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Auchey, F L and Auchey, G J (2004) Picking successful projects by using the prism LLTM model. In: Khosrowshahi, F (Ed.), Proceedings 20th Annual ARCOM Conference, 1-3 September 2004, Edinburgh, UK. Association of Researchers in Construction Management, Vol. 2, 1135–44.
- Type: Conference Proceedings
- Keywords: knowledge-based systems; modeling; procurement; profitability; risk mitigation
- ISBN/ISSN: 0 9534161 9 4
- URL: http://www.arcom.ac.uk/-docs/proceedings/ar2004-1135-1144_Auchey_and_Auchey.pdf
The Project Risk Identification, Selection, and Management model (PRISMTM) is a process for improving the success of project procurement and knowledge management. It consists of three component parts (PRISM l, PRISM ll and PRISM lll), which have been developed over the last five years. PRISM l has been refined over the past four years by working with several companies who have developed their own customized versions of the model. PRISM 11 has been in the development phase over the past two years. The PRISM 1 Model was designed to be used with projects where there was high quality historical data available for analysis; therefore, it was able to use the traditional tools of risk management, such as Expected Monetary Value, Utility Theory, Pair-Wise Comparisons and Critical Risk Ranking Matrix. This helped companies develop an effective bid-no-bid decision prior to committing significant resources to create a full-scale estimate and bid on the project with the highest profit potential. Another output of the Model was the Project Profitability Predictor. In so doing, PRISM 1 proved to be an improved method for both qualifying and quantifying project risk and opportunity information, which was used to prioritize, manage and mitigate risks before going to contract and then throughout the project. In this way, the model served as a template to store quantitative and qualitative data about each project, creating an historical database and effective knowledge management system designed to capture essential information on past projects and, thereby, reduce the number of known-unknowns on future projects. PRISM ll, building on the strengths of PRISM 1, incorporates more sophisticated risk management tools, including Monte Carlo simulation. PRISM lll, scheduled for release over the next two years, will use neural networking, genetic algorithms, and hybrids, such as fuzzy-neural. These enhancements of the PRISM model will be better able to focus on projects with significantly fewer knowns, b) projects with more known-unknowns, or c) unique projects with more unknown-unknown risks.