Project Evaluation and Selection with Task Failures

Abstract

We consider a company that schedules the tasks of its projects to maximize their expected net present value (ENPV) when tasks may fail. The failure of any task terminates the project immediately. We show that for projects with certain decreasing failure rates, the ENPV optimization problem can be solved using a linear program. The main focus of our work is on how constant task failure rates contribute to decreasing project risk as tasks are completed. Under constant task failure rate, earlier completion of a task improves its probability of success and the risk profile of the project. However, it may also accelerate costs, which worsen discounted cash flow. We show the equivalence of cash flow discount rate and failure rate. Further, if task failures are independent, then the failure rates are additive. We develop a model that (a) recognizes the reduction in project risk when a task is completed, (b) implements this risk reduction into the ENPV calculation, and (c) permits optimization of the ENPV through sequencing and timing decisions for the tasks. We design an algorithm to solve the problem optimally. This enables us to validate the contributions of our work using two computational studies. The first study demonstrates a significant increase in maximum project ENPV from improved project scheduling. The second study demonstrates a significant increase in total project portfolio value as a result of better informed project selection. Our work motivates companies to develop more precise information about the failure risks of their project tasks.

Project managers' reactions to project disruption: sponsor actions versus environmental uncertainty

“…, 2020) and provide a behavioral perspective on disruptions that complements recent analytical works (Narayanan et al. , 2020; Zhao et al. , 2020).…”

“…We contribute to the discussion of project termination in the recent OM literature (Subramanian et al, 2020) and provide a behavioral perspective on disruptions that complements recent analytical works (Narayanan et al, 2020;Zhao et al, 2020). We focus on situations when top management does not "predict and provide" but fails to offer adequate support to the project manager (Holweg and Maylor, 2018).…”

Capacity Investment under Bayesian Information Updates at Reporting Periods: Model and Application

“…Recent work aimed at the scheduling and selection decisions of projects has highlighted that it is essential for managers to quantify the risk associated with individual project tasks and use that information to schedule projects to maximize project value (Zhao et al. 2020). We contribute to this stream of literature (Creemers 2019, Creemers et al.…”

“…We show that the introduction of downstream benefit as a factor, along with the ability to estimate the impact of alternate project choices using our models, can increase the manufacturer's expected profit. Recent work aimed at the scheduling and selection decisions of projects has highlighted that it is essential for managers to quantify the risk associated with individual project tasks and use that information to schedule projects to maximize project value (Zhao et al 2020). We contribute to this stream of literature (Creemers 2019, Creemers et al 2015, Lorko et al 2020, Zhao et al 2020, which mainly focus on technical reasons for project timing by highlighting the impact of the project schedule and progress reporting on a manufacturer's capacity addition, and therefore expand the scope of factors that project managers should consider while scheduling projects.…”

“…2020, Zhao et al. 2020), which mainly focus on technical reasons for project timing by highlighting the impact of the project schedule and progress reporting on a manufacturer's capacity addition, and therefore expand the scope of factors that project managers should consider while scheduling projects. Careful choice of “safe bets,” that is, technologies with low profitability but a high probability of success, can increase expected manufacturer profit . In the past, scientists chose technology projects based on science's contribution, with little interest shown in their technological exploration's ancillary benefits.…”