Your CMMS already knows which asset is robbing you. Two days to make it talk.
The full path from a messy downtime export to a defensible maintenance decision: metrics, data preparation, Pareto analysis, Weibull, and the strategy call at the end. Built for engineers and maintainers, and worked on real failure data all the way through.
Four things you can do on the Monday
Every site has the data. Almost none of it is analysed, because nobody was ever shown the path from a downtime export to a decision. This course is that path, walked end to end on real failure data.
Turn a CMMS export into evidence
Collect, clean and structure downtime and event data so it can carry an analysis, including the censoring and classification calls that decide whether the answer means anything.
Find the real bad actor
Pareto analysis by downtime and by event count, why the two rankings disagree, and how to choose the measure that sends the crew to the right machine.
Fit and read a Weibull
Determine shape and scale from your own time-to-failure data, interpret what β says about the failure, and predict the reliability you can expect next quarter.
Set frequencies from evidence
Bring the analysis home: use Weibull results and the P-F interval to defend maintenance task frequencies instead of inheriting them.
Two days, eleven sessions
Day one gets the data ready to speak. Day two is Weibull, prediction, and the maintenance decisions that come out the other side.
- 1Introduction to advanced reliability analysis. Why data-driven maintenance decisions beat opinion, and the toolset for making them.
- 2Key performance and reliability metrics. Availability, utilisation, reliability, MTBF and MTTR, and what each contributes to asset performance.
- 3Data collection and preparation. Sources of downtime and event data, and organising it so an analysis can stand on it.
- 4Data visualisation and Pareto analysis. Pareto by downtime and by event count, and reading them to make informed decisions.
- 5Data censoring and classification. Right, left and interval censoring, and classifying records into failure modes that mean something.
- 6Weibull: introduction. Shape and scale, and what the distribution reveals about how equipment fails.
- 7Weibull: determining and interpreting parameters. From time-to-failure and censored data to parameters you can read.
- 8Weibull: analysis and prediction. Calculating reliability metrics from the parameters and predicting future performance.
- 9Weibull: practical exercises. Worked on customised, site-based scenarios rather than textbook data.
- 10Maintenance strategy optimisation. The P-F interval, and using the analysis to set task frequencies you can defend.
- 11Course recap and close. The full path retraced, and where to take it next.
Optional modules, swapped in at booking
The course carries a bench of deep-dive modules that swap in to fit the crew: bad actor identification from equipment loss data, statistical methods, repairable systems analysis, reliability block diagrams, and Weibull++ for sites that run it. Tell us at booking where your team needs the depth.
Who this is for
Engineers and maintainers
The audience the course was built for. Anyone who owns equipment performance and wants their arguments backed by the site’s own data.
Reliability engineers early in the role
The complete applied toolkit in one hit: data preparation, Pareto, Weibull and strategy optimisation, in the order you actually use them.
Planners and maintenance supervisors
The people who act on the analysis. Understanding how the bad actor list and the frequencies were derived is what makes them defensible on the floor.
What you need before you start
Comfort with the basic reliability vocabulary. Fundamentals of Reliability Engineering is the natural lead-in, and site experience with your own equipment covers most of the rest. No software and no statistics background required.
The applied bridge between the fundamentals and the deep dives
This course walks the whole data-to-decision path once, end to end. The deep-dive courses then take each step further when your site needs it.
Fundamentals of Reliability Engineering
2 days. The language: metrics, criticality, failure behaviour and strategy.
Advanced Reliability Analysis
2 days. The full path from CMMS export to maintenance decision, worked on real data.
Weibull Analysis, RCM Foundations, Maintenance Plan Optimisation
The deep dives on the steps this course opens: life data analysis, task selection, and plan rebuild.
Learn the method. Then put AI to work on it.
Understand the analysis first, leave with the templates, then use AI to do in hours what used to take days. The course closes with a working session on exactly that, inside your site’s IT and confidentiality rules.
What AI accelerates
The data preparation half of this course: extracting failure events from free-text work orders, flagging censoring candidates, and building the Pareto inputs in minutes instead of days.
What stays with the engineer
Which measure to rank by, whether the fit means anything, and what the analysis changes in the plan. AI does the clerical mile. You make the call it sets up.
What you leave with
A prompting guide written for reliability work, a prompt library for data preparation and analysis, and a setup checklist for using AI within site IT and data rules.
We run our own operation this way. The AI workflows taught here are the ones we use to deliver client work, from OEM manual extraction to failure history analysis.
Three ways to take it. One seat or the whole crew.
Every course runs in all three formats. On site is priced as an engagement rather than per head, so crew size is your call. Public and online cohorts confirm once a small minimum books, and every booking is protected: if a date does not confirm, you choose the next cohort, the online session, or a full refund.
Public classroom
Runs in our training room in Maroochydore on the Sunshine Coast. Book one seat or several and work alongside engineers from other operations. Dates confirm once the cohort reaches its minimum.
Live online
Same instructor, same exercises, delivered live rather than recorded. Confirms on a smaller cohort than the classroom, and it is the route in from anywhere outside the Sunshine Coast.
On site at your operation
We come to you and the analysis runs on your equipment. Priced as an engagement, not per head.
Built for a working gold operation, not a lecture hall
This course exists because a major Pacific gold operation asked for it, and it was delivered live online to their engineers and maintainers. The person at the front of the room runs these analyses on operating sites every week.
Delivered by the engineering bench, not a training department
Courses are taught by HolisticAM reliability engineers who deliver this same work for clients between courses. Whoever stands at the front ran this analysis on an operating site recently, so the examples are current and the answers are practical.
Proven in delivery
Training is a delivery arm of the consultancy. The most recent full program trained 26 engineers and maintainers at a major Pacific gold operation, delivered live online, with certificates of completion issued.
It gets used on the Monday
Within days of that delivery, attendees were sending their own analyses of live plant equipment back to the trainer for review. That is the outcome every course here is built for.
The session was informative, well-structured and incredibly helpful. The knowledge gained will certainly enhance my RCA facilitation skills at work.
This course was terrific and beneficial. It opened my mind to expand and think differently. I enjoyed every moment.
Across 27 external participant surveys from our Apollo RCA courses, self-rated knowledge moved from 3 to 8 out of 10.
Before you book
How is this different from the Weibull Analysis course?
This course walks the whole applied path: metrics, data preparation, Pareto, an applied introduction to Weibull, and strategy optimisation, all in two days. The Weibull Analysis course goes deeper on the statistics alone: estimation methods, goodness of fit, growth analysis and repairable systems. Do this one to build the end-to-end capability. Do that one when life data analysis becomes a core part of the job.
Our CMMS data is a mess. Is that a problem?
It is the expected condition, and cleaning it is a third of the course. Data collection, preparation, censoring and classification each get a session, because an analysis is only as defensible as the data underneath it. Bring the export as it is.
Do we need to buy software?
No. The whole path runs in Excel and the tools your site already has. For sites that run Weibull++ there is an optional module that applies the same analysis in it, but nothing in the course depends on a licence.
Can AI just do the analysis for us?
AI is genuinely good at the preparation half: extracting events from work-order text, cleaning exports, drafting Pareto inputs. What it cannot do is own the ranking call or the frequency decision, and without the method you cannot audit what it hands you. The course teaches the method precisely so your team can use AI hard and judge the output. That is why the AI session closes the course instead of replacing it.
Can I do it online?
Yes, and this course has already run that way: its first delivery was live online to a working gold operation. Online is delivered live with an instructor, not recorded, and you work the same exercises on the same data.
Where do public courses run?
In our training room in Maroochydore on the Sunshine Coast. If travel does not make sense from where you are, the live online cohort is the same course with the same instructor, and on site delivery brings it to your operation.
What are the optional modules?
Bad actor identification from equipment loss data, statistical methods, repairable systems analysis, reliability block diagrams, and Weibull++ for sites that run it. They swap into the standard two days at booking, so the course bends to where your crew needs depth.
The data is already sitting in your CMMS. The capability is two days away.
From downtime export to defensible maintenance decision, worked end to end on real failure data.