L’Artificial Intelligence (AI) is a set of technologies that enable computer systems to mimic human cognitive abilities such as learning, problem-solving, perception and decision-making. In the industrial context, AI is the analytical engine of the digital transformation, transforming massive data into intelligent actions and decisions.
In-depth definition : Industrial AI uses sophisticated algorithms to analyze the flow of Big Data generated by sensors IoT (Internet of Things), systems MY and ERP, to identify patterns, make accurate predictions and automate complex decision-making processes that were previously the preserve of human expertise. Its aim is not to replace the human being, but to provide him or her with a ability to analyze and react essential for achieving the’Operational Excellence in increasingly complex and dynamic production environments.
Revolutionary Applications of AI in Industrial Performance
For a digital transformation consultant, the integration of AI is a major lever for creating value for its SME customers, particularly in the following areas:
A. Predictive Maintenance
- Role : This is the most profitable application of AI. The algorithms of Machine Learning continuously analyze equipment vibration, thermal and power consumption data. They are able to predict outage of a machine before it happens, by detecting minimal signal anomalies.
- Impact on Performance : Shift from preventive maintenance (time-based) to more effective predictive maintenance (based on actual state) eliminates unscheduled downtime, significantly increases TRS (Taux de Rendement Synthétique) and reduce massive corrective maintenance costs.
B. Process and parameter optimization (Process Optimization)
- Role : AI can optimize the parameters of a machine or production line (temperature, speed, pressure, dosage) in real time to guarantee maximum quality or the lowest energy cost. It can find the perfect balance point, often too complex for humans to determine.
- Impact on Performance : Reduced scrap, higher production speeds and lower energy consumption, contributing directly to an improvement in the operating margin.
C. Automated quality control (Machine Vision and AI)
- Role : AI is used in conjunction with Machine Vision for visual product inspection at very high speeds. Algorithms are trained to recognize defects (micro-cracks, assembly non-conformities) with accuracy and consistency far superior to that of the human eye.
- Impact on Performance : Guarantee of Quality constant for Series Production, improvement traceability and minimize costly product recalls.
D. Supply chain optimization
- Role : AI analyzes historical sales data, market trends, weather, and other external factors to produce demand forecasts much more precise. It can then optimize inventory levels and production orders (Pulled Flow or Flux Poussé to minimize overstocking and out-of-stock situations.
The Implementation Challenge and the Role of Industrial Engineering
The adoption of AI in industry is held back by two main challenges:
- Data Quality and Quantity: AI is only as good as the data it uses. The role of Industrial Engineering is to ensure that the collection systems (IoT) are reliable, and that the data is clean, structured and relevant to the learning models.
- Systemic Integration: AI solutions must not remain isolated tools. They need to be integrated with execution systems (MES, PLCs) so that AI decisions are translated into concrete physical actions on the line.
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