Whoever said, “What you don’t know can’t hurt you” obviously never had to deal with OSHA, Sarbanes-Oxley, upper management or any of the other necessary reporting functions required in business today. So, of course, you know I’m going to suggest an AIDC solution.
Well, yes and no.
Yes, because AIDC technologies can provide you with hard numbers that show you where you are and forecast where you need—or want—to go. Barcodes, RFID, RTLS, biometrics, voice and mobile computing are all wonderful things. The problem is: They provide you with lots of good data, and you may be tempted to think that having this data actually puts you in control of the situation.
Not always. Let’s take a step back and look at an example.
Let’s say you install sensor-enabled RFID tags on your fleet vehicles. You are automatically presented with hub odometer readings, hours of operation, etc. You dutifully have this uploaded to the equipment maintenance record because you know that, every so many hours of operation, you have to inspect and/or replace something, such as brakes, bearings, tires or some other vital component.
The question I would pose is: “How do you know?” Is that just the standard number? Is that based on manufacturer’s recommendations? Is it an average interval based on fleet usage? Or, is it based on real-world experience?
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Let’s break this down. Suppose Truck A regularly runs between New York and Morgantown, W. Va. (377 miles). Truck B regularly runs between New York and Norfolk, Va. (360 miles). Do both trucks require the same maintenance schedule? (For those not familiar with the routes, the Allegheny mountains are on the way to Morgantown, and the run down I-95 on the East Coast is fairly level.)
Obviously, the answer is “no.” The few extra miles Truck A covers is negligible. What is significant is that Truck A experiences much greater stress on the motor, drive train and brakes going up and down the mountains.
So, knowing the hub odometer reading and engine hours doesn’t give you a real picture of the maintenance needs of these two trucks.
Of course, if Truck A hauls only a light, but bulky, load and Truck B hauls its rated capacity, the equation changes. And, there can also be differences in the way drivers treat the equipment.
While it may be advisable to err on the side of caution (which does not always happen), pulling a truck for inspection and maintenance too early means lost productivity and possibly excessive replacement-part costs. Pulling a truck too late can be a recipe for disaster.
That’s why a little knowledge (even if it comes in the form of a lot of data) can be dangerous. Unless you factor in load, route, driver and even weather conditions, you can’t develop an accurate preventative maintenance schedule. Admittedly, you can’t factor in every bit of data—such as the fact that Driver D always jams gears every time he has chili cheese fries with lunch—but you can build more information into your calculations if you look at your operation.
And, it’s not just trucks and maintenance schedules that I’m talking about. It’s any application in which there are a number of variables— including operator efficiency—that affect the results of a definable item or process. Relying on averages is fine for overall forecasting, but getting the numbers that make up those averages is where you may need to dig a little deeper.
And, as much as I loathe admitting it, implementing new AIDC solutions is not always the answer. Sometimes, it’s just making better use of the information you already have.
