Extending the portfolio and strategic planning horizon by the stochastic forecasting of unknown future projects: A FDOT case study

Construction companies typically work on many projects simultaneously, each with its own specific objectives and resource demands. Consequently, a key managerial function is to allocate equipment, employees, and financial resources across concurrent projects in a way that satisfies individual project constraints while optimizing the company's overall objectives.Project portfolio management (PPM) is concerned with managing multiple projects to accomplish strategic goals. To date, the main research streams in this area have emphasized project selection, project prioritization, and the alignment of a portfolio with strategic goals among a pool of awarded projects. The literature contains a gap regarding the effects of uncertainties associated with future projects, including both known (but yet to be awarded to a contractor) and unknown (although statistically quantifiable) ones. Such a capability, looking into the future, is critical for effective medium- and long-term strategic planning for a company.It is evident that companies should focus not only on current and known projects but also on uncertain and unknown future projects. This research develops and validates a stochastic model for predicting streams of uncertain and unknown future projects. It also seeks to demonstrate the significance and implications of such uncertainties on project portfolios and strategic planning. In terms of scope, this research project considered the Florida Department of Transportation's (FDOT) design-bid-build projects as a case study. Records containing letting information from the past 14 years, along with a pool of candidate variables, were analyzed to capture the characteristics of the time-series data and to identify any correlations between those variables and macroeconomic factors. The objective was to develop a model capable of generating representative future project streams to assist in strategic planning and portfolio management. The findings demonstrate how various univariate and multivariate models can be used to forecast the number of future projects for individual months. Furthermore, a sampling method was developed and verified to assign a cost and duration to each forecasted project. Contractors could, for example, use these stochastic data streams to test different bidding strategies and assess the sensitivity of a portfolio's performance to changes in market factors.