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Predictive maintenance Next

Predictive maintenance software that warns you before the line stops.

MIE is building predictive maintenance software for manufacturing plants on the machine record it already keeps. It will use that record to warn you when a part is likely to fail, to track your monthly production goal, and to tell you the moment downtime puts that goal at risk. It is the next major feature on the MIE roadmap.

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mie.thinklytixai.com/machines/BOILER-01
MIE machine page for Boiler 1 showing each tag in its normal range, an open alert and machine details
Why it matters

Most plants still find out when the machine stops.

Maintenance usually runs on a fixed calendar, or on nothing at all until something breaks. Both waste money. One replaces parts that were fine. The other pays for a stopped line, a rushed repair and a missed month.

Machine failure prediction works from the readings instead. If the data shows a part drifting away from how it normally behaves, the right person hears about it days before it fails. Read more in predictive vs preventive maintenance and how to reduce unplanned downtime.

Fixed-schedule maintenance
  1. Service every machine on the calendar
  2. Replace parts whether they are worn or not
  3. Failures between services still stop the line
  4. Nobody records what the check actually found
Run until it breaks
  1. The night shift notices a stop at handover
  2. An urgent repair, often without the part in stock
  3. Lost output that the month cannot win back
With MIE predictive maintenance
  1. An early sign from the machine's own readings
  2. A plain-language heads-up to the owner
  3. A check booked before the part fails
  4. The outcome recorded, so the next warning is sharper
How it works

Five steps, every time.

A warning is only useful if someone acts on it and the system learns from what they found.

  1. Early sign

    A reading starts drifting from its normal behaviour, well before it crosses a limit.

  2. Heads-up

    The owner gets a plain-language message on WhatsApp, Slack or email: "Please review Turbine 2 bearing. There is a chance it may fail in the next 1–2 weeks. Take a look once."

  3. Acknowledged

    Someone takes it and books a check, or snoozes it with a reason. Unanswered warnings climb the same escalation tiers as alerts.

  4. Fixed

    The outcome is recorded against the machine in three taps: fixed, scheduled or nothing found.

  5. Learns

    Every outcome sharpens the next warning for that machine and machines like it.

Machine failure prediction

Built on data you can trust.

A prediction is only as good as the readings behind it. MIE already collects, checks and stores every reading at client plants. Predictive maintenance adds one more step on top.

  • Collect. Readings leave each machine over HTTPS, MQTT, OPC-UA or Modbus through your gateway. No new sensors are needed.
  • Check. Only known machines and tags get in. Anything else goes to the rejected-data log with the reason, so bad data never skews a warning.
  • Learn normal. MIE learns the normal range and trend for each tag on each machine, from a few weeks of history.
  • Spot the drift. When a reading starts moving away from normal, well before it crosses a limit, MIE raises an early sign for that machine.
  • Keep learning. Every fix, schedule or "nothing found" is recorded, and accuracy improves as outcomes build up.
Production goals & downtime

Know the month is slipping while you can still save it.

Example. A plant plans 2,000 units a day towards a monthly goal of 55,000. With 10 working days left, MIE shows a gap of 22,000 units. It says the goal is at risk and that 2,200 units a day are now needed, 200 above plan. The owner can act while there is still time, not at month end.

  • Goal tracking. Set the plan (for example 2,000 units a day) and the monthly goal (55,000). MIE shows produced so far, projected month end and the gap.
  • Stop alerts. When a critical machine stops, the owner is told straight away, with the units being lost.
  • Goal at risk. When the projection falls short, MIE says so early, with how much output is needed per day from here.
  • Downtime reasons. Each stop can be tagged with a reason, so the month shows where output really went.
What you get

Where it shows up in MIE.

Likely next

A panel in every client summary: which machine, roughly when, how sure, and what to check first.

Machine health

A health view on each machine page with warnings, acknowledgements and fixes on one timeline.

Heads-up messages

Warnings on the channels your team already uses, with acknowledge in one tap.

Predictive maintenance sits alongside the parts of MIE that are live today: plant overview, alerts with timed escalation, Ask, trends and machine pages. You can also build your own views of health and downtime with custom dashboards.

What MIE connects to

Works with the machines and systems you already run.

MIE sits alongside your SCADA or historian. It does not replace them. Your control system keeps running the plant; MIE watches the data and tells people.

  • Machines and controllers. HTTPS, MQTT, OPC-UA and Modbus, from your PLC, gateway, historian or edge device.
  • Business systems. SAP, Oracle, Microsoft Dynamics, SQL Server, PostgreSQL and Excel. Our team handles the mapping during setup.
  • Out to your team. Heads-up messages on WhatsApp, Slack or email, and alerts on Slack, WhatsApp and webhook.
  • Your data stays yours. Encrypted in transit and at rest, isolated per client and never used to train AI models. See security.
Who it's for

For the people who answer for uptime.

Plant ownersFewer surprises in the month.

See which machine is likely to need attention, whether the production goal is on track, and what a stop is costing in units.

Maintenance leadsChecks booked before the failure.

Get early signs with the machine and part named, acknowledge in one tap, and record what the check found.

Operations managersDowntime you can explain.

Every stop tagged with a reason, so the month shows where output really went and which machine to fix first.

Machine builders and service teams will see the same "Likely next" panel in the client summary for each customer site. See MIE for OEMs.

Getting started

Start with MIE today. Predictions build on it.

Predictive maintenance needs a few weeks of readings for each machine. Plants that connect MIE now build that history while the feature is being finished.

  1. Listen

    Day 1. A call and a walk through your plant. We note the problems in your words.

  2. Map

    Week 1. Machines, tags, limits, shifts and goals. We agree what "good" looks like for your plant.

  3. Connect

    Week 2. We set up the gateway over HTTPS, MQTT, OPC-UA or Modbus. Readings start flowing and get checked.

  4. Prove it

    Weeks 3–4. First alerts reach the right people, first answers from Ask, first client summary. Then talk to us about joining the first group of plants for predictive maintenance.

Questions

Common questions.

Is predictive maintenance available today?
It is the next major feature on the MIE roadmap and builds on the data MIE already records at client plants. Talk to us about joining the first group of plants.
What is predictive maintenance software?
Predictive maintenance software watches machine readings, learns what normal looks like for each machine, and warns you when a part is drifting towards failure. In MIE the warning goes to the owner in plain language, and the outcome of each check is recorded so the next warning is sharper.
How is predictive maintenance different from preventive maintenance?
Preventive maintenance services machines on a fixed schedule, whether they need it or not. Predictive maintenance acts on the machine's own readings, so checks happen when a part shows early signs of trouble. Many plants use both.
How does MIE predict machine failure?
MIE learns the normal range and trend for each tag on each machine. When a reading starts drifting from that normal behaviour, well before it crosses a limit, MIE raises an early sign and tells the owner which machine, roughly when, how sure it is, and what to check first.
How much history does MIE need?
Early signs need a few weeks of readings for a machine. Accuracy improves as outcomes are recorded.
Do we need new sensors?
No. MIE works with the readings your machines already send over HTTPS, MQTT, OPC-UA or Modbus.
What happens if nobody responds to a warning?
Unanswered warnings climb the same escalation tiers as MIE alerts, for example shift lead first, then maintenance, then the plant manager. Escalation stops the moment someone acknowledges, and MIE records who took it.
Does predictive maintenance replace our SCADA or historian?
No. MIE sits alongside them. Your control system keeps running the plant; MIE watches the data, warns people and answers questions.
See it on a real plant

Ask the demo plant your own question.

We'll walk you through MIE on a live plant in 30 minutes.

Or call +91 98288 93692 · +91 88519 85656 · info@thinklytixai.com

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