When a compressor stops without warning, the loss isn’t limited to that line’s output. Restarting it briefly spikes energy draw, the share of parts failing quality control rises, and the maintenance team gets pulled into an unplanned intervention. If the same failure had been visible weeks earlier, both the downtime and the wasted energy could largely have been avoided. That’s what predictive maintenance promises: scheduling a repair when the data says so, not when the calendar does.
This article covers how predictive maintenance works, why it’s not just a maintenance topic but a twin transition one, and where a company can realistically start.
How it differs from classic maintenance
Manufacturing maintenance usually sits at one of two extremes: reactive maintenance, which responds after a failure occurs, or scheduled (preventive) maintenance, which replaces parts on a fixed calendar. Both carry a cost — reactive maintenance means unplanned downtime and rush procurement; scheduled maintenance often means replacing a part that’s still working fine, which is unnecessary spend.
Predictive maintenance offers a third path: it reads the machine’s actual condition from vibration, temperature, current, or pressure data and estimates when a failure is approaching. Parts get replaced when they’re actually needed — not too early, not too late.
| Approach | When action is taken | Typical outcome |
|---|---|---|
| Reactive | After the failure occurs | Unplanned downtime, rush procurement cost |
| Scheduled | On a fixed calendar | Sometimes early replacement, sometimes late failure |
| Predictive | When data shows failure approaching | Planned intervention, less downtime and waste |
How it works: from the shop floor to data
Predictive maintenance starts with continuous data collection from machines on the floor. This usually begins with machine-to-machine (M2M) and IoT sensors: vibration sensors, current meters, thermal cameras, or parameters already readable from an existing PLC. Data flows through a gateway into a cloud platform or a local analysis server.
The real value doesn’t come from the raw reading — it comes from its deviation over time. If a motor’s normal vibration range is known, a reading outside that range is an early warning signal. Simple setups can work with threshold breaches; more mature setups train machine-learning models on historical failure data for sharper predictions. Which method fits depends on how critical the line is and how much historical data already exists — there’s no single right answer for every plant.
Why the energy and emissions side matters
Predictive maintenance is usually discussed under “reducing downtime,” but its impact doesn’t stop there. A worn bearing, an unbalanced motor, or a clogged filter typically draws more power before it fails outright — the machine works harder to do the same job. Catching that deviation early prevents both the failure and the extra energy it was consuming.
That ties predictive maintenance directly to the green transition agenda: energy efficiency is a meaningful line item in Scope 1-2 emissions accounting, and it gives a manufacturer preparing a CBAM or CSRD disclosure a concrete answer to “how did you reduce energy consumption.” Predictive maintenance on its own isn’t an emissions-reduction project, but it delivers a measurable contribution on the production-efficiency-plus-energy-efficiency axis — and because it’s data-backed, that contribution is reportable.
Where it meets the ERP
If the signal from a sensor stays as a notification on a maintenance technician’s phone, its impact stays limited. The real difference shows up when that data connects to the planning system: when a failure risk is flagged, a maintenance work order opens automatically in the ERP, the required spare part gets reserved from stock or a procurement process starts, and production planning adjusts knowing that line will need a short stop.
That integration is the job of ERP and process automation. The goal isn’t to watch maintenance data on a separate dashboard — it’s to fold it into the planning and inventory flow the business already runs on. That turns predictive maintenance from an “IoT project” into an operational gain that production and finance teams both see.
What the numbers say
Predictive maintenance’s impact varies by company — line criticality, existing data maturity, and implementation quality all shape the outcome. Still, industry research gives a general direction: analyses published by McKinsey show predictive maintenance can cut total maintenance costs by roughly 18-25% and reduce unplanned downtime by up to 50%. On the energy side, some industry reports point to energy savings of around 10% from optimised operation.
Don’t apply these figures directly to your own plant — they vary by sector, line type, and starting maturity. But the direction is clear: a well-implemented predictive maintenance system creates a measurable difference on both cost and energy.
Where to start as an SME
Starting with the most critical line, not a plant-wide rollout, is the realistic path:
- Pick the line — Start with the equipment where downtime is most expensive or failures most frequent; don’t try to cover the whole plant at once.
- Inventory existing data — What parameters can already be read from the PLC, what sensors already exist, what failure records are on file? You may not need to start from zero.
- Start with thresholds — Demonstrate value with simple deviation alerts before moving to a complex model; add deeper analysis once the team trusts the signal.
- Plan the ERP connection — Generating an alert isn’t enough; the benefit only shows up once it drives work orders, stock, and planning automatically.
- Measure the outcome — Compare downtime, maintenance cost, and where possible energy use before and after; this data is useful both internally and in support or incentive applications.
İkiz Eksen’s approach
For predictive maintenance rollouts, İkiz Eksen measures the data on the shop floor, connects it to the planning system through ERP and process automation, and carries the resulting energy and efficiency gains into green transition reporting. This is a concrete example of the twin transition approach: the same data serves both an operational decision and a sustainability report.
The setup runs on Microsoft Azure infrastructure; on the ERP side, it draws on Qera’s track record of 100+ integration projects, delivered by a team of roughly 35 specialists that has worked with 550+ customers across 15+ sectors. Projects are delivered turnkey across Türkiye — from sensor installation on the floor to ERP integration, with a single solution partner.
We can work out where to start together, based on your existing data infrastructure. Take a look at our solutions or get in touch — let’s talk through your situation.
Frequently Asked Questions
Do I need to buy new sensors for predictive maintenance?
Not necessarily. Most production lines already have parameters readable from the PLC. Start by inventorying existing data; adding targeted sensors for the gaps usually starts faster and cheaper than instrumenting the whole line at once.
What size of company is predictive maintenance suitable for?
Focusing on the single most critical line makes a meaningful start possible for small and mid-sized manufacturers too. At larger scale, the difference is being able to connect multiple lines to the same system and manage the data centrally.
Can predictive maintenance data be used in CBAM or CSRD reporting?
Not as a direct emissions calculation, but it can serve as supporting evidence of improved energy consumption. For manufacturers wanting to show the impact of energy-efficiency investments in Scope 1-2 accounting, it’s useful supporting data.
Can predictive maintenance be set up without ERP integration?
It can, but the benefit stays limited. Generating an alert alone isn’t enough — the real value comes from the maintenance work order, spare-part stock, and production plan adjusting automatically in response to that alert.
How soon do results show up?
A pilot focused on the most critical line usually produces its first meaningful alerts within a few months. Improving the model’s accuracy and clearly seeing the energy/cost impact takes longer — typically around one production season.
Sources and related pages
- McKinsey & Company — industry analyses on predictive maintenance’s impact on maintenance cost and unplanned downtime
- Digital Transition
- Green Transition
- Twin Transition
- Methodology
- Glossary