One of the best parts of authoring the Emerson Process Experts blog is some of the conversations that follow in phone calls and emails. These can happen even years after a post has been published. A good example is one that sparked from the post, What is Your Reactive Maintenance Percentage?
I received an email asking some great questions:
I just read your short but interestingly accurate article entitled, “What is Your Reactive Maintenance Percentage?” and am wondering if you’ve done any further studies relative to ROI? I’m kicking around coming up with percentages to work within the following areas:
- Repairs (reactive) vs. preventive maintenance: I’ve found one mention that a savings of 30-35% can be had in operational costs due to unscheduled parts, labor or vendor cost. For example, not maintaining a machine gearbox by changing the lube cartridge can cost upwards of $20k to refurb it, vs. a minimal monthly labor fee to inspect and keep up on lubrication requirements.
- Increased productivity: In other words, giving “focus” to labor being spent, sometimes on a standby basis, for problems that might come up. This is an area we CAN put a number on. Based on time and motion studies I’ve done in manufacturing-type industries, I believe a PM program, using a very conservative estimate of saving 1 hour per day for 1 employee calculates to: 1 hour/day x 5 hours/week x 52 weeks/year x $70/hour (burdened) = $18,200 per year. Multiply this by a couple more employees and you have a fairly substantial ROI to play with.
I went back to Emerson’s Bill Broussard, whose expertise I had cited in the original post. Here’s a portion of his response emailed back:
The ROI equation, I have found, ultimately has to take a metric that eliminates the emotions involved around a piece of equipment or operational area.
What I have found to work are a few things. And my perspective, honestly, is to help folks who bought our technology continue to get the value from it.
First, we look at apps where an end user has already made an investment in any kind of predictive technology. We take a group of assets, say 200, and go to their CMMS (work Order management system), take a period of time (we often use 6 months, but 12 works as well), and pull from this system the total number of work orders executed against these assets. The CMMS system typically tags the WO [work order] as emergent or planned. So, from there, it is easy to benchmark the planned versus reactive effort for the site.
Second, for sites that either are in the FEED / Design stage, or for brown fields that are trying to figure out how to stop the ‘fire fighting mode’, we apply an asset criticality ranking. This process is a groupthink approach driven by someone who has done it before. It is often effective from a ‘change culture’ perspective to have an external influencer to drive the meeting discussion. Once complete, the rank on top critical assets suddenly is material and in front of everyone. This then allows a ‘how do these assets fail’ discussion to take place, and out of that discussion comes the ‘how do I rationalize investments in predictive intelligence’, and frankly, the whole predictive / proactive allocation approach.
So, in summary, it is the asset’s criticality to the operation that should drive the ROI discussion.
Here’s an article I wrote recently on this process, Integrating Asset Management and Maintenance. Emerson is starting to utilize this process in our own FEED efforts.
I’ve mentioned in the past that there is quite a bit of wisdom trapped in all of our email inboxes and sent items folders. I hope digging an occasional one out helps others with similar questions.