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"We optimize the entire production planning process, including all constraints."
Experte: André Nowak | 02/25/2025 | Teilen auf in
Matthias Banike, Member of the Executive Board at OMMM from Leverkusen, helps his clients take a holistic approach to supply chain planning, strengthening operations management with algorithms and AI.
Mr. Banike, at the INDUSTRIAL FUTURE DAY, you will present solutions that enable companies to make their production more digital, efficient, and sustainable. How do you approach this, and what are OMMM's key focus areas?
Matthias Banike: We help industrial companies increase the productivity of their plants, conserve resources, and enhance their overall competitiveness. With our AI-driven approach, we optimize supply chain planning, making production and logistics processes more precise, flexible, and resilient. Many of our major competitors focus on bottleneck resources and isolated areas of operations management. This results in partial solutions that require manual adjustments and integration—leading to errors and reduced efficiency.
In contrast, we take a holistic view of the entire value chain, including all constraints. This allows us to optimize planning across all production stages comprehensively. We primarily work in the process industry and batch production, with a strong focus on the food industry. This sector presents numerous restrictions and dependencies—from shelf life and hygiene regulations to batch processes, multi-stage production, and sequence-dependent setup times. The food industry, therefore, serves as a key benchmark for the effectiveness of our optimization solutions.
A new and rapidly growing market for us is the chemical industry. OMMM’s solutions are modular, covering demand planning, production planning, and business planning. Our supply chain cockpit consolidates all data streams and key performance indicators (KPIs), providing executives and department heads with real-time transparency over the entire production process—from current bottlenecks and setup times to lead times and deviations from previous periods. This enables our customers to make data-driven decisions at all levels.
How does the collaboration typically work? Could you share a project example with concrete results?
MB: Our solutions integrate seamlessly into existing ERP systems and utilize available data, for example, through interfaces with SAP R/3 and S/4 HANA. Additionally, we incorporate other data sources such as inventory, personnel, and machine data. Based on this, we optimize planning and feed the results, such as production sequences and material orders, back into the system.
The food manufacturer Zentis now uses our software in all its plants in Germany, the USA, and Poland. Optimized planning significantly and sustainably reduces setup times, cleaning costs, disposal costs, and penalty fees for delayed deliveries. We achieve outstanding results because we also factor in dynamic elements such as variable storage capacities and fluctuating raw material availability. For example, many competitors calculate setup times using a fixed value. In contrast, we consider the impact of production sequences on setup and lead times, allowing us to optimize them as well. Zentis analyzed the effects and found that in its main plant alone, our solution generates €1.2 million in hard savings per year.
What are the typical challenges companies need to overcome to comprehensively optimize their operations management?
MB: One of the key challenges is often data quality. Only with a solid data foundation can the full potential of AI-driven analysis and optimization be realized. Many companies still rely on outdated systems and capture their data in a largely unstructured way. In many cases, they also lack the personnel capacity to adapt their IT systems, or employees are tied up with other critical IT projects, such as cloud migration.
That’s why we support our clients in enhancing their data foundation and preparing their data effectively. By creating the necessary structural conditions and improving data quality to the required level, we enable companies to fully leverage the potential of AI. This is one of the most crucial levers for effective digitalization and securing strategic competitive advantages.
Please give us a brief preview of your presentation at INDUSTRIAL FUTURE DAY 2025. What can participants look forward to?
MB: We want to demonstrate that AI is a true game changer in the process industry and batch production. Using the widely prevalent yet often overlooked problem of Multilevel Capacitated Lot-Sizing (MLCLSP), we illustrate how AI can solve such challenges and optimize production processes. In a hands-on presentation, we will showcase concrete use cases and demonstrate our software in action.
