Every fleet manager is looking for opportunities to improve efficiency and save money. We consider many “what-if” scenarios looking for the best return. Should we keep assets longer or replace with new? Do drivers and technicians need more training? Should we automate more, or change maintenance practices?
These, and many other such “what ifs,” require a sound approach to analyzing the best plan of action.
For example, if you’re considering replacing trucks you believe are getting too expensive to maintain, have you compared their maintenance costs to new trucks? Newer units may have higher maintenance costs due to emissions equipment and other complexities. If some of the maintenance costs in your existing fleet are accident- or abuse-related, newer units won't make those costs go away.
Poor or incomplete data leads to flawed analysis
No one wants to invest time and money in a strategy and then get a sub-standard, or even negative, return. There’s an old phrase that goes back to when businesses first started using computers: “garbage in, garbage out.” In other words, the quality of reporting that you get from your system is directly related to the accuracy of the data that gets entered.
The bad data input problem is magnified if you are using one or two years of fleet cost data and projecting it forward; now the $5,000 of cost captured in the wrong Vehicle Maintenance Reporting Standards (VMRS) code becomes $10,000, and if you have 100 assets in that category, becomes $1 million. Little mistakes can compound into skewed analysis results that can seriously impact conclusions. So how can you apply best practices for capturing and categorizing costs on your existing assets, to make sure that the basis for your analysis is valid, as well as identifying some often-forgotten hidden or soft costs in new scenarios?
If you have a good computerized maintenance management system (CMMS), you already have the tool that you need most. Good systems make the process of complete data capture part of normal workflow. If basic data capture is easy but being specific requires the technician to take extra time, you can be sure the extra data will seldom be there. A good example is that if your system supports the full nine-digit VMRS coding for parts and labor, make sure that those functions are enabled.
A real-world example
Your company starts servicing a new region and builds a new shop. Unfortunately, construction runs long, and startup is rushed.
Scenario A:
Since there is very little time to get the parts room set up, parts are only coded with the top-level VMRS system code. In addition, the assistant fleet manager who was supposed to set up the Standard Repair Tasks/Times (SRTs) took another job and SRTs were never implemented. The situation that you have now is that your system captures the maintenance cost in terms of parts and labor, but only at a high level.
After running for a few years, your gut tells you that your brake repair costs and frequency seem high based on your experience, and when you run repair cost reports you see that brakes are indeed a problem. But now what? Your parts and labor data only reveals high brake costs. There are several possibilities:
- Driver abuse
- Poor quality repairs/PMs, which may be a technician training issue
- Aftermarket parts quality versus OE parts
- Incomplete/poor repair data capture
Unfortunately, trying to identify which of the above is the real issue will now require a potentially time-consuming manual review of driver, repair and parts data.
Scenario B:
When you start up your new shop, you bite the bullet and pay the parts team to get parts coded to full nine-digit VRMS (system, assembly, component). Since you don’t have the manpower to immediately define your SRTs, you set aside a few hours a week and knock them out one or two at a time, beginning with the most common repairs. It takes a while, but once finished, you have a much better benchmark in terms of repair labor by task and technician. Now when that higher-than-expected brake repair cost problem appears, you have the same possibilities; but the data you have been capturing allows you much faster identification and correction.
In the situation above, the issue turns out to be an otherwise good technician who was skipping the full caliper inspection as part of the brake service (and corroded caliper slides or pins were causing the premature pad wear). Your SRT reporting would have shown that this particular tech typically finished the job faster than others, and that would be a red flag. With bad or incomplete data, you may have incorrectly drawn a connection to a driver or drivers, and then chased the wrong solution while the real problem remained.
Upfront investment delivers returns
Note that having this good data wouldn’t create any additional data entry work for your techs, parts room or even clerks because all of the definitions had already been set up; that’s the key.
The above issues could play out in hundreds of different ways in your fleet; between your assets, parts, techs, drivers, climate, etc., there are a lot of potential causes. Having solid and complete data allows you to really manage “what-ifs” with some confidence.
If you don’t have a CMMS, you need one. If you do, make sure that you are using it to its fullest. It will help you produce good “what-if” projections, and it will be a huge help in your daily operations as well.
Bob Hausler serves as the Vice President, Marketing & Technology for Dossier Systems. He is responsible for marketing and product development, with more than 35 years of experience delivering software and services for fleet and industrial applications.
