If you’ve worked in reliability or maintenance for any length of time, you’ve probably sat through a meeting where someone asks “so how many spares do we need?” and the answer is basically “well, we’ve always kept two on the shelf.” Or the classic PM frequency debate — why is it every 6 months? Because it’s always been every 6 months.
That kind of thing bugged me. Not because people are lazy, but because the right tools to do it properly are usually locked behind expensive software or buried in a textbook somewhere. So I built four free, browser-based calculators that cover the core decisions reliability engineers deal with all the time. No logins, no licence fees, no fluff.
Here’s what they do:
Weibull Calculator
This is the starting point for pretty much everything in reliability. You’ve got failure data — now what? The Weibull Calculator takes your failure times and suspensions, fits a Weibull distribution, and gives you the full picture: probability plot, PDF, reliability curve, hazard rate, confidence intervals, and tabulated results.
Why does this matter? Because your shape parameter tells you what’s actually going on. β less than 1? You’ve got infant mortality problems — scheduled replacements won’t help you. β well above 1? That’s wear-out, and now time-based maintenance actually makes sense. Without knowing this, you’re just guessing at what strategy to use.
We built it to be simple. Punch in your data, get your plots, read off your B10 life or characteristic life, and move on. (Note it wont handle massive datasets since its brower based).
Weibull Calculator: https://weibull.pardusconsulting.com/
Optimal Maintenance Interval Calculator
So you’ve got your Weibull parameters — now the question is how often should you actually be doing maintenance? This is where most teams end up in a loop of opinions. “I reckon every 6 months.” “Nah, 12 months is fine.” Nobody wins because nobody’s got numbers behind their argument.
This calculator takes your failure distribution, your planned replacement cost, unplanned failure cost, and inspection cost and effectiveness, then works out the intervals that minimise your cost per operating hour. It gives you four answers: optimal inspection and replacement intervals, each for both long-term steady-state and short-term campaign situations.
That short-term vs long-term distinction is actually pretty important. If you’re running a 3-month shutdown campaign, the optimal interval is different from what you’d pick for ongoing operations over the next 5 years. Most people don’t think about that, but it can change the answer significantly.
The whole point is to turn a debate into a calculation. Plug in your numbers, get your intervals, and now you’ve got something defensible to take to the planning meeting.
Optimal Interval Calculator: https://optimal-maintenance-interval.pardusconsulting.com/
Spare Parts Optimisation Calculator
This one took a while to get right, because spare parts stocking isn’t one problem — it’s about five different problems wearing a trenchcoat. The part that fails once every 3 years needs a completely different approach to the consumable you go through weekly, and both are different again from the $500k insurance spare sitting in the warehouse “just in case.”
So we built five calculation pathways into one tool:
Intermittent / low-demand spares — set a target service level (say 95% chance of no stockout) and it tells you how many to hold. Works with MTBF for constant-rate items or Weibull for wear-out, and you can account for PM effects and whether you’re in a new fleet ramp-up or steady state.
Economic optimum — balances holding cost against expected stockout cost and shows you the total cost curve. When you need to justify a stocking decision to finance, pointing at the bottom of a cost curve works a lot better than “the maintenance guys said we need it.”
Reorder point and safety stock — for your regular-use, replenished items. Calculates ROP and safety stock and shows you whether demand variability or lead-time variability is the bigger driver, so you can actually fix the root cause instead of just adding more buffer.
Insurance spares decision engine — for the expensive, slow-moving, critical stuff. Gives you a structured recommendation (stock / marginal / don’t stock) with a breakeven analysis based on your downtime cost per hour.
ABC/VED classification — because you might have thousands of SKUs and not every one deserves a full optimisation study. This sorts your portfolio quickly so you know where to spend your time.
Spares Tool: https://spares.pardusconsulting.com/
Jack-Knife Diagram App
Most people default to Pareto charts for downtime prioritisation, and they’re fine up to a point. The problem is they only rank on one dimension — usually total downtime or number of events. So a piece of equipment that fails once for 40 hours looks the same as one that fails 40 times for an hour each. But those are very different problems that need very different solutions.
The Jack-Knife Diagram fixes this by plotting failure frequency against mean time to repair on a single chart. You can immediately see whether something is a chronic issue (fails all the time, moderate repairs), an acute issue (rarely fails but takes forever to fix), or both. And that distinction matters because it tells you whether you need a design change, a better procedure, or a logistics improvement.
Our app takes your failure codes, intervention counts, and MTTR data and builds the diagram for you. It also exports to PDF in a report format — add your logo, and you’ve got something ready to present without messing around in PowerPoint.
Jack-Knife Diagram Generator: https://jkd.pardusconsulting.com/
How They Work Together
These tools weren’t designed in isolation. There’s a natural flow: characterise your failures with the Weibull Calculator, optimise your maintenance intervals based on those results, set your spare parts levels using the failure rates and operating context, and use the Jack-Knife Diagram to keep an eye on where your maintenance effort should be going.
That said, you don’t have to use them as a set. Each one stands on its own. Pick whichever one solves the problem in front of you today.
They’re all free, they all run in the browser, and we built them because we think good reliability decisions shouldn’t depend on whether your company’s willing to pay for expensive software. Give them a go — and let me know what you think.
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