To accurately predict propagation dynamics for single or multiple change propagations across different product development stages in a sequential or concurrent way is critical for decision-making of implementing change requests. In this paper, a change propagation dynamic model is built based on the compartmentalization of engineering entities into susceptible engineering entities and affected engineering entities (SA), the ordinary differential equations for describing the rate of affected entities with respect to the total ones and the duration for resolving all the changes for every moment are presented by combining the calculations of change impacts with different split and joint junctions. Considering the difficulty of finding analytical solutions to the differential equations, algorithms for sequential and concurrent simulations of change propagations across different development stages, and random and GA (Genetic Algorithm)-based optimal selections of feasible propagation paths are developed to obtain numerical solutions for single and multiple change requests. Simulation results show that change ripples and blossoms can be observed in both sequential and concurrent change propagations, and these propagation patterns are not sensitive to the initial change effect and the threshold value for propagations, while critical change propagation paths and the number of initiated changes have important effects on both concurrent and sequential change propagation process. It is also demonstrated that concurrent propagation strategy is advantageous for processing single or few of initiated changes since it can shorten product redevelopment time, sequential propagation strategy has an advantage of robustness for handling multiple initiated change requests.

In complex products the values of parameters are rarely exactly the required values, rather they often have a margin that might be designed in deliberately or be the incidental results of other design decisions. These margins play a critical role in design processes in managing engineering change and iteration. While engineers often talk about margins informally, designers and researchers also use other terms for specific margin concepts. This paper reviews the existing literature on related concepts and defines margins formally. It discusses the role margins play in handling uncertainty by distinguishing between buffer and excess. Buffer deals with uncertainty and excess with the remaining overcapacity of the design. Buffer can transition into excess of the design solution if the uncertainty can be reduced. The concepts are applied to the temperature margins of several candidate materials for a non-rotary jet engine component. This shows that a clear understanding of margins can help a company to select design alternatives.

Engineering design changes constantly occur in complex engineering design processes. Designers need appropriate measures to handle the numerous design changes in order to realize consistent and completely validated product models so that successful product development is assured. In this paper, a time-based mathematic model is presented to characterize the sequential change propagation process, and then the shortest path algorithm is given to find the most timesaving routes for changes to propagate to other dependent design tasks. An analysis method is introduced to compute the sensitivities of change impacts on the affected design tasks, which indicates that the more time consumed by a change to take its effect, the more sensitive the change impacts on those downstream dependent tasks. A case study of change propagations in motorcycle engine design process was presented to demonstrate the proposed method.