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Asset Management
Asset Management seems, at times, to be all things to all people. The term is commonly used in the financial arena to address efforts to maximize profits realized through buying, holding and selling of equities, bonds and real properties. The definition blends financial and operational aspects when decision makers are responsible for maintaining major physical capital assets. The degree to which the proper functioning of the physical assets impacts the profitability of the business is a key factor in distinguishing between the financial and real-estate asset manager and the "equipment asset manager". Equipment Asset Management is the focus of this paper.
Although finances are always part of the decision process in Equipment Asset Management, the main goal is to optimize profitability over the life cycle of critical plant equipment. Are the equipment and the supporting personnel and programs taking advantage of all possible profit opportunities? Could the current maintenance program be changed to improve the equipment capacity or reliability? Would it be more profitable to continue to maintain the current equipment or replace it with newer or different equipment? How long would it take to realize the profit from such a decision? These are examples of types of questions that equipment asset managers need to answer.
The decision making faced by equipment asset managers can be broken into three categories:
- Strategic decisions focus on meeting overall performance or profit goals.
- Tactical decisions focus on the effectiveness of specific programs in meeting the defined objectives of those programs.
- Operational decisions target the direction of specific resources on a day-to-day basis to comply with the tactical programs in support of strategic "success".
What does this structuring of the decision making landscape have to do with data mining and asset management? It limits the range of decision support that can be reasonably included in the definition of data mining. Although still broad categories these categories provide a focus for the presentation of specific examples of data mining.
Asset Management and RCM/Process Improvement
Many utility companies in the Transmission & Distribution area have undertaken efforts to refine their specific needs for information. These efforts are targeted at key decision makers in the organization. At the strategic level projects are undertaken involving business analyses and assessments that develop key performance indicators. At the tactical level many utilities have conducted Reliability Centered Maintenance projects to improve performance and reduce maintenance costs. In the operational area there is an effort to provide better prioritization support to planners, schedulers and supervisors. People in these roles assign resources to pecific tasks. They need to know how they can best reduce risk of failure which requires knowing which equipment presents the greatest current risk.
In many cases the RCM projects mentioned above have been qualitative in nature because they had to rely on anecdotal histories, failure data gleaned from work order closeout free form text and sketchy maintenance and equipment cost information. While adequate, a better basis for RCM decisions is possible through quantitative data review. In addition to the initial RCM study and implementation some utilities are seeking to continue to improve the performance of their equipment and their staff through a process of ongoing evaluation. This has been referred to by various labels such as Living Program or Performance Focused Maintenance. The goals are to establish an ongoing assessment of the initial decisions that will document whether the objectives of the initial RCM analysis has been met or not and to confirm or refute the "information" upon which the original decisions were based on.
In the last few years there has been a tremendous increase in the capability to capture and store data and in the belief that there is value in doing so. This has lead to the creation of gigabyte and terabyte databases that contain data from all levels of the organization and all aspects of operations. This has often happened in a somewhat ad hoc manner. Many of us have lived through the evolution from paper to mainframe/dumb terminal to stand-alone PC to isolated Local Area Networks (LAN) to Wide Area Network (WAN) to Client Server. This evolution lead to the creation of data sources (not always formal databases) developed to support very specific business needs. The person or group that developed them used them to provide better answers to the "decision makers" they supported. Few saw that in what would be an astoundingly short time their data would be even more valuable if it could be combined with data in other systems. So, we now have many important, but disparate, data sources that we need to extract critical business information from to support ever-increasing performance demands.
Enter the tools for accessing, processing and reducing data to information. Enter also the tools for rendering information into usable formats and delivering it to decision makers.
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