AI in manufacturing: where to start

Manufacturing has been sold AI promises for years. Most haven’t materialised. What does work today is smaller, cheaper, and aimed at problems you’ve already got: predictive maintenance, defect detection, the boring stuff.

Four places we see manufacturers getting real returns right now.

1. Predictive maintenance

Machines break. You fix them. Downtime costs more than the repair.

Sensors on critical equipment (vibration, temperature, power draw) feed a model that spots patterns before failure. Not predicting the exact day a bearing will fail. Flagging that something’s off and needs checking.

The manufacturers we work with typically see 30-50% less unplanned downtime. Maintenance shifts from reactive to planned, which means fewer emergency callouts and less overtime.

What you need: historical failure data (even informal logs help), sensors on 3-5 critical machines, and someone in maintenance who’ll act on the alerts.

2. Quality control

Defects caught late are expensive. Rework, returns, reputation damage.

Computer vision on the production line spots scratches, misalignments, incorrect assemblies. Faster and more consistently than human inspection. The system doesn’t get tired or distracted.

Defect escape rates usually drop by 60-80%. Inspectors stop doing routine checks and focus on edge cases the camera struggles with.

What you need: good lighting, a few hundred images of good and bad parts, and a line where stopping for inspection is already a bottleneck.

3. Demand forecasting

Too much stock ties up cash. Too little loses orders.

Most manufacturers use reorder rules based on industry averages or gut feel. A forecasting model uses your actual sales patterns (seasonality, promotions, supplier lead times) and adjusts accordingly.

Typical result: 15-25% inventory reduction without stockouts. Purchasing becomes proactive, not reactive.

What you need: two years of sales data, willingness to challenge existing reorder rules, and buy-in from whoever places orders.

4. Process optimisation

Your production line has bottlenecks you can’t see. Cycle times vary and nobody knows why.

Analysing production data (changeover durations, machine utilisation, throughput by shift) shows where time and capacity disappear. Often the fix is operational, not technical.

We’ve seen 10-20% throughput increases without new equipment. Sometimes it’s as simple as sequencing jobs differently.

What you need: data from your MES or ERP system, and someone who understands the line well enough to validate findings.

Where to start

Don’t do all four. Pick one where the problem costs real money, you have data (or can collect it in weeks, not months), and someone in-house will own it after we leave.

The diagnostic is designed to help you choose. Two weeks, fixed price, honest answer on what’s worth doing.

More on how we work with manufacturers on our manufacturing page, or talk to us about your factory.