Complex systems and products evolve over years to meet new requirements, while applying tried and tested technology. To maximise the reuse of components through the life span, companies need to plan for changes that they can anticipate and cannot anticipate, and facilitate accommodation of such changes in the original architecture and design of the system. However, the degree to which future product changes can be planned depends on the uncertainties that the system, product or product family is subject to. A deeper understanding of these uncertainties is the focus of this paper. The paper first provides a brief literature survey, and discusses the sources and nature of uncertainty. This is followed by a classification of the types of uncertainties that are often encountered and that should be considered, as well as methods and techniques for modelling these uncertainties for incorporation in system design. The paper also provides examples of uncertainties for a variety of systems and products throughout and concludes with an uncertainty checklist for system architects and product designers.@en

Understanding how and why changes propagate during engineering design is critical because most products and systems emerge from predecessors and not through clean sheet design. This paper examines a large data set from industry including 41,500 change requests that were generated during the design of a complex sensor system spanning a period of 8 years. In particular, the networks of connected parent, child, and sibling changes are resolved over time and mapped to 46 subsystem areas of the sensor system. These change networks are then decomposed into one-, two-, and three-node motifs as the fundamental building blocks of change activity. A statistical analysis suggests that only about half (48.2%) of all proposed changes were actually implemented and that some motifs occur much more frequently than others. Furthermore, a set of indices is developed to help classify areas of the system as acceptors or reflectors of change and a normalized change propagation index shows the relative strength of each area on the absorber-multiplier spectrum between-1 and +1. Multipliers are good candidates for more focused change management. Another interesting finding is the quantitative confirmation of the "ripple" change pattern previously proposed. Unlike the earlier prediction, however, it was found that the peak of cyclical change activity occurred late in the program driven by rework discovered during systems integration and functional testing.@en