Price-sensitive customers, fluctuating tariffs, and new competitors: Manufacturing companies are under constant pressure to optimize. With AI-powered Operational Intelligence, they unlock additional potential—in processes, planning, or quality. And when paired with human expertise, this leads to new levels of excellence. Elisa Industriq will present possible paths to achieving this at HANNOVER MESSE 2026.

Real-time data as the key to operational excellence

Until now, those wishing to measure their operational performance had to rely exclusively on historical data, static models, and random samples—often a guessing game. Now, however, it is possible to analyze massive amounts of data using AI—even in real time. Humans contribute context and judgment, which ideally allows decisions to be made faster and more efficiently than ever before. This combination opens up a whole range of options for greater quality and precision in manufacturing.

Dynamic Process Optimization

Traditional process optimization interprets the past. AI, on the other hand, learns from real-time data and dynamically adjusts parameters. For example, the algorithms optimize throughput, quality, and energy consumption in production based on current conditions. Combined with human expertise, they enable the fastest possible interventions and thus maximize yields. A clear advantage—not only in quality- and price-sensitive industries such as electronics or semiconductor manufacturing.

Improved Quality Assurance

Systems can already detect defects in the blink of an eye—even those that experienced inspectors miss. This applies both to in-house production and to incoming materials from suppliers. AI creates further value by additionally suggesting corrective actions. Human experts now only need to validate the relevance. The result: fewer errors, less rework, and consequently lower costs and higher customer satisfaction. The accompanying automatic documentation also facilitates compliance with regulatory requirements, whether for RoHS/REACH, the EU Ecodesign Regulation (ESPR), or the Battery Passport.

Resilient Supply Chain

To identify potential disruptions in a timely manner, AI analyzes not only data from the company’s own operations but also market trends, supplier performance, and geopolitical developments. Increased demand and delayed deliveries can thus be detected early enough. Based on this, AI-based tools suggest, for example, alternative suppliers or adjustments to production planning. However, the decision to switch to a new supplier or change the schedule remains with humans.

Data-Driven Action in Manufacturing, Logistics, and Maintenance

These examples show: Today, it is more important than ever to be able to react flexibly and set the right course in a timely manner. This is only possible if meaningful insights are derived from the vast amount of available data—in real time where necessary. Thanks to the capabilities AI offers today, it is not only possible to easily connect a wide variety of internal and external data sources. Smart algorithms also help to detect anomalies in the blink of an eye, consolidate the information gathered, and put it into context. Complemented by human experience, the operational intelligence gained in this way acts as an amplifier along the entire operational value chain. Employees can then make decisions about necessary adjustments based on more and more up-to-date information—thereby ensuring better results. For example, when the next maintenance interval is due or when a change in process temperature is required. Additionally, team leaders and specialists can use AI to check the relevance of their own proposed actions or validate them. All of this minimizes downtime and improves quality.

Elisa Industriq is presenting these and other approaches to operational intelligence this year in Hanover. On the one hand, the company will showcase camLine’s modular software solutions for managing high-tech manufacturing processes. On the other hand, Elisa Industriq will present sedApta’s solutions for demand-driven manufacturing.