From conventional production to autonomous factories

Making performance measurable, uncovering efficiency potential and enabling well-founded decisions: production data are the foundation of these key figures. With a consistent data basis, you can integrate and analyze this data in real time, providing precise and up-to-date KPIs. This creates a continuous flow of data from the machine to management.

Context-related information, instructions and feedback in real time reduces errors, speeds up training and supports consistently high product quality. Production data flows directly back into the system, improving transparency and enabling fast reactions. Embedded in a data strategy and integrated via an industrial data platform, a continuous data cycle is created: Knowledge is made usable, processes are adaptive and man and machine are optimally linked - for agile, data-supported production.

A sophisticated data strategy enables the systematic collection, integration and use of your production data. By analyzing it professionally, bottlenecks, quality problems and inefficiencies can be identified at an early stage. This allows you systematically optimize your processes, reduce downtime and use resources more efficiently. Data-based decision-making increases transparency and supports continuous improvement processes in your production.

Incident management describes the structured process for responding to unplanned events or disruptions in your production. We support you in the transformation from an isolated, reactive task to an integral part of a continuously improving, data-driven production system. Reduce not only the direct costs of downtime, but also sustainably increase the efficiency, quality and resilience of your entire production.

Condition monitoring is a method for continuously monitoring the condition of your machines and systems. It enables you to detect wear or error patterns at an early stage. The analysis of sensor data and system information provides precise insights into the operating status. Benefit from more stable processes and data-supported decisions for optimized production.

Digital Twins model production processes virtually and enable real-time monitoring, simulation and optimization. Depending on the area of application, you can identify bottlenecks at an early stage, improve production processes and minimize downtimes. Together with you, we establish the necessary requirements for the use of this technology on the basis of a scalable data framework.

The central collection and evaluation of relevant information from production, logistics and procurement is crucial for modern supply chain management in order to gain a holistic view of material flows and bottlenecks. A data platform also helps with demand planning, warehouse optimization and seamless coordination with customers and suppliers in production.

Machine learning is an enabler for optimizing your processes, reducing costs and increasing quality. The basis for this? High-quality production data. With data analytics and data science, you can recognize patterns and generate valuable insights for real competitive advantages. Because whether algorithms, machine learning or AI: it's the optimization of your processes that counts.

In modern production environments, seamless traceability is crucial. Digital systems continuously record data along the entire production and delivery process. We help you to bundle this information centrally and network it intelligently so that the origin, processing steps and quality characteristics can be traced at all times. This creates transparency and trust and simplifies the handling of product recalls, audits and legal requirements.

Predictive maintenance uses data analysis to monitor the condition of your machines and create forecasts for your production. This allows you to avoid unplanned downtime and extend maintenance cycles. Algorithms enable you to precisely predict maintenance times based on your machine data and sensors. Targeted data enablement can increase data quality and availability of data.

Centralize and analyse production data, energy consumption and supplier information. Linking this data creates precise energy KPIs for each product or line. This allows you to identify peak loads, optimize energy-intensive processes and realize potential savings. Create transparency across all interfaces and make automated, data-based decisions for efficient energy management in production.

Structured asset management allows you to digitally map and manage your production facilities. Various entities such as machines, production lines and work sequences as well as master data and machine programs can be recorded. Although asset management itself does not create any direct value, it forms the basis for digital services within your production, such as loss analyses, reports and dashboards.