Stokke, Per R. and Boyce, Thomas A. and Lowrance, John D. and William K. Ralston, Jr. Industrial Project Monitoring with Evidential Reasoning. Nordic Advanced Information Technology Magazine, vol. 8, no. 1, pp. 18-27, Jul 1994.
Abstract
Current project management systems share a common weakness. Although all provide ample information about the current status of a project, none provide meaningful information about the likely outcome of that project. Integrating information about the wide range of factors that affect project success and using that information to monitor and take early corrective action form the bases of the Project Early Warning System (PEWS). The system identifies problems and developments that might lead to deviations from planned project outcomes and does so at such an early stage that effective corrective action can still be taken. PEWS combines a proven project reporting methodology with the latest artificial intelligence techniques such as evidential reasoning. Together, they ensure the successful outcome of large projects. The system encourages objective assessment and reporting by project leaders, while providing upper management with a clear and concise report that pinpoints aspects of projects in the company’s portfolio. Keywords: Artificial Intelligence, Artificial Intelligence Center, AIC