AI in Manufacturing
Where it is genuinely working
Visual inspection
Defect detection on production lines. Mature, high-volume, and consistent in a way sustained human attention cannot be.
Predictive maintenance
Forecasting equipment failure from sensor data, reducing unplanned downtime. Requires historical failure records — the most common gap.
Demand and production planning
Forecasting that accounts for seasonality, lead times and capacity constraints.
Process optimisation
Yield improvement, energy efficiency, parameter tuning — often classical optimisation rather than machine learning, and we will say when that is the case.
Supply chain risk
Supplier reliability, lead-time variability, disruption anticipation.
Documentation and knowledge access
Maintenance manuals, standard procedures and technical specifications made queryable for floor staff.
Practical constraints
Sensor coverage and data historisation determine what is possible. Many plants log data they never retain long enough to model from.
Failure data is often scarce by definition — well-maintained equipment fails rarely, which is good operationally and difficult for modelling. This shapes approach and expectations.
Imaging conditions decide inspection feasibility. Lighting, angle and consistency matter more than model architecture, and improving capture is frequently cheaper than improving the model.
OT and IT integration is usually the practical bottleneck rather than the modelling.
Services that apply most
Inspection work is computer vision. Predictive maintenance and planning are machine learning development. Sensor and historian consolidation is AI data engineering. Floor-level knowledge access is RAG & knowledge systems. Systems integration across MES, ERP and quality platforms often needs ERP & CRM integration.
Frequently asked questions
Sometimes. Existing SCADA and historian data is often sufficient for a first project. The assessment tells you whether additional instrumentation is required before committing to it.
Enough examples of failure to learn from, which is the usual constraint. Where failures are rare, anomaly detection on normal operation is often the more realistic approach than failure prediction.
Frequently, and where it cannot, the fix is usually lighting or positioning rather than better cameras. We assess your actual images before quoting.
Yes, typically. Edge deployment is standard in manufacturing for latency and connectivity reasons, and modern edge hardware handles inspection workloads comfortably.
Legacy machines can often be instrumented externally with retrofit sensors or vision. Full replacement is rarely necessary to get value.
Wherever failure is most expensive — usually either a quality escape reaching customers, or unplanned downtime on a bottleneck asset.