May 22, 2012
Using Asset Maintenance Software to Manage Maintenance
A thought recently struck me: How many people are swimming in a sea of asset maintenance data, trying to figure out how to use it to their advantage without drowning in all the information? I’m sure that’s most folks considering the vast Internet Ocean in which we all live. But let’s narrow down the scope a bit: Your asset maintenance software!
Maybe you've been doing it for years or maybe just a few months, but you've gathered lots of data on your Assets, Physical Locations, Spare Parts, Employees, and so on. Now it’s time to refine all that data, filter it out and make sense of the really valuable information it contains. Yet, exactly how is that list of equipment or that completed work order report really going to save your department money, improve performance, or find that wily problem out on the production line?
To begin, you need to think about the data your asset maintenance software collects differently than just ‘lists’ of part numbers and such. Your first objective is to enter lots of related information, not merely the bare essentials. If you only enter an Equipment# and a Work Order#, then all you will get is…well, that’s all you get. Not enough to learn anything important. But if you diligently enter in vendor/manufacturer information, specifications, work descriptions, failure codes, etc., basically whatever you can, then your asset maintenance software can go to work and reap a virtual harvest of cost savings and gains in productivity.
For example, let’s think about a way to save the maintenance department money that really doesn’t take a huge amount of data entry, other than perhaps some work history. First, let’s start out assuming our asset maintenance software has recorded a specific plant equipment asset with the Asset#, the Asset Keyword, the Manufacturer, and some related Work Orders with Failure Codes.
Okay! Most shops I’ve seen have multiple manufacturers for similar equipment. For instance, one City Parks Department has several different riding lawn mowers makes and models. Of course, knowing this we can simply compare work history between different manufacturers. But, let’s look at the data itself as a way to compare how well one manufacturer truly equates to another. To accomplish this, require your asset maintenance software to run a report that sorts work history by Keyword, with the total number of Failures for each Manufacturer. For instance, the City Parks Department ran a report on all their lawn mowers using a specific Keyword that showed Manufacturer A with 4 Failures, Manufacturer B with 9 Failures and Manufacturer C with 15 failures (assuming one mower per manufacturer).
What have we learned? Well, we can look at this data a few different ways:
- Initially we can compare the types of failures between mowers. Are we constantly repairing the same thing or are the failures scattered between multiple problems? A study of the failure history may reveal a problem in the environment, such as improper equipment storage or an incorrect oil mixture. Determining the underlying root cause of any failure is the first step in preventing it.
- Next, should we possibly look at altering preventive maintenance (PM) for Manufacturer C to reduce the failures? Maybe we are not doing something according to their specifications. Perhaps a review of all PM mower maintenance is in order. More mowers ready to use means more crew productivity.
- Finally, we might consider removing Manufacturer C from our line of equipment. It is failing nearly 4x the rate of Manufacturer A. If we track maintenance costs on our work orders, we can determine an ROI on the capital purchase of new mowers from Manufacturer A. Over time, the department will significantly reduce repair costs since there will be far fewer failures.
Obviously you can extend this simple example to a variety of equipment types, and improve upon it. There are no "right" or "wrong" answers, just the proper solutions that work for you. Please share your ideas, what’s worked, what hasn’t. It will be like a life vest for those still flailing about the flotsam in their ocean of data.