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By Phil Wales
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For facility managers interested in using facility data to drive down costs, it is essential to standardize and optimize processes for how work gets done, including clearly defining how service partners will be engaged, a clear definition of how work will be done, and which metrics need to be measured. Then it is realistically possible to determine the data set to support those activities and processes, aligning the data that will be collected and managed.
The catch is that organizations, after purchasing new technology, sometimes say, “Look at all the stuff that I can track now.” Yet it’s typically no more than an illusion, since much of that data will add no value to those who will have to collect or maintain it, thus guaranteeing it will not be kept current.
Even when the data is well-maintained, facility managers still often find that a significant portion just doesn’t add value to the organization’s mission.
The facility manager’s challenge is to get the appropriate procedures — the business processes — in place so that a solution can properly illuminate the condition of assets being monitored. When a clear definition of how assets should be managed is achieved, the data set needed to support those processes becomes clear and procedures can be put into place that will allow, even encourage, users to continually keep that data accurate.
That leads directly into the second part of the issue: driving costs down with this new, improved attention to data. In preventive maintenance, service is performed on a set schedule, usually based on manufacturer recommendations in turn based on average or “standard” conditions. The problem is that standard conditions rarely occur in the field. Thus, assets are typically serviced at periods when the “optimum” time is unknown. If maintenance were predicted based on real data points, preventive maintenance could be timed to be at its optimum point. This would potentially save significant dollars and possibly prolong the life of the assets.
Better decision-making is the name of the game and is the key to driving down costs with appropriate data. An actual example involves a boiler, which failed several years before the end of its life expectancy. Several weeks were required to find and purchase a replacement and get it installed, all while the company was forced to shut down a significant portion of data operations at one of its call centers. According to the CFO, the failure’s cost was immeasurable, compared to the relatively “acceptable” cost of repairing or replacing the boiler on time by using appropriate data in a predictive way.
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