Publications

Final report: mathematical method for quantifying the effectiveness of management strategies

Robinett, R.D.; Brewer, Jeffrey D.

Large complex teams (e.g., DOE labs) must achieve sustained productivity in critical operations (e.g., weapons and reactor development) while maintaining safety for involved personnel, the public, and physical assets, as well as security for property and information. This requires informed management decisions that depend on tradeoffs of factors such as the mode and extent of personnel protection, potential accident consequences, the extent of information and physical asset protection, and communication with and motivation of involved personnel. All of these interact (and potentially interfere) with each other and must be weighed against financial resources and implementation time. Existing risk analysis tools can successfully treat physical response, component failure, and routine human actions. However, many ''soft'' factors involving human motivation and interaction among weakly related factors have proved analytically problematic. There has been a need for an effective software tool capable of quantifying these tradeoffs and helping make rational choices. This type of tool, developed during this project, facilitates improvements in safety, security, and productivity, and enables measurement of improvements as a function of resources expended. Operational safety, security, and motivation are significantly influenced by ''latent effects'', which are pre-occurring influences. One example of these is that an atmosphere of excessive fear can suppress open and frank disclosures, which can in turn hide problems, impede correction, and prevent lessons learned. Another is that a cultural mind-set of commitment, self-responsibility, and passion for an activity is a significant contributor to the activity's success. This project pursued an innovative approach for quantitatively analyzing latent effects in order to link the above types of factors, aggregating available information into quantitative metrics that can contribute to strategic management decisions, and measuring the results. The approach also evaluates the inherent uncertainties, and allows for tracking dynamics for early response and assessing developing trends. The model development is based on how factors combine and influence other factors in real time and over extended time periods. Potential strategies for improvement can be simulated and measured. Input information can be determined by quantification of qualitative information in a structured derivation process. This has proved to be a promising new approach for research and development applied to personnel performance and risk management.