Need a better way to show the contributions of a reliability effort to your bottom line?
Most people in the reliability profession probably have heard the saying that the maintenance of today is the capacity assurance of tomorrow. Many in our field would agree that business trends already have taken the industry to that day of the future. Our teams no longer maintain the status quo. What we do is strive to assure our assets’ capacity by constantly optimizing equipment availability to make the product when it is scheduled to be made. We work hard to make sure that machines don’t generate scrap. And we ensure that the equipment runs as close to the expected productions speeds as possible. For today’s capacity assurance managers, overall equipment effectiveness (OEE) works very well as the key performance indicator of our success.
Our business leaders understand the importance of the reliability effort, too. But, they also understand the need to control manufacturing overhead. As a result, they must clearly see how a reliability effort contributes to the bottom line. What’s the best way to make this type of business case?
Keep it simple
A reliability effort does contribute to an increase in the return on assets. The most direct and easy-to-understand impact resides in the expenses section of the income statement. There, it is apparent that the greatest part of the reliability financial benefit lies in the conversion costs reduction.
Let’s think in simple terms. We expect a machine to be available for a certain period of time to make a specific number of product units. When the hard work of our maintenance teams leads to higher machine reliability, the company spends less time making those expected units of product. The machine is operational and the operators are standing by. Their labor costs in dollars per unit already are accounted for.
Given this scenario, we now have three choices: send the operators home, reassign them to do something else or make more units of product by running the machine and incurring related material costs and all other conversion costs but the labor. In other words we achieved a reduction in labor costs per unit of product or generated additional capacity to make more units of product without having to account for the labor.
Is that simple enough? Let’s see if we can translate that logic into dollars and cents. The basic conventions that we will start with are shown in Table I.
Suppose that a business needed to make X number of product units and scheduled N number of hours to do it. In actuality, however, the equipment only ran at UT1 uptime. So, to make the intended units of product the number of run hours was:
N + (1 – UT1) • N = (2 – UT1) – N (1)
Similarly, at improved UT2 uptime the time to make needed number of units would be:
(2 – UT2) • N
We can express the Total Direct Labor Costs without Uptime Improvemetn in two different ways by using either hourly wage or the labor costs per unit:
TLC1 = W • (2 – UT1) • N = VL1 • X (2)
Solving for W:
W = (VL1 • X) / ([2 – UT1] • N) (3)
Applying the same reasoning:
W = (VL2 • X) / ([2 – UT2] • N) (4)
([VL1 • X] / [(2 – UT1) • N]) = ([VL2 • X] / [(2 – UT2) • N]) (5)
VL2 = VL1 • ([2 – UT2] / [2 – UT1]) (6)
Plugging in the above findings to derive the conversion costs per unit of product we will get:
CCU1 = V + VL1 (7)
CCU2 = V + VL2 = V + VL1 • ([2 – UT2] / [2 – UT1]) (8)
And now, if we apply some more basic algebra, we easily come to the conclusion for the reduction in conversion costs per unit:
= CCU1 – CCU2 = VL1 • (UT2 – UT1) / 2 – UT1
Formula 9 can be expressed in a verbal statement shown in Fig 1.
Any reliability practitioner after looking at that statement and thinking for a few minutes would say: “I knew that!” There is no doubt that it is somewhat intuitive for the insiders. For maintenance and engineering managers, it is quite empowering in that it can be applied to any time interval and any area of the production process or piece of equipment to prioritize the allocation of resources. This statement also appears to be a straightforward tool for quantifying the reliability objectives for the business leadership and showing them the gains triggered by the improved uptime. Let’s demonstrate.
Say, for example, that last year a manufacturing area produced 100,000 widgets at $8.00 of labor costs per every widget and at 85% physical availability or uptime. This means that due to equipment reliability issues the machines ran only 85 out of every 100 scheduled hours. This year, our hypothetical maintenance organization has committed to increasing the uptime to 90% by improving reliability. Then:
VL1 = 8 ($/Widget) UT1 = 0.85 UT2 = 0.90
If we plug in the numbers from Formula 9, the maintenance objective for the reduction in conversion costs per unit for this year is:
= 8 • ([0.90 – 0.85] / [2 – 0.85]) = 8 • (0.05 / 1.15) = 0.35 ($ / Widget)
Accordingly, the total commitment by maintenance for this year to reduce labor costs and consequently the overall conversion costs at the same production levels of 100,000 caused by either increased or decreased reliability. The widgets is:
0.35 ($ / Widget) • 100,000 = $35,000
Table II shows a few semi-random examples of calculated conversion costs variances related to uptime changes caused by either increased or decreased reliability. The numbers in Column 6 of this table result from plugging all values into Formula 9.
If absorption is a company’s costing method of choice, the fixed costs are treated as product costs just like labor. Since in that scenario fixed costs are assigned to each unit produced, the same uptime driven proportionality factor that is demonstrated in Fig. 1 can be utilized to derive the fixed costs variance. The improved uptime will demonstrate a favorable variance for the reporting period.
Crunch your own numbers
It may make for an interesting exercise to apply this approach to a few hypothetical scenarios for your own business—and crunch some numbers of your own. Some results may appear puzzling, though.
One important thing to keep in mind is that the numbers are not constant. What this means, more often than not, is that companies take advantage of improvements in reliability and uptime by making more units of product. That additional capacity without added labor costs translates into higher labor productivity, which prompts lowering the number for the labor costs per unit produced for the next round of calculations. So, ultimately, the model needs to be readjusted for every consecutive time period.
It is possible that such a model can be taken further to try to predict the impact of various reliability trends on the bottom line. It also is possible—especially when analyzed on a smaller scale—that the savings may never materialize into something measurable. But then the numbers can be reported to management as intangible labor productivity gains.
In its present form—with all other factors but uptime being irrelevant—the method seems to be surprisingly simple and elegant. We can choose to analyze a quarter or a month or even a week. We may decide to run the numbers for the separate machines or for the entire plant or multiple plants.
As long as the data being used are accurate, the numerical results will show what the business leadership really needs to know. Hopefully, this will help enable business teams to effectively rank priorities and make the right choices on funding and resource allocation. MT
Mike Shekhtman is a senior regional reliability engineer for the Goodyear tire manufacturing North American region, based at Goodyear Tire and Rubber corporate headquarters in Akron, OH. Prior to joining Goodyear earlier this year, he had spent 20 years in the manufacturing industry in various capacities in maintenance management and engineering. A licensed Professional Engineer and a Certified Maintenance and Reliability Professional, Shekhtman holds an MSME degree from St. Petersburg State Polytechnic University in Russia and an MBA from Cleveland State University. Telephone: (330) 796-7245; e-mail:firstname.lastname@example.org