Find the optimal replacement interval

This is the calculation that turns a reliability model into a maintenance decision, and it answers a question every maintenance planner has argued about: how often should we change this thing out?

Replace too often and you're throwing away good life and paying for outages you didn't need. Replace too rarely and you're paying for failures — which cost more, because they happen at the worst possible moment and take other things with them. Somewhere between those is a minimum, and if you know the life distribution and the two costs, you can find it rather than argue about it.

What you need

Go to Strategy → Optimal replacement and pick a saved model. Then supply two numbers:

• Planned replacement cost — what it costs to change the component out on your terms, during a planned window.
• Unplanned (failure) cost — what it costs when it fails in service.

The absolute values matter less than the ratio between them. A 10:1 ratio pushes toward frequent preventive replacement; a 1.5:1 ratio means failures barely cost more than planned changes, so there's little to gain by pre-empting them.

Be honest about the unplanned figure. It isn't just the part and the labour — it's the lost production, the collateral damage, the callout at 3am, the expedited freight, and the safety exposure. Most people undercount it dramatically, and that single number moves the answer more than anything else on the page.

The result, and when there isn't one

Reliafy computes the long-run cost rate across candidate intervals and finds the minimum — the interval at which the cost per unit time is lowest — and compares it against the cost of simply running the component to failure.

Sometimes the honest answer is don't replace preventively at all, and the tool will tell you so. That happens whenever the component isn't wearing out. If your Weibull β is around 1, the failure rate is constant: a new unit is exactly as likely to fail as an old one, so swapping a working component for a fresh one buys you nothing and costs you a replacement. If β is below 1, scheduled replacement is actively harmful — you'd be removing survivors and installing the riskiest units you have.

This is why the fitted model matters so much here. Preventive replacement only pays when there's wear-out to pre-empt, and the shape parameter is what tells you whether there is. A schedule set by habit or by a vendor's default interval has no way of knowing.

Where to go next

If the component is inspected rather than replaced on a schedule — you're looking for a hidden failure rather than pre-empting a visible one — the failure-finding interval calculator under Strategy is the right tool instead.

If you want to know how many failures to expect across a fleet over the next year, rather than when to replace one item, that's fleet forecasting.

And if you're deciding among several candidate policies, save each analysis — Reliafy keeps them under Strategy → Saved analyses so you can compare and cite them later, which matters when someone asks in six months why the interval is what it is.