Stochastic forecasting of project streams for construction project portfolio management

Abstract

Background: Construction companies typically work on many projects simultaneously each with its own objectives and resource demands. Consequently, a key managerial function is to allocate financial, equipment, and human resources between these concurrent projects in a way that satisfies the individual project constraints while optimizing the company's overall objectives. Project portfolio management is concerned with managing multiple projects to accomplish strategic goals. The main concentrations of research so far have been project selection, prioritization and alignment of a portfolio with strategic goals among a pool of awarded projects. The objective of this research is to develop a model capable of generating representative future streams of projects that can be used to assist in strategic planning and portfolio management. Methods: This paper reports on an on-going research project aimed at developing and validating a stochastic model of streams of uncertain and unknown future projects and to demonstrate the significance and implications of such uncertainties on project portfolio and strategic planning. The scope of the research is Florida Department of Transportation (FDOT) projects. Records of the past 12 years letting information combined with a pool of candidate variables is analyzed to capture characteristics of the time series data and to determine any project characteristic correlations with macroeconomic factors. Results: The performances of the various model components presented indicate the viability of an integrated project stream forecaster that predicts, within a simulation environment, the frequencies of projects and empirical distributions of project duration and cost. Conclusion: The gap in the current body of knowledge is a lack of consideration of the effects of uncertainties associated with future projects, both known (but yet to be awarded to a contractor) and unknown (although statistically quantifiable). It is evident that companies should not just focus on current known projects but also uncertain and unknown future projects. Such a capability, looking into the future, is key to the effective medium and long-term strategic planning for the company. Contractors can, for example, use these stochastic streams of data to test different bidding strategies and to see how sensitive the performance of their portfolio is to changes in different market factors.

Hyper-project portfolio management: post-merger project portfolios

“…However, their specific states, space, or quantities are uncertain and cannot be accurately quantified. (Ramasesh and Browning, 2014;Shojaei and Flood, 2017) Unknownunknown 3rd condition (Imprudent ignorance)…”