Evaluating the Complexity of Engineered Systems: A Framework Informed by a User Case Study

Matthew W Potts*, Angus Johnson, Seth Bullock

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Downloads (Pure)

Abstract

Evaluating the complexity of an engineered system is challenging for any organization, even more so when operating in a System-of-Systems (SoS) context. Here, we analyse one particular decision support tool as an illustratory case study. This tool has been used for several years by Thales Group to evaluate system complexity across a variety of industrial engineering projects. The case study is informed by analysis of semi-structured interviews with systems engineering experts within Thales Group. This analysis reveals a number of positive and negative aspects of (i) the tool itself, and (ii) the way in which the tool is embedded operationally within the wider organization. While the first set of issues may be solved by making improvements to the tool itself, informed by further comparative analysis and growing literature on complexity evaluation, the second ``embedding challenge'' is distinct, seemingly receiving less attention in the literature.

In this paper we focus on addressing this embedding challenge, by introducing a complexity evaluation framework, designed according to a set of principles derived from the case study analysis; namely that any effective complexity evaluation activity should feature collaborative effort towards building an evaluation informed by a shared understanding of contextually relevant complexity factors, iterative (re-)evaluation over the course of a project, and progressive refinement of the complexity evaluation tools and processes themselves through linking project evaluations to project outcomes via a wider organizational learning cycle. The paper concludes by considering next steps including the challenge of assuring that such a framework is being implemented effectively.
Original languageEnglish
Pages (from-to)707-723
Number of pages17
JournalSystems Engineering
Volume23
Issue number6
Early online date7 Sep 2020
DOIs
Publication statusPublished - 6 Nov 2020

Keywords

  • complexity science
  • project planning/assessment/control
  • risk and opportunity management

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