By Daryl Mather
Data managementyou either love it or hate itis one of the vital areas that support modern facility asset management. Its an area that we often neglect, creating opportunities for people with less than adequate levels of asset management knowledge to make large-scale mistakes.
Technological advances in how asset managers capture, organize, and use asset-related data is the most important advance that weve made in the 20 years that Ive been consulting in this area. The implications of this are immense. It can permanently change the way a company manages its physical asset base.
New technologies also can bring dangersnot the least of which is the potential for allocating a lot of time and money on tools, services and activities that dont support the central goals of asset management.
The humble computerized maintenance management system (CMMS) is the center of most asset maintenance efforts to capture, store and analyze asset data. In larger operations, this has been superseded by enterprise asset management (EAM) and enterprise resource planning (ERP) systems, but the goal remains the same. These also are supplemented by mobile working and bar-coding solutions, GIS integration, RFID tagging systems, online condition monitoring, plant management systems, and a range of detailed analytical software tools.
A common issue occurs when companies implement a system, starting with asset register information, but never progress to effective and dynamic workflow management. They remain stagnant, going in ever decreasing analytical circles focusing on collecting reams of static data, often without a real analysis of how theyre going to use it. This important step can highlight inefficiency, aid effective reliability management when applied through a framework of RCM logic, reduce inventory, and provide defensible asset replacement plans. These benefits, however, turn out just to be ancillary to the main benefit.
Today, many maintenance groups often make large decisions based on expert judgment, opinion, and anecdotal information.
How does a company get to the point where it can make asset maintenance and asset management decisions based principally on data? Here are some tips Ive used over the years to help companies advance their decision-support frameworks.
- Dont focus on the quality, volume, and integrity of the data until you know what data should be collected. A great deal of money is spent, often unwisely, on programs aimed at perfecting data quality, integrity, and monitoring volumes of data. If you want to define your data sets, then I strongly recommend that you look at what you need it for first. This is a top-down approach that has helped me immensely in the past.
- Being flexible in work methods, combined with process controls and exception reporting, always will provide better results over the long term than locking an interface. If faced with a screen that requires certain information before proceeding, users often get frustrated and annoyed, and instead of entering whats required, they enter garbage or nothing at all. A better approach is to train people what to do and why, ensure that exception reporting is part of somebodys role so errors in data entry can be picked up in the short term, and put feedback loops in place to tell people where mistakes were made.
- Integrate data-quality activities into the day-to-day work. Dont put it off to one side and assign it to an asset data department or, worse, to the IT department. Asset maintainers and managers need to be the ones reviewing asset data exception reports to ensure they meet minimum criteria. Some tools that might be of use:
- 24-hour reportsa report of all of the corrective and reactive works that have arisen in the past 24 hours to management for their discussion at morning meetings.
- Backlog reportsregular reviews of the work order backlog to ensure that garbage work orders are identified and either corrected or eliminated.
- Work order closure reportsrun daily and check to make sure that the information is correct and can be of future use.
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