Predictive Maintenance 2.0: A holistic approach

Posted by Scott Mutchler

Oct 24, 2014 8:17:00 AM

Predictive maintenance (PM) programs, in asset heavy industries, can generate massive ROIs. These ROIs are delivered through the following benefits:

Benefits of Predictive Maintenance Programs in Asset Heavy Industries

  • Reduced asset failures
  • Increased asset production
  • Reduced unnecessary maintenance
  • Increased asset life

It’s worth noting that Predictive Maintenance delivers on both the cost saving and the revenue generation sides of the business.

Traditionally PM benefits are delivered by optimizing maintenance schedules to perform just-in-time maintenance on the highest risk assets. This is PM 1.0. PM 1.0 focuses heavily on high-risk assets.

While this approach is often the best approach, there are many cases where allowing high risk assets to fail is a better financial decision. This sounds hard to believe but the approach below shows how we can use the economics of maintenance and production to arrive at an optimal maintenance schedule that sometimes allows assets to fail.


Optimizing Predictive Maintenance Schedules

Optimizing predictive maintenance schedules relies on two separate buckets of information.

Calculating Net Present Value (NPV)

First, we have the probability that an asset will fail (with a specific failure mode) over the next t days or hours.

Second we have the costs associated with failure and required maintenance. We can combine these two factors into a single metrical called NPV of maintenance (where NVP is defined as a simplified calculation of net present value).

calculating net present value for asset maintenance

Or in plain English, the net present value of performing maintenance is the probability of asset failure multiplied by the difference in costs between the cost of running to failure and the costs of performing preventative maintenance.


Probability of Failure Calculation

In predictive maintenance, the probability of failure is calculated using predictive models. These models leverage [potentially] hundreds of different data inputs for historical asset failures (and non-failures) to accurately predict the potential for part failure over the next t days or hours. The costs for run to failure and maintenance are a mixture of both fixed and variable costs. The variable costs are also often the output of predictive models. We can break-down the costs as follows:

calculation for probability of failure


Costs for Preventive Maintenance Calculation

The total cost for run to failure is the sum of the lost production costs, replacement costs and labor.

Similarly, we can also calculate the cost for preventive maintenance.

preventive maintenance calculation


Final Equation for NPV

Generally, the parts and labor costs for running to failure will exceed the cost for performing preventative maintenance. However, the cost of lost production may be less for running to failure than if we perform preventive maintenance. For example, if we are generating electricity with a gas turbine and over the next 3 days while the price of energy is very high, then running to failure may be financially advantageous.

We can substitute the detailed costs to arrive at a final equation for NPV expressed as dollars of benefit:

final calculation for NPV

Not running for 3 days of high revenue generation, to perform preventative maintenance, may actually generate a negative NPV. In this case, running to failure is the best option. Here is a table that summarizes how NPV makes PM decisions crystal clear from a financial standpoint:

chart showing negative npv

As we can see, even though the bearing has a high probability of failure, repairing it is a bad financial decision. The seal, on the other hand, has a relatively low probability but replacing it has a positive NPV of $520.

NPV has the added benefit of allowing a company to identify the exact dollar amount benefit of a PM program. Using historical modeling, simple NPV calculations can justify investments into software and services to put a PM program in place.

Finally, NPV is the basis for building optimized maintenance schedules but PM 2.0 doesn’t stop there. PM 2.0 schedules must address high NPV maintenance first but it must also include constraints such as logistics, part availability, technician skillsets and vacations. This is where optimization engines can drive schedules that maximize the total NPV of maintenance.

Are you ready for Predictive Maintenance 2.0? If not, give QueBIT a call.


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