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Description:
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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 . |