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Design tasks need to be rescheduled and reprioritised frequently during product development. Inappropriate priority decisions generate rework; thus, the policy used to guide such decisions may have a significant effect on design cost and lead time. Generic priority rules provide easily implementable guidelines for task prioritisation and are theoretically effective for many planning problems. But can they be used in design processes, which include iteration, rework and changes? In this article, a discrete-event simulation model is developed to investigate priority policies in design. The model explores the combined effects of progressive iteration, rework and change propagation during design of interconnected parts in a product architecture. Design progression is modelled as an increase in the maturity of parts; rework and change propagation cause maturity levels in certain parts to reduce. Twelve product architecture models ranging in size from 7 to 32 elements are simulated to draw qualitative and general insights. Sensitivity of the findings to assumptions and model inputs is tested. Generally effective priority policies are identified, and their impact is shown to depend on the interconnectedness and organisation of product architecture, as well as the degree of concurrency in the design process.

The Dependency Structure Matrix (DSM) has proved to be a useful tool for system structure elicitation and analysis. However, as with any modelling approach, the insights gained from analysis are limited by the quality and correctness of input information. This paper explores how the quality of data in a DSM can be enhanced by elicitation methods which include comparison of information acquired from different perspectives and levels of abstraction. The approach is based on comparison of dependencies according to their structural importance. It is illustrated through two case studies: creation of a DSM showing the spatial connections between elements in a product, and a DSM capturing information flows in an organisation. We conclude that considering structural criteria can lead to improved data quality in DSM models, although further research is required to fully explore the benefits and limitations of our proposed approach.