Which error or bottleneck matters?
A camera, forecast or agent is not a goal by itself. First define which decision should be better prepared and what a wrong signal would cost.
Manufacturing · Operations
AI for manufacturing, from use case to day-to-day operation.
Industrial AI does not start with a model. It starts with a clear task, representative data and a decision about how the result flows back into ERP, inventory systems or the existing workflow.
Where does AI make sense in manufacturing?
Where a repeated observation, forecast or handoff can be described clearly and checked later. Typical areas include visual inspection, quantity and barcode capture, demand forecasting and data-led planning. Each case needs its own input data and acceptance criteria.
Before the pilot
Four questions determine whether an idea can become a reliable system.
Is the data representative?
Images need real lighting, material and line conditions. Forecasts need time-series history, stable features and actual outcomes for comparison.
Where does the result go?
A finding must flag an exception, notify a person or feed into inventory management, ERP or the existing workflow. Otherwise it remains a demonstration.
How will operations be monitored?
Input conditions, failures, processing times and model quality can change. Thresholds, human review and a traceable way to adjust the system are therefore part of operations.
System components
An industrial use case is rarely one model alone.
The components remain distinct but work together in operation: capture, forecast, connect data and route exceptions.
Publicly supported project patterns
Two documented project examples, not broad promises.
For an industrial operation, SAP and Microsoft Dynamics were connected in one platform with machine-learning forecasts per item. For a logistics operation, cameras capture pallets and barcodes at the loading gate. The public material does not prove general defect detection, predictive maintenance, or accuracy and impact metrics.
The operating boundary
A pilot is useful only when its data and acceptance criteria represent later operations. Predictive maintenance, MES integration and fine defect classes each require separate validation.
Which production step is difficult to measure today?
We examine the task, data, interfaces and acceptance criteria first. The result is a clearly scoped next step, not a generic AI programme.