Research on changes in design has mainly focused on the product domain, which is the manifestation of the design process. This article investigates changes in the process domain, which describes the execution of coordinated and concurrent design activities through interdisciplinary teams. More specifically, this article focuses on changes in the iterative behavior of activities as one of the key levers determining the performance of complex concurrent design processes. In activity network-based models of design processes, the occurrence of such behavior is often expressed probabilistically through iteration-likelihoods. First, the impact of iteration-likelihood changes on the effort and duration of both individual activities and the overall design process is examined through stochastic analysis. Consequently, a method for the investigation of such changes is developed, which grounds on an experimental approach using Monte Carlo simulation of activity network-based process models. The method is applied to the design process of a high-speed machining device for the manufacturing of planetary-ball-bearing housings. This analysis results in a two-dimensional criticality ranking of potential iteration-likelihood changes and in the identification of the most affected individual activities. The article concludes with managerial implications for process planning and improvement and discusses which design activities need to be targeted by project management to prevent and react to critical iteration-likelihood changes.

Engineering changes (ECs) are raised throughout the product life cycle and their management can determine the commercial success of products. A well-established method to support engineering change management (ECM) is the change prediction method (CPM). The function–behaviour–structure (FBS) linkage method enhances CPM with an FBS scheme and allows more detailed modelling and analysis of ECs. The goal of this paper is to provide an industrial evaluation of the FBS Linkage method. For that purpose, we provide first an overview of the FBS Linkage method, before applying it to a diesel engine design and evaluating it by a group of 10 experienced engineers from the diesel engine manufacturer. Overall, the engineers favoured the FBS Linkage method and ranked it on average 3.7 out of 5.0 against a set of 25 different requirements for ECM methods. The evaluation underlines the benefits of the method in terms of a systematic way for capturing, explaining and transferring knowledge about the product and effects of ECs on it. Identified improvement areas include more guidelines on the scope of the method, reduction of the effort required to build FBS Linkage models, and an integration of the method into other applied systems.

Engineering changes are essential for any product development, and their management has become a crucial discipline. Research in engineering change management has brought about some methods and tools to support dealing with changes. This work extends the change prediction method through incorporation of a function–behaviour–structure (FBS) scheme. These additional levels of detail provide the rationales for change propagation and allow a more proactive management of changes. First, we develop the ontology of this method based on a comprehensive comparison of three seminal functional reasoning schemes. Then, we demonstrate the FBS Linkage technique by applying it to a diesel engine. Finally, we evaluate the method.

Managing change can be challenging due to the high levels of interdependency in concurrent engineering processes. A key activity in engineering change management is propagation analysis, which can be supported using the change prediction method. In common with most other change prediction approaches, the change prediction method has three important limitations: L1: it depends on highly subjective input data; L2: it is capable of modelling 'generalised cases' only and cannot be; customised to assess specific changes; and L3: the input data are static, and thus, guidance does not reflect changes in the design. This article contributes to resolving these limitations by incorporating interface information into the change prediction method. The enhanced method is illustrated using an example based on a flight simulator.

Engineering changes (ECs) are essential in complex product development, and their management is a crucial discipline for engineering industries. Numerous methods have been developed to support EC management (ECM), of which the change prediction method (CPM) is one of the most established. This article contributes a requirements-based benchmarking approach to assess and improve existing methods. The CPM is selected to be improved. First, based on a comprehensive literature survey and insights from industrial case studies, a set of 25 requirements for change management methods are developed. Second, these requirements are used as benchmarking criteria to assess the CPM in comparison to seven other promising methods. Third, the best-in-class solutions for each requirement are investigated to draw improvement suggestions for the CPM. Finally, an enhanced ECM method which implements these improvements is presented.

Engineering changes (ECs) are raised throughout the lifecycle of engineering products. A single change to one component produces knock-on effects on others necessitating additional changes. This change propagation significantly affects the development time and cost and determines the product's success. Predicting and managing such ECs is, thus, essential to companies. Some prediction tools model change propagation by algorithms, whereof a subgroup is numerical. Current numerical change propagation algorithms either do not account for the exclusion of cyclic propagation paths or are based on exhaustive searching methods. This paper presents a new matrix-calculation-based algorithm which can be applied directly to a numerical product model to analyze change propagation and support change prediction. The algorithm applies matrix multiplications on mutations of a given design structure matrix accounting for the exclusion of self-dependences and cyclic propagation paths and delivers the same results as the exhaustive search-based Trail Counting algorithm. Despite its factorial time complexity, the algorithm proves advantageous because of its straightforward matrix-based calculations which avoid exhaustive searching. Thereby, the algorithm can be implemented in established numerical programs such as Microsoft Excel which promise a wider application of the tools within and across companies along with better familiarity, usability, practicality, security, and robustness.

Engineering change (EC) is a source of uncertainty. While the number of changes to a design can be optimized, their existence cannot be eliminated. Each change is accompanied by intended and unintended impacts both of which might propagate and cause further knock-on changes. Such change propagation causes uncertainty in design time, cost, and quality and thus needs to be predicted and controlled. Current engineering change propagation models map the product connectivity into a single-domain network and model change propagation as spread within this network. Those models miss out most dependencies from other domains and suffer from hidden dependencies. This paper proposes the function-behavior-structure (FBS) linkage model, a multidomain model which combines concepts of both the function-behavior-structure model from Gero and colleagues with the change prediction method (CPM) from Clarkson and colleagues. The FBS linkage model is represented in a network and a corresponding multidomain matrix of structural, behavioral, and functional elements and their links. Change propagation is described as spread in that network using principles of graph theory. The model is applied to a diesel engine. The results show that the FBS linkage model is promising and improves current methods in several ways: The model (1) accounts explicitly for all possible dependencies between product elements, (2) allows capturing and modeling of all relevant change requests, (3) improves the understanding of why and how changes propagate, (4) is scalable to different levels of decomposition, and (5) is flexible to present the results on different levels of abstraction. All these features of the FBS linkage model can help control and counteract change propagation and reduce uncertainty and risk in design.