Studying Dynamic Change Probabilities and Their Role in Change Propagation

Abstract

Long-lived systems are likely to experience many independent modifications during their lifecycles. Prior literature provides tools for predicting how a change in a fixed system is likely to propagate, but these tools do not address change propagation across multiple, independent modifications. The phenomenon of a modification consuming excess, thereby increasing the likelihood of change propagation in future modifications, is studied in this work as dynamic change probabilities (DCP). This research builds on change propagation techniques, network theory, and excess to provide high-level guidance about how DCP may alter change propagation within a system over time. A sample of existing and synthetic systems are explored, as we show that the rate of change likelihood increase following a modification depends on the number of components (nodes), the dependencies between components (edges), and initial change propagation probability values (edge weights). Results also show that excess placement in specific components, and the presence of system hubs (high-degree components), can mitigate the impact of excess consumption when multiple system modifications are made over time.

A dynamic model for engineering change propagations in multiple product development stages

“…When the value reaches 0.15, no more than 8% and 6% of total engineering entities will be infected in the respective sequential and concurrent change propagation processes. Therefore, if the excess for each affected entity can be preset with enough values, which means that the threshold value can be bigger in this research, its change effects can be restrained within a very limited range, and this is consistent with what was found in Long and Ferguson (2020). Furthermore, big threshold value can reduce change ripples and blossoms, as illustrated in Figure 18, and emergent changes can decrease in the change propagation process.…”

“…Considering propagation dynamic models describing evolution trends of propagation characteristics such as change durations and number of changes with respect to the product development time are highly relevant with the calculations of propagation impacts and entity recovery rate of changing state to the state of waiting for changes, so propagation dynamic models can be built based on the change propagation prediction methods from which the above information can be inferred (Clarkson et al , 2004; Lee et al , 2004; Cheng and Chu, 2012; Chua and Hossain, 2012; Koh et al , 2012; Li et al , 2012, 2016; Morkos et al , 2012; Yang and Duan, 2012; Hamraz et al , 2013 b ; Li and Zhao, 2014; Wynn et al , 2014). Recently, Long and Ferguson studied the dynamic change probabilities in engineering changes of long-lived systems by building on change propagation techniques, network theory and excess, but the change propagation dynamics within a limited time interval was not given (Long and Ferguson, 2020). In terms of propagation phenomena in the engineering and social fields, there are some similarities shared by engineering change propagations and infectious disease transmission.…”

“…For example, both engineering change propagations and disease transmission proceed in a discrete way; there are outbreaks in the propagation or transmission processes if poor management strategies or methods are adopted; both need appropriate predictions, containments, or deployments of resources in order to keep the process in a controllable state; both phenomena can be modeled using graph theory, etc. Considering their enormous influences on the engineering development and social stability, many references can be found in the literature focusing on the change propagation prediction and analysis (Clarkson et al , 2004; Eckert et al , 2004; Lee et al , 2004, 2010; Pahl et al , 2007; Giffin et al , 2009; Siddiqi et al , 2011; Cheng and Chu, 2012; Chua and Hossain, 2012; Hamraz et al , 2012; Koh et al , 2012; Li et al , 2012, 2016; Morkos et al , 2012; Pasqual and De Weck, 2012; Vianelo and Kristensen, 2012; Yang and Duan, 2012; Ahmad et al , 2013; Hamraz et al , 2013 a , 2013 b ; Li and Zhao, 2014; Wynn et al , 2014; Long and Ferguson, 2020), dynamical modeling of epidemic outbreaks (Wang et al , 2018). However, they also have some significant differences: • First, in the compartmentalization of individual objects in both cases, engineering objects can be divided into two groups, namely affected and susceptible, while human objects can be divided into two, three, or more groups according to different diseases, for instance, SIS (Susceptible, Infected, Susceptible), SIR (Susceptible, Infected, Removed), SEIR (Susceptible, Exposed, Infected, Removed), etc.…”

Concepts of change propagation analysis in engineering design

“…For example, as more design issues are progressively taken into account, a denser structure of dependencies is generated in the design and changes become more likely to propagate. Margin may also be reduced over time as a design is further optimised, causing changes to propagate more easily (Long and Ferguson 2020). Parts may become more sensitive to changes over time as they are further detailed and optimised.…”

“…Some consideration of evolving dependency structures is provided by e.g. Yu et al (2017) and more recently, dynamic change probabilities are discussed by Long and Ferguson (2018), Ferguson (2020), andLi et al (2021a). However, the majority of reviewed approaches do not explicitly address this issue.…”