Forecasting is a critical component of project management. Project managers must be
able to make reliable predictions about the final duration and cost of projects starting
from project inception. Such predictions need to be revised and compared with the
project's objectives to obtain early warnings against potential problems. Therefore, the
effectiveness of project controls relies on the capability of project managers to make
reliable forecasts in a timely manner.
This dissertation focuses on forecasting project schedule progress with
probabilistic methods. Currently available methods, for example, the critical path
method (CPM) and earned value management (EVM) are deterministic and fail to
account for the inherent uncertainty in forecasting and project performance.
The objective of this dissertation is to improve the predictive capabilities of
project managers by developing probabilistic forecasting methods that integrate all
relevant information and uncertainties into consistent forecasts in a mathematically
sound procedure usable in practice. In this dissertation, two probabilistic methods, the Kalman filter forecasting method (KFFM) and the Bayesian adaptive forecasting method
(BAFM), were developed. The KFFM and the BAFM have the following advantages
over the conventional methods: (1) They are probabilistic methods that provide
prediction bounds on predictions; (2) They are integrative methods that make better use
of the prior performance information available from standard construction management
practices and theories; and (3) They provide a systematic way of incorporating
measurement errors into forecasting.
The accuracy and early warning capacity of the KFFM and the BAFM were also
evaluated and compared against the CPM and a state-of-the-art EVM schedule
forecasting method. Major conclusions from this research are: (1) The state-of-the-art
EVM schedule forecasting method can be used to obtain reliable warnings only after the
project performance has stabilized; (2) The CPM is not capable of providing early
warnings due to its retrospective nature; (3) The KFFM and the BAFM can and should
be used to forecast progress and to obtain reliable early warnings of all projects; and (4)
The early warning capacity of forecasting methods should be evaluated and compared in
terms of the timeliness and reliability of warning in the context of formal early warning
systems.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/85811 |
| Date | 10 October 2008 |
| Creators | Kim, Byung Cheol |
| Contributors | Reinschmidt, Kenneth F. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, born digital |
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