however, a detailed system representation is often missing, making an explicit evaluation of design variables a viable choice for change option identification (Cardin, 2013).…”

Function Driven Assessment of Manufacturing Risks in Concept Generation Stages

“…CPM, in addition, also considers the knock-on effect of change and therefore propagates the probability to other connected components. Since the CPM was first published by Clarkson et al (2004), significant advances have been made in the approach, with ICED23 many authors reporting improvements in it (Brahma and Wynn, 2022). However, a review of the literature does not reveal any work where CPM is used to assess the combined risk of a product and a manufacturing system architecture.…”