Integrated Shipboard and Shore-based Maintenance Management Decision Tool Navy SBIR FY2005.1 Sol No.: Navy SBIR FY2005.1 Topic No.: N05-051 Topic Title: Integrated Shipboard and Shore-based Maintenance Management Decision Tool Proposal No.: N051-051-0520 Firm: The DEI Group 1127 Benfield Blvd Suite H Millersville, Maryland 21108-2540 Contact: Charles Floyd Phone: (410) 729-1290 Web Site: http://www.dei-group.com Abstract: The DD(X) ship and life cycle support process design objectives are driven by three key objectives: high availability with reduced annual support costs and a manning compliment lower than the predecessor class of ships. To achieve these objectives requires that the shipboard systems and their relationship to alternate maintenance strategies be analyzed using a structured modeling approach to determine: 1) their failure modes, 2) monitoring methods to automatically recognize the failures, 3) the impact of failures on mission readiness, 4) alternate operational scenarios, and 5) maintenance requirements. The analysis must lead to an optimum strategy within constraints, and be captured in a form that allows implementation within the shipboard and shore-side environment. This will allow the use of the design model within the run-time decision support system, that integrates condition monitoring systems, system predictive model simulator, operator displays for status and recommendations, mission readiness assessment models, and maintenance and logistics systems that integrate with the shore-based components. To support these objectives, The DEI Group proposes to design and develop the prototype for an integrated systems framework for use in designing an optimal life cycle maintenance strategy that will also support shipboard deployment within the planned DD(X) Mission Readiness Support System. Benefits: Shipboard HM&E machinery closely resembles the machinery associated with power generating plants. As power utilities around the world are deregulated, they enter into a far more competitive marketplace, where both short-term and long-term profitability is at risk. In this environment, a host of pressures will require utility executives to focus on a clear, well-articulated, risk-based, long-term strategy to address the uncertainties of managing a diverse portfolio of various generation asset types, in different geographic locations. Since electricity cannot be stored to a reasonable extent, production and consumption have to be balanced in real-time. Buyers and sellers of power are then required to minimize the imbalances, but unforeseen events such as weather changes, politically induced fuel price fluctuations, and unscheduled plant outages may nevertheless cause a significant discrepancy. Many senior managers in asset intensive industries are now turning their attention to strategic management of their production machinery assets. The $300 billion electric power industry spent over $30 billion in 2004 on non-fuel operations and maintenance (O&M) expenses. The annual earnings of the entire power industry are roughly $40 billion. Many utilities executives are looking to implement well-proven information engineering based solutions which have helped corporations to reduce O&M expenditures by 20% or more. But cost reductions, in the face of the risks associated with the commodity nature of the new power industry cannot be a singular strategy. Reducing costs at the expense of plant availability and heat rate will not achieve defined tactical and strategic objectives. The strategy supporting the achievement of the prescribed vision is taking a holistic approach. Specific requirements that have been identified by commercial executives include:  Dynamic maintenance planning and material inventory optimization based on machinery performance and availability risk analysis  Maximum utilization of automated data acquisition, analysis, reasoning, and reporting  Extension of the time between major planned outages without incurring unacceptable risk to generation  Minimization of planned outage durations  Just-in-Time maintenance  Closed loop strategy assessment and continuous improvement through accurate activity related cost and event impact data collection  Real-time decision management, within a predictive simulation framework providing accuracy confidence factors Cost reductions in asset intensive industries of 30%-40%, capacity factor improvements of 5%-8%, and efficiency improvements of 2%-4% are achievable based on average industry metrics. One of the critical components of developing the required asset management strategy is the ability model maintenance strategy options more accurately in order to optimize capital and O&M investments. Return