📝 Overcoming Manual Limitations: 5 Steps to Automatic CNC Data Collection
Manual recording of production data (e.g., on paper or Excel) leads to many problems such as inaccuracy (human error), delays, and inability to analyze in real time. Therefore, moving to automation is essential to improve OEE.
Here are five key steps in transitioning from manual to automated CNC data collection:
Step 1: Outline Data & Goals
Before starting any system installation, the first thing to do is to have a clear understanding of the problem and goals.
Identify the problem: Where does manual recording cause the most loss (e.g., downtime, waste, speed)?
Set goals: What do you want to improve? (e.g., reduce downtime by 25%, increase OEE by 10%)
Define data: What data needs to be collected to measure performance against the target (e.g., machine run time, setup time, actual number of parts produced, Run/Idle/Stop operation status)?
Step 2: Choose the appropriate data capturing technology.
Selecting the correct tool that is compatible with your CNC machine is of utmost importance.
Compatibility: Make sure the chosen technology can communicate with the CNC machine controller (even legacy machines), perhaps using an IIoT Gateway or PLC.
Data collection methods: Consider using appropriate technology, such as:
Direct Connection: Pull data directly from the CNC Controller.
Sensor-Based: Install external sensors to measure operating status (e.g. status indicators, power consumption).
Protocol: Select a protocol that can translate data from machine language (Proprietary Protocols) to the analytics system (e.g. MQTT).
Step 3: Develop an implementation and operation plan.
The transition should not be a sudden one, it should start with a pilot project.
Choose a starting point: Select the most important (bottleneck) machine or production line, or the machine that the team is most prepared for piloting.
Installation Planning: Schedule the Hardware/Software installation and network connectivity.
System Integration: Plan for the new system to work seamlessly with existing systems (e.g. MES, ERP).
Step 4: Train Staff and Manage Change
Employee acceptance is the most important factor in transition.
Training: Educate technicians and CNC operators on the new system, including operation methods, data collection device maintenance, and dashboard usage.
Change Management: Communicate the benefits of automation (not control) to employees to reduce resistance and build trust in the data.
Testing and Tuning: Initial Testing is available and feedback is collected to improve data accuracy and display format.
Step 5: Monitor, Analyze, and Scale
Once the system is up and running, continued use of the acquired data is the key to success.
Monitoring: Closely monitor the stability and accuracy of automated data collection during the initial stages.
Analysis: Use the collected data (OEE, Downtime Reasons, Cycle Time) to identify trends and hidden issues (e.g., where the most waste occurs).
Scale: Once the pilot project is successful and the returns are clear, expand the installation to other machines and production lines throughout the plant.
Following these steps will enable factories to overcome the limitations of manual data and leverage real-time CNC data to drive business decisions and continuous improvement.
| Main process | Automated Data Capture , CNC Data Capture , Digitalization , Automated Data Capture, Overcoming Manual Limitations, Transition to Automation |
| Transition process | 5 Steps to Automation : Define Goals, Select Technology, Implementation Plan, Employee Training, Monitor and Analyze Scale |
| Tools and techniques | IIoT Gateway, Sensor-Based, Protocol Conversion, Pilot Project, Change Management, System Integration, Real-Time Data |
| Results/Goals | Reduce Human Error , Increase OEE , Data Analysis, Predictive Analytics, Productivity Improvement |
| Environment | CNC Machining , Industrial Plant, CNC Shop Floor, Legacy Machines |
Automated CNC Data
5 Steps to Automation
IIoT Gateway
Production data collection
Reduce Manual Data
