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How to Demonstrate “Payback” in Maintenance

If you’ve heard the phrase “the hidden cost of failures” then you’re probably aware that it can be a real challenge to get agreement across the organization as to what the hidden cost actually equates to.

In contrast, the “costs of maintenance” (those tasks that are planned and budgeted for) is very well known and visible to all. So how can we demonstrate the overall “payback” that the investment in maintenance provides?

This article looks at some of the reasons why maintenance teams struggle to measure the value of their actions and outlines a way to demonstrate the payback in maintenance without relying on trial and error.

Common barriers to measuring the value of maintenance

The sad reality for many organizations is that a simple, clear, and quantitative process for determining the value of the maintenance actions is not available – or they are simply unaware of methods to achieve this.

Too often maintenance decisions are made and implemented hoping that the outcomes will be better. The problem with this approach is that decisions can only be tested after they are implemented – it’s called “change and measure” where we do it and then measure the outcome to decide if we are better, and if not, then we change it again. Often we can’t relate outcomes to the specific changes, or even worse we don’t get the changes we hoped for.

How much better would it be to be able to test the effects of these maintenance decisions and see a before and after result to determine the effectiveness and payback of the maintenance actions or changes without the risk of a poor maintenance decision manifesting in real life?

What if we could simulate the outcomes of a change in maintenance and be able to quantify the direct effect of that change?

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Using simulation to evaluate maintenance decisions

Uptime predictions on alternative strategies can supply us with the necessary data and justification to implement actions before actually expending the resources to achieve a result.

Reliability improvement tools such as Isograph’s Availability WorkbenchTM can give the organization this capability.

Through the use of RCM techniques the assets in question can be modeled, failure information can be assigned, maintenance actions and the associated costs can be defined, and the cost of downtime as well as risk items like safety and environmental severities can be incorporated to simulate the lifetime costs.

Once these components are accounted for, the results of various maintenance actions can be compared to see the effects on costs, projected numbers of failures, or even risk profiles to determine the optimal maintenance action.

For “green field” assets a comparison of the expected costs of a “run-to-fail” scenario against OEM recommended strategy can be made. A cost benefit ratio of this strategy can be considered as well as those areas where additional or varied strategies may be required.

For “brown field” processes the existing maintenance actions and their effectiveness can also be assessed and the same comparisons applied.

In summary, a probabilistic based and quantitative RCM simulation approach can provide an organization with the tools and processes to measure the effectiveness of maintenance actions and to determine the costs of maintenance, and therefore the payback expected. Using this approach can also help to begin changing the culture of an organization more towards proactive maintenance.

“R.A.I.” the Reliability.aiTMChatbot

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