Mapping Uncertainty for Risk and Opportunity Assessment in Projects

Qazi A., Dikmen İ., BİRGÖNÜL M. T.

EMJ - Engineering Management Journal, vol.32, no.2, pp.86-97, 2020 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.1080/10429247.2019.1664249
  • Journal Name: EMJ - Engineering Management Journal
  • Journal Indexes: Science Citation Index Expanded, Social Sciences Citation Index, Scopus, ABI/INFORM, Business Source Elite, Business Source Premier, Compendex, INSPEC
  • Page Numbers: pp.86-97


© 2019, © 2019 American Society for Engineering Management.Risk management is deemed as a critical process toward achieving the objectives of any project. A number of tools and techniques have been developed to help project managers reduce the risk involved in complex projects as risk is generally perceived as a negative event. However, uncertainty, which may lead to risk events may well result in opportunities. With the main aim of managing both risk and opportunity within an interdependent setting of interacting project uncertainties, we present a new thinking, called ‘uncertainty thinking’, a process model, a Bayesian Belief Network and Influence Diagram based modeling approach to implement uncertainty thinking in practice. We demonstrate the implementation of the developed uncertainty modeling approach through an illustrative case. We introduce new loss and opportunity metrics to help project managers prioritize uncertain variables within a network setting taking into account interdependencies and develop an optimization scheme for selecting loss reduction and opportunity exploitation strategies in accordance with the decision maker’s loss averse/gain seeking preferences. The results reveal the importance of modeling both risk and opportunity in managing project uncertainties as focusing only on the negative connotation of risk may result in selecting sub-optimal decisions. Furthermore, we also establish that minimization of the expected loss across a risk network does not necessarily maximize the expected gain.