4 paths to AI-supported operational excellence

Be it due to more price-sensitive customers, fluctuating customs duties or completely new competitors: manufacturing companies must constantly adapt to new challenges. Until now, it was often a guessing game as to which measures would prove worthwhile. This is because traditional operational intelligence has so far relied exclusively on historical data and static models in addition to human expertise. AI, on the other hand, can create real-time analyses from huge amounts of data. But it is humans who provide the context. Their judgment ensures that the AI findings match the company's goals. This combination is known as AI-supported operational intelligence. The software company Elisa Industriq shows four ways in which AI takes operational intelligence in manufacturing to a new level.

1. dynamic process optimization

While traditional process optimization is based on static models, AI continuously learns from real-time data and dynamically adjusts process parameters. For example, AI algorithms integrated into production lines align targets in terms of throughput, quality or energy consumption directly with current conditions. Together with human expertise, this creates optimal conditions for maximum yields. A clear advantage - especially in industries such as semiconductor production, where even small deviations can significantly reduce yields.

2. improved quality assurance with relevance check

Systems can already detect errors in real time. AI creates added value by enabling automatic action to be taken once a defect has been detected. Advanced computer vision technologies even identify defects that an inspector might miss. The human experts ensure that the detected defects are actually relevant. This leads to significantly lower error rates, less rework, reduced costs and increased customer satisfaction.

3. resilience of the supply chain

AI tools analyze market trends, supplier performance and geopolitical developments in order to identify potential disruptions in good time. This allows higher demand and delayed deliveries to be recognized early enough. Based on these findings, the tools recommend specific additional suppliers and make suggestions as to how production planning can be adapted. Importantly, the final decision is made by people based on their experience and the recommendations of the AI.

4. data-supported decision-making

At its core, AI-driven operational intelligence functions as an amplifier. It does not replace human expertise, but enhances it. One example is predictive maintenance: AI solutions detect deviations in seconds or even milliseconds, such as temperature changes when processing workpieces, and recommend maintenance times. However, the final decision is made by the experienced specialist after assessing the machine's condition and process requirements. This minimizes downtimes and significantly reduces maintenance costs.

Achieving operational excellence in production with AI support

AI-supported virtual managers help to combine human intelligence with artificial intelligence: LUMI VM from Elisa Industriq, for example, supports planning managers as an intelligent decision-making partner. At the click of a button, Lumi analyses historical and current data, suggests optimizations and provides support with what-if scenarios.

„AI-supported operational excellence is created where artificial intelligence and human expertise work hand in hand. Together, they help to increase efficiency and innovation exponentially,“ says Michael Fatum, Managing Director of Elisa Industriq in Germany. „However, standard off-the-shelf AIs are not enough for this. In addition, customized AI solutions must be seamlessly integrated into the existing infrastructure. Only then can benefits - such as increased efficiency or innovative strength - really be realized.“

Source and further information: Elisa Industriq