BACKGROUND
Work order and condition monitoring systems provide data to the maintenance organization. Why collect data? To support making the best decisions regarding what maintenance to perform, where, and when. An “optimal” decision rule or policy will apply the right maintenance at the right time to the right components in a way to achieve highest long term profitability. Maintenance managers and engineers have long believed that within their data lie the secrets to achieving optimal decisions. That realization has driven the growth of high technology services and tools to collect, display, manipulate, store, and analyze limitless quantities of data.
THE PROBLEM
Given that the amount of data available is infinite, the maintenance engineer faces new questions:
- Which data will support optimal decision making?, and
- How should one transform the relevant data into optimal maintenance policies?, and
- How should one verify the performance of those policies in order to improve them continuously?
THE LRCM SOLUTION
The “Living” RCM (LRCM) methodology addresses Three Challenges to Achieving Reliability from Data:
- Management of the relationship between the work order system (CMMS, EAM, ERP) and the RCM knowledge base,
- Sample generation, and
- Reliability analysis.
Of these challenges, the first is the most difficult. It is the one that requires creative collaboration among managers, supervisors, engineers, technicians, and support staff. Challenges 2 and 3, on the other hand, are technical in nature. Nevertheless, they may be accomplished only upon the success of Challenge 1. The innovative LRCM methodology confronts Challenge 1 squarely, by unifying theory as embodied in a RCM knowledge base, with practice as recorded in the work order system. Consequently, LRCM ensures that the correct reliability enabling information is available for sample generation (Challenge 2), followed by reliability analyisis (Challenge 3). Reliability analysis, assisted by software, processes a sample in order to generate optimal decision policies, called “models“. Reliability analysts periodically update those maintenance policies thereby ensuring maximum performance consistent with the latest data and knowledge available.