Where Is AI Used in Manufacturing? A Realistic View

AI is a much-discussed topic in manufacturing, but applications that create value on the shop floor are concentrated in specific areas. What they share is plenty of well-organised data and a clearly defined problem.

Areas that work today

• Quality control with image processing: camera-based detection of defects that are hard to describe with rules, such as surface flaws, missing parts or label checks.
• Anomaly detection: spotting early deviations from normal in motor current, vibration or temperature data.
• Process optimisation: finding the settings that affect quality in multi-variable processes (e.g. temperature, pressure, speed).
• Demand and planning: forecasting from historical data in production planning.

Prerequisites

1. 1
   Data

   An AI model cannot be built without regular, time-stamped and correctly labelled data from the machines. In most projects this is the bulk of the work.

2. 2
   Solid automation foundation

   AI does not replace the PLC; its decisions are still carried out in the field by PLCs, drives and safety systems. If the foundation is not reliable, the AI layer will not be either.

3. 3
   Measurable goal

   A measurable goal such as "reduce scrap" or "reduce unplanned downtime" shows whether the project works.

In AI projects, ELKAR supports you from collecting machine data and preparing the automation foundation to developing the application and putting it into operation. We assess your needs and available data together to set a realistic starting point.