Analytical Methods for Change Propagation Analysis

Clarkson et al. proposed a method called change prediction matrix (CMP) to trace change propagations and analyse the impacts they may cause (Clarkson et al., 2004). The method transformed the dependency relationship between components in a product model into a design structure matrix (DSM). Based on this matrix, the likelihood that potential change propagations might happen between components were estimated. Also in the same way, the impacts of these potential change propagations were estimated. By combining the change likelihood matrix and the change impact matrix, a change risk matrix was generated. With help of visualising method, change propagation paths and their relative risks were clarified.

In another research, Eckert et al. (2004) proposed a method to analyse change propagation at a parametric level and identify four types of change propagation behaviours, namely constants, absorbers, carriers, and multipliers. These four types of change propagation behaviours helped to analyse change propagations that cross multi-levels. Four types of change propagation behaviours represented four situations when a change of a component propagating to another component via some other components. They included changes being passed without effect, being reduced or eliminated, being replaced with new changes from the intermediate component, and being enhanced. This method was also integrated with the CMP method to enhance the performance of change propagation analysis in product conceptual design (Keller et al., 2009).

Suh et al. proposed a method, called change propagation index (CPI), to measure the degree of change propagation caused by an element in the system when there was an engineering change imposed (Suh et al., 2007). They adopted Eckert's definition of change behaviours that an element performed in response to engineering change. As

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reviewed above, the change reactions are defined as multiplier, carrier, absorber and constant (Eckert et al., 2004). The degree of change propagation (the CPI) of an element is calculated in a matrix by subtracting the number of elements that are changed by the element from the number of elements causing changes on the element. If the result is a positive value, it means the element is a multiplier. A negative value means it is an absorber. A zero means it is a carrier. If there is no element causing change on the element, nor changed by the element, that means it is a constant. They also estimated change cost of each element in the system (Kswitch). By combining the

CPI and Kswitch, the whole impact of a change propagation route can be estimated in

terms of the change costs. Although this method qualitatively formalises Eckert’s definition of change behaviours, it is far from accurate. The impact of change propagation from one element to another cannot be simply estimated by a binary value (0 or 1) since the impact depends on the essence of the interaction or connection between them. Also, the change impact largely depends on the extent to which the causing element is going to change.

Researchers in MIT conducted an investigation in engineering change management with 41500 change requests (Giffin et al., 2009). They claimed that it was the biggest data set that had been investigated among the current published product design literature. They used the change prediction matrix (CPM), a method proposed by Clarkson et al. (2004), to analyse the large set of engineering change requests. One interesting finding from this analysis is that there are a lot of change propagations where direct structural connections do not seem to exist. This shows some relationships between components may not be identified in the CMP method but they actually cause change propagation.

They also used graph theory to analyse the data set and tried to find engineering change patterns. They found that most of the components were related to fewer than 10 changes while a small number of components related to a huge number of changes. They also recognised that the engineering change network was very complex and basic change patterns might be useful for clarifying the relations between engineering changes. They proposed three types of change patterns (motifs), namely, the ‘1-Motifs’ pattern representing a single and isolated engineering change, the ‘2-Motifs’ pattern

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representing the parent-child type change network or the sibling type change network, and the ‘3-Motifs’ pattern representing a hybrid change network of the parent-child type change and the sibling type change. However, only the parent-child change network was used to analyse change propagation. The other patterns were either too simplified or too complex to use for analysis.

Following on from the work of Suh et al. (2007), they enhanced the algorithm to calculate the change propagation index (CPI). The CPI was calculated by dividing the difference between the incoming changes and the outgoing changes to an element by the sum of both of them. Therefore, it came out with a value ranging from -1 to 1. The value could be used to indicate whether it is a multiplier, an absorber, a carrier or a constant (Eckert et al., 2004). Although this improved method still cannot solve the problem with Suh’s method which, as mentioned above, is difficult to analyse and predict change propagation during product development, statistically it can help engineers identify which part of the system is more likely to have change propagation happening based on the large sample of engineering change cases.

Koh and Clarkson (2009) proposed a modelling method for engineers to track change propagation during product design. Rather than just focusing on change propagation in physical components as in some other similar methods, their method considered change propagation analysis as a way to pre-examine the design plan and improve resource allocation before going to embodiment design. This method employed a multiple domain matrix to model and to qualify interdependent and interactional relationships between product attributes, design features and components. By using the change prediction matrix (Clarkson et al., 2004), change propagations between components were identified and their impacts on related design features were quantified. Then an algorithm was developed to revise the performance ratings on product attributes with consideration of change impacts on related design features. As a result, a performance rating chart was populated to compare performance ratings on product attributes with and without change propagation analysis. The method defined a clear and intuitive way for engineers to analyse design change propagation and to adjust their expectations of performance of product attributes, which could benefit optimisation of design plan and resource allocation. However, there are also some

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difficulties in using the method. Predefinitions of relationships between product attributes, design features and components are roughly estimated so that the accuracy of performance rating calculation is questionable. Also according to responses from engineers, the method is not easy to use since the modelling approach is too complex and not user-friendly.