Project Team Risk/Reward Allocation (Archived)

RT-021 Topic Summary
RT 021

Overview

The Construction Industry Institute’s Project Team Risk/Reward Task Force worked with the University of California Berkeley research team to develop a General Performance Model (GPM) that would improve project cost-effectiveness. The GPM allows management to test different combinations of project execution options and predict expected cost, schedule, and other performance impacts.

The research team developed a new methodology for capturing and formalizing construction experts’ knowledge in a model that can predict project performance. The methodology developed evaluates project performance by utilizing multiple performance criteria, with each having a flexible weighting procedure for evaluation. It also evaluates different execution strategies, individually and combined, to reveal the relative effectiveness. The modeling approach includes procedures to disaggregate the problem and capture expertise from construction industry experts at different levels. The model also uses uncertainties and cause-effect relationships among variables that are an explicit part of the structure.

Key Findings and Implementation Tools

1 : Performance Modeling Methodology Developed

The CII Project Team Risk/ Reward (PTRR) Task Force developed a performance modeling methodology that can evaluate the impact of project execution strategies on project performance to support project planning decisions. The project combines experience from experts with assessment of the project team and organizes it in a causal model where the team specifies the performance elements to be evaluated. Some features of the methodology:

  1. Predictive capabilities to help establish performance targets
  2. Incorporates risks and uncertainties, usually present in construction projects, in the analysis
  3. Allows a more comprehensive evaluation than currently available methods which are limited to one or two performance measures
  4. Allows the evaluation of the effects on project performance of two or more management options acting simultaneously
  5. Provides sensitivity analysis capabilities to identify the most important variables for project performance
  6. Provides not only results but exploratory capabilities through the model causal structure. It can help management obtain a better understanding of factors that affect performance, to identify opportunities for improvement, and then take effective actions.
(SD-80, p. 205) 
 
Reference: (SD-80)

2 : Integration of Previous Research in Dissimilar Field

The mathematical model developed in this research is characterized by an innovative use and integration of previous research from dissimilar fields such as Futures Research and Probabilistic Algorithms. A number of adaptations and extensions to previous research were carried out in this report. Some of the tasks accomplished in this area are:

  1. Adaptation of the Cross – Impact analysis events’ structure to represent the performance level of the GPM variables.
  2. Modifications of the original Cross – Impact algorithm to incorporate characteristics of the GPM events and improve computer processing performance.
  3. Design of the analysis approach to predict performance from the Cross – Impact Analysis results.
  4. Modification and adaptation of an algorithm to perform probabilistic inference to combine probabilistic information obtained from model results. The proposed algorithm allows the evaluation of the simultaneous effect of multiple options on project performance.
  5. Simplification in the knowledge acquisition demands of the traditional Cross – Impact methodology through the introduction of the concept of patterns.
(SD-80, p. 206)
 
Reference: (SD-80)

3 : Prototype Computer Implementation

The planned computer implementation of this methodology can have substantial benefits. The prototype version developed for this report has allowed the testing of the algorithms and has provided recommendations for final implementation. Benefits are:

  1. Reduction in analysis effort
  2. Inexpensive exploration of execution strategies
  3. A new door to innovation by providing a tool to systematically evaluate new and untried construction strategies
(SD-80, p. 207) 
 
Reference: (SD-80)

4 : Capture and Integrate Subject Matter Expertise

The methodology provides a framework for capturing and formalizing construction experts’ knowledge and integrating research information. A systematic methodology for knowledge acquisition in the modeling process supports one of the major difficulties found in practice for the development of this type of model. The knowledge is stored in modules that are self-contained and independent. Some of the features resulting from the above characteristics are: (SD-80, p. 208)

  1. It facilitates isolation and capture of expertise. Each expert can contribute his/her own expertise in a localized area.
  2. It automatically introduces causal relationships that describe the interactions among project variables.
  3. It can be easily updated without altering the model structure.
Reference: (SD-80)

5 : Prediction and Causal Analysis Combined

As a research tool, this methodology is the first attempt to rigorously analyze the basis of project performance in such a way that prediction and causal analysis can be assessed in the same structure. (SD-80, p. 208)
 
Reference: (SD-80)

6 : Comparison of Possible Execution Strategies

The computational scheme utilized within the model allows all possible execution strategies to be compared on a relative basis. Preferred strategies are ranked either on the basis of combined performance on any single chosen criterion. (SD-80, p. vii) 
 
Reference: (SD-80)

Key Performance Indicators

Improved cost, Improved performance/achieved success

Research Publications

Project Performance Modeling: A Methodology for Evaluating Project Execution Strategies - SD-80

Publication Date: 10/1992 Type: Source Document Pages: 289 Status: Archived Reference


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