Guest author: Bernd Lammert, Editor dpa-Custom Content
Artificial intelligence (AI) is becoming a key technology in Smart Factories, transforming how production is planned, monitored, and optimized. AI helps companies manage complex data streams in real time and make informed decisions. But what challenges lie ahead, and which practical application scenarios are generating real added value today?
The vision of a Smart Factory in 2026: Intelligent systems communicate independently with machines at production level, optimize their own design plans, and identify quality issues early in the production process. Artificial intelligence (AI) is not only changing customer-oriented areas, but is increasingly being integrated into industrial systems.
Challenges of AI
Some AI projects remain pilot projects, not because of inadequate technology, but because recommendations for action derived from complex algorithms are lacking in transparency and no one can explain them in a way that is accountable. This is known as the black box dilemma, which undermines trust, accountability, and security – without adequate explanations, causes of errors remain hidden, and recommendations are often ignored.
The solution: explainable predictive AI. Apart from providing accurate predictions, AI makes data, drivers, patterns, assumptions, and uncertainties transparent, integrates into workflows, and is audit and compliance capable. This allows teams to understand, review, and defend decisions, and pilot projects generate measurable business benefits.
Projects often fail due to unclear expectations. Companies expect precise AI results without first providing sufficient, high-quality data and transparent context. Furthermore, it is often unclear what constitutes a “correct result.” However, without sound and comprehensive expectation management, the success of AI can hardly be measured.
Fields of application for AI
The increasing prevalence of AI applications marks the transition from automated to truly intelligent factories, where data-driven decisions are made in real time and production systems dynamically adapt to changing requirements.
One of the most stable areas of application in the Smart Factory is decision-supporting AI. It analyzes complex relationships in real time and empowers planners and schedulers to make informed decisions.
Another key area of application is autonomous quality and process monitoring (Predictive Maintenance). Edge analytics directly at the machine enable real-time responses. AI detects anomalies earlier than traditional sets of rules and identifies causes across multiple process stages. The aim is to achieve higher throughput and equipment efficiency.
Other established areas of application:
- Computer vision in quality control: AI-based image processing detects the smallest defects, continuously learns new failure patterns, and makes context-dependent decisions. Sensor fusion (combining camera, temperature, force, and acoustic data) makes it possible to do 100 percent inspections instead of random sampling and early detection of quality deviations.
- Digital twins with AI: A virtual mapping of equipment/processes is enhanced with AI to simulate future developments. Improves predictions and process planning.
- Human-machine collaboration: Intuitive AI systems and cobots (collaborative robots) learn from human actions, adapt to individual working methods, and take on ergonomically stressful or dangerous tasks.
- Energy management: AI dynamically optimizes energy consumption, forecasts peak loads, and integrates renewable energy sources to reduce costs and emissions.
Current trends and use cases
How will the use of AI in industry develop in the future? And what effects can be expected in key areas such as production, logistics, and supply chain? Here is a summary of current trends and use cases:
- Edge AI: Artificial intelligence is increasingly processing data directly at the machine and production line where it is generated. Inference (i.e., running a pre-trained model to make predictions) is moving from the data center to the network edge. The result is reduced latency, more robust decision-making in the face of network disruptions, and stable real-time applications.
- Federated learning: Instead of centrally collecting sensitive raw data, sites train their models locally and only share model updates. Data sovereignty remains local, while all locations benefit from shared learning progress.
- AI agents in operations: Autonomous software agents take on specialized tasks such as production planning, capacity checks, or alternative sourcing. Several agents work in a coordinated manner, compare information, and generate recommendations for action that are ready for approval. Teams maintain control but accelerate their decisions and respond more resiliently to disruptions.
- AI as the core of software architecture: AI is evolving from an additional feature to a structural component. Data and model pipelines are adaptive, monitored, and tested automatically; MLOps processes (operation and further development of models) are part of the standard architecture. Systems continuously improve themselves instead of just executing rigidly programmed rules.
- Advances in computer vision: Hyperspectral Imaging (HSI, cameras with multiple wavelength channels) detects materials and contaminants that are barely visible in normal light. Vision transformers (a transformer architecture adapted for images) are increasingly running in the edge in a way that conserves resources. Combined with sensor fusion linking 2D/3D images, thermal, and HSI data, this results in more robust quality decisions, up to and including 100 percent inspection.
- Robotics with greater autonomy: Analytic AI recognizes patterns and predicts failures, generative AI finds innovative solutions and enables natural operation via voice or gestures, and the AI agent plans in a multi-step and goal-oriented manner. Put all this together, it makes robots, even humanoid ones, more independent. Reliable cycle times, low energy consumption, and manageable maintenance costs remain crucial for the breakthrough.
- Emerging paradigms: Vision-language-action models combine seeing, understanding, and acting. Robots interpret images, understand instructions, and derive appropriate actions. Neuromorphic hardware (brain-inspired chips, often with spiking neural networks) provides energy-efficient real-time processing directly at the sensor. Quantum and hybrid optimization combine quantum and classical computing power to solve complex planning and logistics problems faster. These strategies address growing demands for response speed and optimization quality in the shop floor.
The common denominator of these areas of application is their consistent focus on specific use cases with clear operational benefits.
Would you like to learn more about specific applications of AI in the Smart Factory?
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