Measuring project resilience – Learning from the past to enhance decision making in the face of disruption

Although projects are regularly exposed to disruptive events, the literature lacks an effective measurement system for project resilience. This gap presents challenges for decision makers because of the consequent lack of quantitative information about the level of resilience and its impact on project performance throughout a project's life. We argue that managers can be supported by a priori information about past similar projects as well as new data that evolve during disruption and recovery stages to enhance decision making by key project leaders, such as funders when approving new projects, project managers when developing the detailed plan, and project owners when approving corrective actions following a major disruption. Therefore, this paper develops a mathematical model to measure the level of project resilience by predicting disruption and recovery profiles based on past similar completed projects, as well as actual events unique to the project at hand. We illustrate and validate the model based on a portfolio of 43 major projects that faced disruptions from various sources. Our results provide the first empirical evidence to measure the impact of project resilience on the disruption and recovery behavior of real-life projects. The outputs of this research can be used as a decision support system that enables managers to make informed decisions throughout a project's life. • This paper takes an empirical approach to measure project resilience. • A novel quantitative measure of project resilience is developed. • The proposed model is validated in a portfolio of 43 major projects. • The model supports decision making in projects by creating a learning system.