Continuous-production plants
Factories where a stopped line stops the whole chain. In these plants, a single machine failure creates losses far beyond its own job, and prioritisation is set accordingly.
CUSTOM SOFTWARE · PREDICTIVE MAINTENANCE
Predictive Maintenance: Turning Downtime into Planned Downtime
We collect a machine's vibration, temperature, current and cycle data, learn its normal operating signature, and catch deviations from that signature before failure occurs. An alert becomes a maintenance work order; the machine's history and the parts used accumulate in the same record.
In a factory, the cost of downtime is almost always greater than the cost of maintenance. But the real difference lies not in the total duration — it lies in when the downtime happens. In a planned stop, the part is ready, the crew is scheduled and the production plan has been shifted accordingly. In an unplanned stop, the line halts, the part is hunted for, supply sometimes takes days, and delivery dates slip in the meantime. The difference between the two versions of the same failure is often large enough to decide a product's profitability.
Maintenance's classic answer is periodic maintenance: predefined tasks carried out at set intervals or operating hours. This reduces unplanned stops, but it wastes in both directions. A machine that does not yet need attention is taken down for service, and some parts are replaced before their life is spent; meanwhile a machine whose schedule has not yet come round fails anyway. Because the calendar measures elapsed time, not the machine's actual condition.
Predictive maintenance shifts the decision from time to condition. Measurements such as vibration, temperature, current, pressure and cycle time are collected continuously from the machine; the system learns its normal operating signature. When a bearing's sound changes, when motor current starts creeping up on the same job, or when cycle time quietly lengthens, the deviation is caught and maintenance is planned before the failure occurs. The value here is not that it gives a precise failure date; it is that it says early that the machine's condition is deteriorating.
The condition for setting this up honestly is keeping expectations in the right place. The system does not foresee every failure, and we do not promise that; some failures develop suddenly and without warning. There will also be false alarms in the early period, because what counts as normal has not yet been learned well enough. Weeding those out is part of the job. On the other hand, once the measurement infrastructure is in place, simply recording machine history and downtime causes produces value on its own — before a single prediction has been made.
Who is it for?
Who is Predictive Maintenance a good fit for?
Operators of critical, one-of-a-kind machines
Businesses with equipment that has no backup and whose failure halts the workflow. This is where predictive maintenance yields its highest return; you start with these machines, not the whole fleet.
Factories with small maintenance teams
Plants running a large machine fleet with a handful of technicians. For these teams, the real gain is that the work gets queued and they know where to go first.
Businesses whose maintenance knowledge lives in one person
Factories where only the old master knows the machine. An unrecorded maintenance history disappears completely when that person leaves; the system's first concrete benefit is moving that knowledge into the company.
What we build
What we deliver within Predictive Maintenance
Field data collection
Vibration, temperature, current, pressure and cycle data are collected from the machine. Where existing control systems and counters can be used, they are used first; additional sensors are recommended only where genuinely needed. Fitting a sensor to every machine is a common but expensive mistake.
Learning the normal operating signature
The system learns how the machine behaves across different products and speeds; normal ranges are set separately for each operating condition. Instead of a single threshold, a reference that shifts with the operating regime is used — otherwise the alarm goes off at every product change.
Deviation detection and early warning
When a deviation from normal is caught, an alert is generated — and it arrives with a recommendation stating who should do what. Alerts are graded by severity, so the team does not have to treat every notification as equally urgent.
Maintenance work orders and job tracking
An alert becomes a maintenance work order; the order is assigned to a responsible person, and the work done and time spent are recorded. Planned maintenance runs through the same flow, so unplanned and planned work end up in one place.
Spare parts and stock linkage
Spare parts lists are kept against each machine; parts used in maintenance are deducted from stock, and minimum levels are monitored for critical parts. The value of an early warning materialises when the part can be procured in time.
Machine history and downtime analysis
Every machine accumulates its history of failures, maintenance, parts and downtime. Reports show which machine stopped for how long, which cause keeps recurring and which equipment is a constant troublemaker. In most plants, this report is the first thing to put investment decisions on a data footing.
Technologies
The technologies we work with
- Vibration and temperature measurement
- Modbus and industrial communication
- PLC and SCADA data reading
- Time-series data storage
- Deviation and anomaly detection
- Maintenance work orders and workflow
- Spare parts stock integration
- Mobile field notifications
- Downtime and failure reporting
Process
How we move from discovery to go-live
- 01
1. Selecting critical equipment
We start with the equipment that causes the most damage when it stops — not the whole machine fleet. Selection is based on downtime history and impact on production. Keeping the scope narrow is the fastest route to results.
- 02
2. Building the measurement infrastructure
We determine which data can be taken from existing systems and which needs additional measurement. Data collection begins. No predictions are made in this period; the goal is reliable, uninterrupted data.
- 03
3. Reference period and labelling
The machine's normal operation is observed for a period. Downtime and failures during this period are labelled with their causes; this is how the maintenance team's knowledge enters the system. Analysis without labelling is analysis that does not know the shop floor.
- 04
4. Activating and tuning the alerts
Deviation alerts are switched on and tuned throughout the early period. False alarms are examined one by one and thresholds corrected. This tuning period is a natural part of the job; skip it and the team stops trusting the alerts — and the system effectively dies.
- 05
5. Connecting maintenance processes and handover
The work order flow, spare parts linkage and reports go live. The system is handed over to your team, with written guidance on what to do for each alert and how to update the thresholds.
Frequently asked questions
Common questions about Predictive Maintenance
Will it foresee every failure?
No, and we do not promise that. Gradually developing problems such as wear and imbalance can be caught early; sudden breakages and externally caused failures usually cannot. The system's real contribution is that gradual deterioration is seen early and maintenance stops being unplanned. We would advise caution towards any proposal promising more than that.
Our machines are old — can data be collected?
In most cases, yes. Where an old machine's control system provides no data, monitoring can be done with externally mounted vibration and temperature measurement, or via motor current. We prefer methods that work without touching the machine itself — which matters especially for equipment still under warranty.
How many machines do we need to fit sensors to?
Not all of them at the start. You begin with the critical equipment and widen the scope as results come in. The most common mistake in practice is a broad sensor investment followed by data nobody uses. Keeping the investment staged reduces both the risk and the cost.
We have a production tracking system — will this clash with it?
It does not clash; it builds on top. The production tracking system knows what was produced and where stops happened; predictive maintenance monitors the machine's condition. When the two are connected, downtime causes and machine condition are read together and the analysis gets stronger. If you already have a system, we do not rewrite it — we connect to it.
Won't false alarms cost the team's trust?
That is a real risk, and it is exactly why we plan the tuning period as part of the job. In the early period alerts run as information only, and the team is not pushed to act; once the thresholds have settled, action is expected. Alerts are also graded by severity, so not every notification arrives with the same urgency.
What do we end up with?
Continuous condition monitoring on critical equipment; normal ranges defined per operating regime with deviation alerts; maintenance work orders and job tracking; spare parts lists tied to each machine; machine history plus downtime and failure reports. Everything — including the source code and the accumulated machine data — belongs to you.
Contact
Let us talk about your Predictive Maintenance project
In a 30-minute discovery call we listen to what you need and tell you honestly whether custom development or an off-the-shelf product is the better answer.
Related
Related pages and guides
IoT and Embedded Systems Production Tracking and MES Energy Management and Alerts Factory Software Solutions Let's Talk About Your Critical Machines After delivery: maintenance, support and capacity subscription
Computer Vision Quality Control
We build camera-based visual inspection on the production line: surface defects, colour and shade variation, missing parts, label verification and dimensional checks. The work does not begin as a software installation — it begins with feasibility and sample testing.
Details →Demand Forecasting and Production Planning Intelligence
We generate demand forecasts from order history, seasonality and stock data, and link that forecast to your production and purchasing plans. We sequence work under capacity constraints, calculate safety stock and measure how accurate the forecast actually was.
Details →Timekeeping and Incentive Management
We build a system that collects labour data from the shift plan to the time-and-attendance terminal, from overtime to piece-rate incentives, and prepares the file that goes to payroll matched against production records. Who has earned what is visible clearly enough that objections become unnecessary.
Details →Energy Management and Mandatory Reporting
We build a system that collects meter and field data and allocates energy to lines, machines and products. The basis for your annual declaration is ready, and a product's true energy cost stops being an estimate.
Details →Custom CRM Software
Off-the-shelf CRM packages do not recognise the real sales flow of industry and export. We develop a custom CRM that talks to your ERP, modelling the long sales cycle, the sample-proforma-order chain, dealer management and quotation tracking around your own process.
Details →Payments & Fintech Integration
We develop custom payment software that unites virtual POS, digital wallet, reconciliation and invoicing flows in a single secure system, built on iyzico, PayTR and bank POS infrastructures.
Details →