Data monetization in CNC manufacturing
CNC manufacturing is undergoing rapid technological development, driven by process integration, automation, digital transformation and the fascinating prospects of artificial intelligence. In this dynamic environment, data is becoming a strategic asset. But while machines, processes and sensors generate huge amounts of information, too often much of this valuable data remains unused. The crucial question is therefore no longer whether data from machining product creation can be systematically recorded, processed and transferred into sustainable value creation routines, but how.
From data to internal added value
Valuable data is generated at every stage of the value chain - from ERP and MES systems to CAD/CAM parameters and real-time information from machine control and quality control systems. This data not only helps to optimize manufacturing processes, but also offers potential for monetization, both internally for process improvement and externally through data-driven services and partnerships.
The first stage of monetization lies in internal company use. Companies that systematically evaluate their machine data benefit from fewer downtimes, optimized processes and more efficient use of resources. One example is predictive maintenance. AI-supported analyses make it possible to detect signs of wear at an early stage so that maintenance intervals can be adjusted in a targeted manner. This maximizes machine availability, reduces unproductive downtime and prevents expensive breakdowns.
Further potential lies in the dynamic optimization of machining parameters. Instead of working with static values for feed rate or spindle speed, real-time data enables continuous adjustment. This results in shorter cycle times, reduced energy consumption and fewer rejects. Data-based approaches also offer considerable advantages in quality assurance. Sensor technology and image processing make it possible to identify errors during the production process, minimizing rejects and increasing product quality.
External monetization: data as a business model
In addition to internal process optimization, the marketing of data opens up new business models. For example, digital twins make it possible to map production processes in virtual simulations and test optimizations before actual production. In addition, new cooperation models are emerging as a result of increasing networking in the industry. Machine manufacturers and tool suppliers could use real-time data to offer automated reorders or targeted product improvements, among other things. Anonymized production data could also be made available for benchmarking analyses or research purposes.
Data as a driver for innovation and sustainability
Machine data is not only a basis for increasing efficiency, but also a valuable source of technological innovation. One important approach is adaptive machine control. Instead of using fixed parameters for machining processes, algorithms could in the future suggest optimized settings based on real application data. This would allow efficiency and quality to be continuously improved.
New opportunities are also opening up in software development. Machine manufacturers can develop new features or adapt updates to real production conditions in collaboration with their customers by analyzing usage data. Benchmarking analyses also offer valuable benefits. By comparing anonymized data from different companies, manufacturing companies can identify how efficient their processes are compared to the industry and derive targeted improvements.
In addition to process optimization, sustainability aspects are also playing an increasingly important role in the industry. Customers and regulatory requirements are increasingly demanding transparency regarding the carbon footprint of components and processes. Intelligent control systems such as CELOS X can precisely document energy consumption in this regard and also identify optimization potential.
Challenges and success factors
Despite its diverse potential, the monetization of data requires a strategic approach. Data protection and data sovereignty are essential issues that need to be clearly regulated. Companies must define who is allowed to access which data and what information is shared. Another challenge lies in standardization and interoperability. Data must have open interfaces and compatible formats if it is to be used economically. It is also crucial to increase the acceptance of data-based business models. Transparency and convincing benefits are crucial for users to be willing to share their production data.
Manufacturing companies are therefore well advised to put data on their agenda as the key to the next stage of value creation. The digital transformation already provides the technological basis for this. The added value for production is just waiting to be exploited.
ADDED VALUE FOR MANUFACTURING
The Digital Transformation is already laying the technological foundations today, to open up various added values in the CNC manufacturing process chain in the future.
- Increased Efficiency in Production
• Optimization of machining parameters in real-time
• Reduction of cycle times and setup times
• Minimization of machine downtime
- Cost Reduction through Data-Driven Decisions
• Lower material consumption through more precise machining
• Reduction of scrap through early error detection
• Savings on maintenance costs through predictive maintenance
- Improved Quality Assurance
• Automated quality control using sensors and AI
• Seamless traceability of components and processes
• Reduction of complaints and rework
- Optimized Maintenance and Spare Parts Strategy
• Early detection of wear for proactive maintenance
• Targeted planning of spare parts orders
• Avoidance of unexpected production interruptions
- Sustainability and CO₂ Reduction
• Transparency on energy consumption and emissions
• Reduction of ecological footprint through data-driven optimization
• Compliance with regulatory requirements (e.g., CO₂ reporting for customers)
- Better Planning and Increased Flexibility
• Simulation and optimization of production processes using digital twins
• Adaptation of manufacturing to changing market demands
• Faster response to bottlenecks and supply chain disruptions