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Originally published May 21, 2012
Whatís the best way to drive down unscheduled maintenance expenses? The most common answer from industry professionals is to tune up your preventive maintenance program. Okay, that makes sense. But how do you accomplish that large and ambiguous objective? Just as importantly, how do you control the costs of the events when they do occur?
PreventionFirst is prevention. This is the ultimate target of those pursuing predictive analytics related to the subject area of life cycle costs (LCC). Past behavior is often the best predictor of future behavior, right?† Having a rich, reliable history of fleet maintenance data in your data warehouse is a must. You not only gain perspective on LCC, but you also facilitate opportunities for low-effort, high-return analytics.
ManagementNo one is perfect. Things will happen. Are you prepared? Do you have the processes in place to support point-of-impact decisions?† In the world of fleet operations, every unscheduled maintenance event is more costly than the repairs themselves. It interrupts normal operations, keeps your drivers on the sidelines and ultimately jeopardizes your service commitments to your customers. Having the right tools to help you expedite diagnosis and dynamically select the right vendor for the repair Ė no matter where your equipment may be Ė is critical. You cannot make the best decision without knowing the history of your equipment, the qualities and capabilities of your vendors or possibly even the historical behavior of your drivers.
MitigationIt happened. You made the best decision you could. How do you minimize the financial impact? When your personal vehicle breaks down, you get an estimate, right? The same is true here, and most organizations are good about doing this. However, do you capture the estimate and hold your vendors and management team accountable? Thereís going to be some variance between the estimate and the final payment, but donít make any assumptions.
ChallengesThe ROI generated from these processes is huge, so why arenít more organizations implementing these actionable forms of business intelligence? The number one challenge is the data itself. A solid extract, transform and load (ETL) process that navigates multiple data sources is required. You also need the technical and subject matter expertise to generate efficient, actionable intelligence from the overwhelming amount of information. In the end, itís worth the effort.
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