Forecasting project progress and early warning of project overruns with probabilistic methods

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Title: Forecasting project progress and early warning of project overruns with probabilistic methods
Author: Kim, Byung Cheol
Abstract: 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 .
URI: http : / /hdl .handle .net /1969 .1 /85811
Date: 2008-10-10


Forecasting project progress and early warning of project overruns with probabilistic methods. Available electronically from http : / /hdl .handle .net /1969 .1 /85811 .

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