IT consulting

AI in production: more efficient, higher quality, more sustainable

Production is facing major challenges: rising demands, increasing competition and the pressure to operate more sustainably. Artificial intelligence (AI) offers targeted approaches to make processes more efficient and create added value. With AI in production, companies can:

Increase quality through automated and precise inspection processes.
Minimize downtimes with predictive maintenance
Increase productivity through optimized planning and material flows
Reduce costs through optimized use of resources
Achieving sustainability goals

The AI use cases in production / manufacturing

Artificial intelligence is revolutionizing production processes by increasing efficiency and productivity. AI-supported solutions optimize production planning through precise forecasts, avoid bottlenecks and use resources efficiently. In production, AI helps to identify quality problems and reduce machine downtimes. These applications shorten throughput times, increase adherence to deadlines and sustainably improve process quality – decisive advantages for companies operating in a dynamic and highly competitive industry.

Quality inspection in production processes

Problem: Manual quality checks are time-consuming and lead to incorrect results. Solution: AI analyzes production data and images in real time to identify quality defects immediately. Benefits: Higher product quality, fewer rejects and faster inspection processes.

Reducing energy consumption in production

Problem: High energy consumption leads to rising costs and often misses sustainability targets. Solution: AI analyses energy data, identifies inefficient processes and suggests optimization measures. Benefits: Lower energy costs, reduced CO₂ emissions and more sustainable production processes.

Predictive maintenance

Problem: Unplanned machine downtime causes high costs and production losses. Solution: AI monitors machine data, detects anomalies at an early stage and suggests preventive maintenance measures. Benefits: Reduced downtimes, optimized maintenance intervals and longer machine service life.

Control material flow

Problem: Uncoordinated material movements lead to delays and inefficient processes. Solution: AI analyses movement and consumption data to optimize the material flow within the production facilities. Benefits: Smooth processes, lower warehousing costs and improved production capacity utilization.

Optimize production planning

Problem: Production plans are often rigid and do not react to short-term changes such as fluctuations in demand or machine breakdowns. Solution: AI integrates real-time data into planning and takes capacities, availability and priorities into account. Benefits: More efficient production processes, greater flexibility and reduced production costs.

Ifaa study

"Artificial intelligence in manufacturing companies"

The 2022 study examined the use of AI in manufacturing companies and identified opportunities, challenges and critical success factors. 459 participants were surveyed, mainly from the metal and electrical industry (59%), with a focus on manufacturing (27%). Almost half of the participants (44%) had no management responsibility.

Further use cases

Discover exciting use cases from different areas of the company and be inspired by how other teams find and implement innovative solutions. This exchange opens up new perspectives and creates valuable synergies for joint growth.

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