Risk analysis in mangement planning and project control. (Probabilistic techniques are applied to the estimation, planning, forecasting and control of large capital projects to ascertain and reduce the degree of inherent risk and uncertainty)

Abstract

Effective estimation, planning, and control of the functions, operations, and resources of a project are among the most challenging tasks faced by the management of today's engineering and construction organisations. The increase in size and complexity of modern projects demand a sound organisational structure and a rational approach. The main objectives of the present study are two-fold. Firstly to report and critically review theoretical and practical developments of different aspects of the management of engineering and construction projects. Secondly to further develop conceptual, practical techniques and processes; also to provide Guidelines to make more effective use. of resources and systems. To achieve these objectives the present research was carried out in close collaboration with various indurtrial organisations. The current literature on project management is critically examined from the point of View of project cost estimation, planning and control. Various existing and recommended procedures, approaches and techniques are reviewed with particular emphasis on using probabilistic techniques. As the problems of scale are increasing, progressively more industries are adopting systems and project management approaches. Problems, deficiencies and gaps in the existing systems are identified. An analysis of a questionnaire survey on Systems-Caps is carried out and the results of the analysis are reported. . S-curves (or progress curves) are widely used in the plauaing and control of cost, time and resources. A mathematical model for the S-curve is adopted for this purpose. Expenditure data on a number of ii recent projects is analysed and fitted to two S-curve models suggested by Keller-Singh and the Department of Health and Social Security (D. H. S. S. ). A comparative study of the models is carried out. A set of standard parameters for the models is obtained and the predicting accuracy of these models for forecasting expenditure for future similar projects investigated. Quantification aspects of risk involved with the completion time of a project are studied. 'A number of stochastic distributions arc fitted for this purpose to the programed and actual durations for the different activities of a housing project. The maximum likelihood method is used for the estimation of parameters of the fitted distributions. Due to the increasing use of indices in the construction industry, building cost and tender price indices, their application, limitations and methods of formation are discussed. Box-Jenkins models are employed to study past behaviour and to forecast future trends for labour, materials and building cost indices. Finally, general conclusions derived from the present regearch are sunmarised and areas requiring further research are proposed.