Optimal PM Interval Calculator (Weibull Cost Model)
Most PM intervals were set by the vendor manual or by habit. This calculator shows the interval that minimizes cost per operating hour for a component that wears out, and what your current interval costs relative to that optimum.
Your numbers
Hours by which about 63% of units have failed. Fit from failure history or use a vendor figure.
Above 1 means wear-out. Roughly 2 to 4 is typical for bearings, seals and belts. The model needs a value above 1.
Parts, labor and planned downtime for one scheduled PM task.
Repair, secondary damage, lost production and expediting for one unplanned failure.
Your results
First-order model: expected failures per interval are approximated by the cumulative Weibull hazard (T/eta)^beta, which is most accurate when failures per interval are well below one. Validate with your own failure data before changing a PM.
Annual cost vs PM interval
Sweeps Your current PM interval from 500 to 8000, holding your other inputs fixed.
Review my PM intervals against ERP failure history
An optional expert review of how your maintenance plan cycles compare with the failure data in your ERP or CMMS.
Optional. The tool and every export work without this. No spam.
How to use it
Enter the Weibull characteristic life and shape factor for the component, the cost of one planned replacement, and the cost of one in-service failure. Then enter your current interval and annual operating hours. The result shows the optimal interval, cost per hour at both intervals, and the annual saving. The chart sweeps the interval so you can see how flat or steep the cost curve is around your current setting.
- Use hours of operation, not calendar time, when the asset runs irregular shifts.
- Count the full cost of a failure, including secondary damage and lost output.
- If beta is close to 1, failures are random and fixed-interval replacement does not help.
The model behind it
The tool uses a classic preventive replacement cost model. Cost per unit time at interval T is (Cp + Cf x H(T)) / T, where H(T) = (T/eta)^beta is the cumulative hazard, an approximation of the expected number of failures in an interval. Setting the derivative to zero gives T* = eta x (Cp / (Cf x (beta - 1)))^(1/beta). The optimum exists only when beta is above 1, and it moves earlier as failures get more expensive relative to planned work.
Reading the result
A large gap between your annual cost and the optimum means the interval is either too long, so failures dominate, or too short, so you replace healthy parts. The chart is usually flat near the minimum, so an interval within 20 to 30 percent of the optimum is often good enough, and you can round to a convenient schedule such as a shutdown date. If your interval is far below the optimum, check whether the PM is destroying value through infant mortality after reassembly.
Common mistakes
Fitting beta from too few failures, mixing different failure modes into one Weibull, and using a failure cost that ignores production loss are the usual errors. Also remember that condition-based inspection can beat any fixed interval when a reliable warning signal exists. In an ERP, the optimum should feed the cycle length of the maintenance plan, for example in SAP PM, Infor EAM, Epicor or Dynamics asset management, so treat the output as an input to plan maintenance, not a one-time answer.
Frequently Asked Questions
What if I do not know the Weibull parameters?
Estimate them from failure dates for the same component across similar assets, using Weibull plotting or median rank regression. Without data, use vendor life data and a beta of 2 to 3 for wear-out parts, then test how sensitive the answer is by sweeping the chart.
Why does the model need beta above 1?
If beta is 1, the hazard rate is constant, so replacing a working part early does not lower failure risk. The cost per hour keeps falling as the interval grows, and the best policy is run to failure. Below 1, parts improve with age.
Is this block or age replacement?
It uses a first-order cost rate that is exact enough for both when failures per interval are low. For high failure counts per interval, a renewal-theory model gives slightly different numbers, but the optimal interval is in the same range.
Should I always move to the calculated optimum?
Not blindly. Check that the PM can be done at that interval without extra downtime, that your failure data are credible, and that a cheaper inspection or condition-monitoring task would not do the job better.
If your failure history and PM costs already sit in your ERP or CMMS, Netray can help pull them into a repeatable analysis that flags plans whose intervals look off. This is optional; the calculator works without it.
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