AI for Manufacturing
On the factory floor AI watches three things: process parameters, machine condition and product quality. Predictive models call a failure a week ahead, and vision systems catch defects that sampling inspection never sees. Plants that have deployed them cut unplanned downtime by 45% and maintenance costs by 25%. The price of entry is instrumentation: without sensors and a history from them the model has nothing to learn on, so the first phase of a project like this usually has nothing to do with AI.
Three uses of AI in Manufacturing
Predictive maintenance
IoT sensors collect vibration, temperature and acoustic data from machines, and an ML model reads the wear patterns and calls a failure two to four weeks out. It builds the preventive maintenance schedule highest-risk-first, and parts are ordered against that schedule.
Quality control with computer vision
A deep-learning vision system inspects products on the line and catches defects invisible to the eye: micro-cracks, dimensional drift, surface faults. A camera and a model replace manual inspection at 99.5% accuracy and 100+ units a minute.
Process parameter optimisation
A reinforcement-learning algorithm sets machine parameters - temperature, pressure, speed, cycle time - live, so that quality and throughput rise on the lowest practical energy and material use. Every production cycle is more data, so the settings improve on their own.
Recommended stack
Return on investment
30 h
Hours saved weekly
€25
Hourly rate
€37,000
Annual saving
The maths: 30 h/week × €25/h × 48 weeks = €37,000 a year
Figures are quoted in euro, converted from Polish złoty at a fixed rate of 4.30 PLN to 1 EUR and rounded. Contracts are settled in either currency.
What makes it hard
A mixed machine park - different makers, different decades, different protocols (OPC-UA, Modbus, PROFINET)
No network on the shop floor - many plants lack the WiFi or LAN to carry IoT traffic
OT security - putting machines on the network creates new attack surface and demands IT/OT segmentation
Management reluctance to invest in digitisation, which makes a pilot with measurable return the way in
Frequently asked questions
Can our older machines be connected?
Yes - even machines twenty years old can be instrumented with external IoT sensors for vibration, temperature and current. Sensoring one machine costs €450-1,900. It touches neither the machine's control system nor its warranty.
What does predictive maintenance cost?
A pilot on five to ten critical machines is €9,500-18,500 including sensors, platform and model. Full deployment across 50+ machines: €35,000-93,000. The system pays for itself after two or three unplanned stoppages avoided, typically within three to six months.
What data does it need?
As a minimum, sensor data from the point of installation plus historical failure and service records. More history means better models - six to twelve months is ideal. With no digital data at all, we start by fitting sensors and collecting a baseline over two to three months.
AI in Manufacturing
Tell us what gets done by hand at your company, and how often. Within 24 hours you get back where to start and how long it takes.
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AI readiness audit
Ten questions about processes, data and people. The result appears on screen.