Digitalization lays the foundation, but it’s the optimization phase that follows which delivers long-term compounding returns.
Once the digital infrastructure is in place, industrial AI can create a loop of continuous optimization that incrementally improves performance, creating an ongoing cycle of analysis, adjustment and refinement.
Here, I highlight five areas where industrial AI is already delivering measurable, sustained optimisation for manufacturers.
1. Energy efficiency
Energy costs represent one of manufacturers’ biggest expenses, but industrial AI transforms how organisations manage consumption.
At Kellanova‘s Pringles factory in Poland, an AI-enabled energy management system reduced consumption by 7%, a significant achievement in the energy-intensive production of snack foods.
The system continuously monitors energy use across production processes, identifying inefficiencies and automatically making adjustments to minimise waste.
Building X, the Siemens platform for smart building operations, demonstrates similar principles. Users are achieving energy savings up to 30% through data-driven optimisation, while the platform’s Comfort AI application delivers an average 6.4% monthly energy cost savings by automatically optimising HVAC operations.
The system constantly analyses consumption patterns, weather conditions, occupancy levels and production schedules to find new efficiencies.
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Siemens is a technology company focused on industry, infrastructure, transport, and healthcare. From more resource-efficient factories, resilient supply chains, and smarter buildings and grids, to cleaner and more comfortable transportation as well as advanced healthcare, the company creates technology with purpose adding real value for customers. By combining the real and the digital worlds, Siemens empowers its customers to transform their industries and markets, helping them to transform the everyday for billions of people.
