Search

For decades the project management community has been using forecasting models based on the schedule performance index (SPI) and recently SPI(t), duration based schedule performance index, to predict the duration and completion of an in-progress project. In this paper, the authors have used and recommend the earned duration index (EDI), developed as part of earned duration management (EDM) in conjunction with the well-established exponential smoothing forecasting technique to predict the completion of a project. The study compares several models and provides a comparative analysis of performance of these models using variety of projects at multiple stages of completion. The research findings suggest that using EDI of earned duration management is a preferred option compared to SPI(t). In addition, when EDI is used together with exponential smoothing techniques, the results become even better. [ABSTRACT FROM AUTHOR]

A model for project budgeting and scheduling with uncertainty is developed. The traditional critical-path method (CPM) misleads because there are few, if any, real-life deterministic situations for which CPM is a great match; program evaluation and review technique (PERT) has been seen to have its problems, too (e.g., merge bias, unavailability of data, difficulty of implementation by practitioners). A dual focus on the distributions of the possible errors in the time and cost estimates and on the reliability of the estimates used as planned values suggests an approach for developing reliable schedules and budgets with buffers for time and cost. This method for budgeting and scheduling is executed through either simulation or a simple analytical approximation. The dynamic buffers provide much-needed flexibility, accounting for the errors in cost and duration estimates associated with planning any real project, thus providing a realistic, practical, and dynamic approach to planning and scheduling. [ABSTRACT FROM AUTHOR]

A pragmatic procedure is suggested to determine the size of a project's, a program's, or a system's risk contingency budget at any specified level of certainty, e.g., 99% confidence. The size of the budget is a function of the number of risks expected at the specified confidence level. The number of risks used for developing the contingency budget depends on the total number of risks to be considered and the estimated probabilities of occurrence of the risks. The exact number, derived from the binomial distribution, is given in a table or, when the number of risks to be considered is greater than about 20, by a simple formula. The findings suggest that a specific contingency budget amount can be set aside for the small fraction of risks that typically materialize. The specific numbers would depend on the risk tolerance of the organization. [ABSTRACT FROM AUTHOR]