The possible industrial applications of artificial intelligence (AI) are manifold. There are AI systems that perform quality inspection more reliably than humans as well as systems that support engineers in the design phase. Thus, will entire factories soon be managed by AI? Practical examples demonstrate what AI systems can already do, where they are already applied and what limits prevail. Altogether, the new role of production managers will be projected.
Timely maintenance of railway tracks is crucial for resilience, comfort and safety in rail traffic. It increases the acceptance of rail transport and its sustainability.
incontext.technology developed a smart monitor for rails which identifies and categorises anomalies on railway tracks by analysing sensor data based on daily train operations. Connected to the operator’s business processes, the resulting digital twin of the tracks keeps the maintenance teams up to date about the rail conditions and their expected evolution.
Through the expansion of renewable energies and the market ramp-up of electromobility, the energy transition is taking place especially in the distribution grid. As a result, power generation is increasingly shifting to medium and low voltage. This dynamic is accompanied by new challenges for distribution system operators: they have to deal with bidirectional power flows and enormous peak loads – and all this within the huge distribution grid area. This is only possible with innovative approaches such as state estimation and thus the inclusion of artificial intelligence!
AI allows retailers to translate their business strategies in decisions at scale with high degree of automation. As grocery retailers experience intensifying competition, they need to manage pricing processes for expiring products to balance profitability and waste. This talk will discuss in detail how AI enables the full utilization of intraday POS data and Real-Time decision making to manage markdowns effectively. Markdown pricing directly contributes to sustainability goals in helping to minimise and control waste based on the retailer’s desired business strategy.
Looking at artificial intelligence as hyped technology no longer seems to be appropriate – it has already penetrated most sectors as a key technology. Especially in the energy sector the high efficiency potential of AI has been very well received – much faster than expected. In dena’s new AI-analysis the most promising fields of application for AI in the energy industry are classified and an initial assessment is carried out to examine the opportunities and challenges.
There are a growing number of Computer Vision (CV) use cases for increasing efficiency and cost savings of organisations. Prior the Edge AI CV revolution, cameras were used by agents for limited visual monitoring actions. Most of the time, agents could at best see an incident at it was occurring or investigate after it happened. With the implementation of Edge AI CV, a single agent can monitor multiple sites remotely and leverage camera infrastructure and AI to prevent incidents and take proactive decisions. This result in significant improvement in business process and reduction in product loss for an increase in ROI of organisations.
Decision tools used in precision agriculture and integrating AI are designed using useful digital agricultural data. These data come from the many connected objects in place in farms: weather sensors, drones, electronic loops, etc. By relying on reliable data, exchanged with the consent of farmers, AI can make a major contribution to the sustainability of the agricultural sector.
To tackle the complexity of sheet metal production AI solutions can help make this manageable. The overall efficiency of laser cutting of sheet metal can be greatly improved by new tools to minimize waste an increase speed in production. Learn how a AI solution utilizes computer vision to support the worker on the shop floor.
Innovation & Digital Ecosystems
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