I’ve spent the last two years leading an INCOSE Fellows’ project to review and update the definitions INCOSE uses for “System” and “Systems Engineering”.
Key results of this were presented during the International Symposium in Washington DC in July 2018, and a couple of papers were also published last year, one at the International Symposium in Adelaide, and one in Systems Engineering Journal.
Copies of all of these papers can be viewed using the links below.
Our final recommendations will be out for review to INCOSE members shortly, and after any useful improvements from this review are incorporated, will be offered for formal adoption by INCOSE over the winter of 2018-19.
New paper on Tom Walworth’s research on modelling the effect of hidden rework on project performance and systems engineering metrics:
Walworth, T., Yearworth, M., Shrieves, L. and Sillitto, H. (2016), Estimating Project Performance through a System Dynamics Learning Model. Syst Eng, 19: 334–350. doi:10.1002/sys.21349
Monitoring of the technical progression of projects is highly difficult, especially for complex projects where the current state may be obscured by the use of traditional project metrics. Late detection of technical problems leads to high resolution costs and delayed delivery of projects. To counter this, we report on the development of a updated technical metrics process designed to help ensure the on-time delivery, to both cost and schedule, of high quality products by a U.K. Systems Engineering Company. Published best practice suggests the necessity of using planned parameter profiles crafted to support technical metrics; but these have proven difficult to create due to the variance in project types and noise within individual project systems. This paper presents research findings relevant to the creation of a model to help set valid planned parameter profiles for a diverse range of system engineering products; and in establishing how to help project users get meaningful use out of these planned parameter profiles. We present a solution using a System Dynamics (SD) model capable of generating suitable planned parameter profiles. The final validated and verified model overlays the idea of a learning “S-curve” abstraction onto a rework cycle system archetype. Once applied in SD this matched the mental models of experienced engineering managers within the company, and triangulates with validated empirical data from within the literature. This has delivered three key benefits in practice: the development of a heuristic for understanding the work flow within projects, as a result of the interaction between a project learning system and defect discovery; the ability to produce morphologically accurate performance baselines for metrics; and an approach for enabling teams to generate benefit from the model via the use of problem structuring methodology.