A machine downtime monitoring system that finds the lost hours
Every stop costs you output, but most stops go unrecorded or land in a spreadsheet nobody trusts. Brilliqs captures each stoppage the moment it happens, tags it with a reason, and shows your team the causes worth fixing first.
- Every stop captured and reason tagged
- Top downtime causes ranked for you
- MTTR and MTBF tracked per machine
- Years in delivery
- 20+Years in delivery
- Enterprise clients
- 135+Enterprise clients
- Data projects
- 500+Data projects
- Logged and tagged
- Every stopLogged and tagged
Stop guessing where your lost time goes
Ask a maintenance team where their downtime comes from and you often get a hunch, not a number. Stops happen all shift long. Some get scribbled on a log sheet, many get missed, and the short ones that add up the most rarely get recorded at all. The result is a plant that loses hours it cannot name.
A downtime monitoring system fixes that at the root. It watches each machine, marks the exact moment a line stops, and prompts the crew to say why. Planned and unplanned stops are separated, reasons are grouped into clear codes, and the lost minutes are counted honestly instead of estimated after the fact.
Brilliqs builds this around your equipment and your reason codes, not a generic list. We connect to what you already run, set up the stoppage categories your team uses, and give you a ranked view of what is really eating your available time so the next fix is the one that matters most.
Why lost time stays invisible
When stops are logged by hand or not at all, the biggest losses hide in plain sight. These are the gaps a downtime monitoring system closes.
Short stops go uncounted
Micro stops of a minute or two rarely reach the log sheet, yet across a shift they can rob more time than the big breakdowns everyone remembers.
Reasons get guessed later
When a stop is written up hours after it happened, the reason is filled in from memory. Vague codes like machine fault tell you nothing you can act on.
Planned and unplanned blur together
Changeovers, breaks, and breakdowns land in the same total. Without a clean split, you cannot tell a scheduling problem from a reliability one.
No ranking of causes
Even when stops are recorded, they sit as a long flat list. Nobody knows which single cause is costing the most, so effort scatters instead of targeting.
What your downtime monitoring system does
Three jobs sit at the center of the solution: catch every stop, name it correctly, and point you at the cause worth fixing first.
Capture every stop automatically
The system watches machine signals and marks the exact start and end of each stop, including the short ones a person would never bother to log. Nothing slips through, so the downtime total you see is the real one.
- Automatic detection of stop start and stop end
- Micro stops captured alongside long breakdowns
- Exact duration recorded to the second
- Planned stops such as breaks and changeovers marked apart
Tag each stop with a reason
When a line goes down, the crew picks a reason from codes built around your plant. The prompt comes while the stop is fresh, so the reason is accurate, not a guess added at the end of the shift.
- Reason codes shaped to your equipment and process
- Operator prompt at the machine while the stop is live
- Reasons grouped into families for clean reporting
- Free text notes for anything the codes do not cover
Rank the causes that cost you most
The system sorts your lost time into a Pareto view so the largest cause sits at the top. Instead of a flat list of stops, your team gets a clear order of what to tackle to win back the most hours.
- Downtime Pareto by reason, machine, and line
- Lost minutes totalled per cause over any period
- Repeat offenders flagged before they become chronic
- Trend view so you can prove a fix actually worked
Features that turn stops into fixes
Practical tools that make downtime something your team measures and reduces every week, not a number they argue about in the morning meeting.
Live stop detection
The moment a machine goes down, the stop opens on screen and the clock starts, so nothing runs unrecorded.
Reason code prompts
Operators tag the stop from your own code list while it is still happening, keeping every reason accurate.
Downtime Pareto
Lost time is ranked by cause so the biggest loss always sits at the top of the list, ready to act on.
MTTR and MTBF tracking
Mean time to repair and mean time between failures are calculated per machine so reliability is measured, not assumed.
Response alerts
When a stop passes a set threshold or stays open too long, the right person is notified to speed up the response.
Loss trend reports
Weekly and monthly views show whether a given cause is shrinking or growing, so fixes can be proven or revisited.
What plants gain from tracking downtime
The system is not about counting stops for its own sake. It is about winning back hours by fixing the causes you can finally see.
A complete record
From micro stops to full breakdowns, every stoppage is captured, so the downtime you report is the downtime you actually have.
Effort aimed at the right target
A Pareto view puts the largest loss first, so maintenance and improvement work goes where it wins back the most time.
Shorter response and repair
Alerts and clear reasons help crews reach the machine sooner and act with the right part in hand, cutting time to repair.
Recurring stops removed
When the same cause keeps showing up, it gets designed out, so the stop that used to happen weekly stops happening at all.
How Brilliqs sets up your downtime tracking
A clear path from first walk of the floor to a live system your team uses on every shift. No long study, no rip and replace.
Agree your reason codes
We sit with maintenance and production to build the stoppage codes that match your machines, then split planned time from unplanned cleanly.
Connect the machines
We read stop signals from your PLCs and controllers, and add a simple operator input where a line has no sensor to report itself.
Set up capture and prompts
We tune what counts as a stop, set the micro stop threshold, and design the operator prompt so tagging takes seconds, not minutes.
Launch and improve
We roll out line by line, train the crews, and review the first Pareto with your team so the system starts cutting downtime right away.
The downtime signals your system puts on one screen
A downtime monitoring system earns its place through the measures it makes clear. These are the signals we surface most often for plants.
Downtime capture
- Total stop time per machine, line, and shift
- Count of stops and average stop length
- Micro stop time separated from long stops
- Planned downtime against unplanned downtime
Reliability measures
- Mean time to repair for each machine
- Mean time between failures over any period
- Availability loss from stops against planned run time
- Time from stop start to maintenance response
Reasons and losses
- Downtime Pareto ranking causes by lost minutes
- Top reason codes per machine and per line
- Changeover and setup time between runs
- Repeat stops flagged as chronic losses
Where a downtime monitoring system earns its keep
The same record of stops supports very different jobs across the plant. A few of the ways teams put it to work.
Cutting the top cause
Improvement teams open the Pareto, take the largest cause, and drive a project against it. Because the lost time is measured, the saving is easy to prove once the fix is in.
- Biggest loss picked straight from the ranking
- Before and after lost time compared on the same view
- Wins backed by numbers, not opinion
Speeding up maintenance response
Maintenance managers watch open stops and response times. When a machine goes down, an alert reaches the right person, and MTTR shows whether repairs are getting faster over time.
- Live alerts on stops that stay open too long
- Response time tracked from stop to arrival
- MTTR trend per machine reviewed each week
Taming micro stops
Short frequent stops often hide the largest loss. The system counts them properly, so teams can finally see a line that stops for a minute forty times a shift and act on it.
- Micro stops counted rather than ignored
- Frequency and total time shown side by side
- Nuisance stops traced back to their source
Planning maintenance from failures
Reliability engineers use MTBF to see which machines fail most and how often. That turns a reactive fix list into a planned maintenance schedule built on real failure patterns.
- MTBF compared across machines and lines
- Failure patterns used to time planned work
- Reactive breakdowns turned into scheduled tasks
Hand logging versus a downtime monitoring system
The difference is not just neater records. It changes which stops you can see and which causes you can remove.
| Downtime logged by hand | With a downtime monitoring system | |
|---|---|---|
| Which stops get recorded | Long stops only, if someone remembers | Every stop, including short micro stops |
| How reasons are set | Filled in later from memory | Tagged at the machine while the stop is live |
| Planned versus unplanned | Mixed together in one total | Split cleanly so each is measured on its own |
| Finding the top cause | Guesswork from a long flat list | Ranked by lost minutes in a Pareto view |
| Reliability measures | MTTR and MTBF not tracked at all | Calculated per machine and trended over time |
Built to fit the machines you already run
You do not need new equipment to start tracking downtime. Brilliqs reads stop signals from modern controllers and from older machines that only offer a basic run signal, and fills any gap with a light operator input so no line is left out.
We bring the stop data together, clean it, and turn it into the reason codes and rankings your team acts on. Because our background is data engineering, the record behind the system stays accurate as you add machines, lines, and new reason codes over time.
The stop nobody knew was there
A short example of how a hidden cause surfaces once every stop is captured and ranked.
Picture a filling line that everyone blamed for slow output. The team pointed at the old pump and planned to replace it. Before spending the money, they turned on downtime capture and let it run for two weeks with reasons tagged at the machine.
The Pareto told a different story. The pump was not the top cause at all. A jam at the labeler was stopping the line for under a minute, dozens of times a shift. Each stop was too short to log by hand, so nobody had ever counted them, yet together they were the single largest loss on the line. The fix was a small guide bracket, not a new pump.
Once the bracket was fitted, the labeler stops fell away and the lost time on the Pareto dropped with them. Nothing else about the line changed. What changed is that the team could finally see the stop that was really costing them, and prove it was gone.
Why plants track downtime with Brilliqs
Plenty of tools count stops. We build a downtime system that fits your plant and keeps giving you answers you can act on.
Codes built around your floor
We shape reason codes to your machines and your language, so the reports read the way your team already thinks.
Serious data engineering
Our roots are in data pipelines, so the record of every stop stays accurate and stable as your plant grows.
Vendor neutral
We connect to the machines and systems you own and stay independent of any single equipment brand.
Support that stays
We train your crews and keep refining codes, thresholds, and reports long after launch so the system keeps earning its place.
Made for the people who chase lost time
A downtime monitoring system gives each role the view they need to cut stoppages and lift reliability.
Maintenance Managers
See which machines stop most, track MTTR and MTBF, and turn a reactive fix list into planned work.
Production Managers
Watch stops as they happen, know why a line is down, and keep the shift moving toward its plan.
Plant Directors
Hold every line to the same honest downtime record and back reliability spending with real lost time figures.
Continuous Improvement Managers
Work from a ranked Pareto to pick the loss worth removing next and prove the saving once it is done.
Reliability Engineers
Use MTBF and failure patterns to time maintenance and design out the stops that keep coming back.
Industrial Engineering Managers
Separate changeover and micro stops from breakdowns to target the right kind of loss on each line.
Machine downtime monitoring questions
Straight answers to what maintenance and production teams ask before they start.
What is machine downtime monitoring?
It is the practice of automatically detecting every time a machine or line stops, recording how long each stop lasts, and tagging it with a reason. Instead of relying on handwritten logs, your team gets a complete, honest record of lost time sorted by cause, so the biggest losses become easy to see and fix.
What is the difference between planned and unplanned downtime?
Planned downtime is time you schedule, such as changeovers, breaks, and maintenance you decided to do. Unplanned downtime is when a machine stops on its own, from a breakdown, jam, or fault. The system keeps the two apart so a scheduling issue does not get confused with a reliability one, and each can be measured and reduced on its own terms.
How does the system capture the reason for a stop?
When a line goes down, the stop opens on screen and the crew picks a reason from codes we build around your plant. The prompt comes while the stop is still happening, so the reason is accurate rather than guessed at the end of the shift. Free text is there for anything the codes do not cover.
What are MTTR and MTBF and why do they matter?
Mean time to repair is the average time a machine stays down once it fails, so it shows how quickly you recover. Mean time between failures is the average run time between one failure and the next, so it shows how reliable a machine is. Tracking both per machine tells you where to speed up repairs and where to design out the failures.
Does it capture short micro stops?
Yes, and this is often where the biggest hidden loss lives. Short stops of a minute or two rarely reach a manual log, yet across a shift they can add up to more lost time than the big breakdowns. The system counts them properly so you can finally see and act on them.
Do we need new machines to use it?
No. Brilliqs reads stop signals from the equipment you already run, from modern PLCs to older machines with a basic run signal. Where a line cannot report itself, we add a simple operator input so every stop still gets captured and tagged.
How is this different from an OEE dashboard?
An OEE dashboard rolls availability, performance, and quality into one score. A downtime monitoring system goes deep on the availability side alone, capturing every stop and its reason so you can act on specific causes. The two work well together, and the clean downtime record here feeds a more trustworthy OEE number.
Related downtime and reliability tools
Explore the rest of the Brilliqs manufacturing suite and the services behind it.
OEE dashboard
Roll availability, performance, and quality into one clear score built on trustworthy downtime data.
Machine performance dashboard
Track how each machine runs against its potential and spot the ones dragging your output.
Production monitoring dashboard
See live output by line and shift so you can act on a slowdown while the shift is still running.
Data analytics services
See how we turn raw operational data into reporting and insight across the wider business.
See where your lost time really goes
Book a short demo and we will show you a downtime monitoring system set up around one of your own machines, with your reasons and your Pareto.