ODC – Operational Data Collection explained simply: From manual paper notes to intelligent production

The Initial Situation: Why Production Data Is No Longer Optional Today

In modern manufacturing, the framework conditions have fundamentally changed. What used to be manageable with tally sheets and manual records quickly reaches its limits today:

Increasing product variety: Customer-specific variants, smaller batch sizes, and frequent product changes significantly increase complexity in production.
Networked machine environments: CNC machines, robots, presses, and assembly lines are increasingly interconnected – and continuously generate data.
Large volumes of data: Every shift, every order, every machine hour generates information. Without structured collection, transparency is lost.
Difficult planning: Without valid real-time data, bottlenecks, delays, or quality issues remain invisible for too long – until it is too late.

The result: Production managers work with incomplete or outdated information. Decisions are based on assumptions rather than facts. Delivery dates come under pressure, downtimes remain unexplained, and optimization potential is left unused.

This is exactly where Operational Data Collection (ODC) comes into play: It makes visible what is actually happening in production – in real time, completely, and transparently.

What Is ODC? Definition and Differentiation

Operational Data Collection (ODC) refers to the systematic, digital collection of all production-relevant information directly at the point of occurrence – in manufacturing, at the workstation, at the machine.

Among others, the following data is recorded:

• Order data: Which order is currently running? Which product is being manufactured?
• Times: Setup time, processing time, downtime
• Quantities: Good parts, scrap, rework
• Personnel: Who is working on which order? What qualifications are available?
• Downtime reasons: Why is the machine stopped? What is the cause of the downtime?
• Quality data: Measurement values, inspection results, deviations

ODC vs. MDC: What Is the Difference?

The term Machine Data Collection (MDC) describes the automatic collection of machine data – i.e., signals and states directly from the control system (e.g., PLC, CNC). MDC provides objective, machine-side information such as runtimes, cycle rates, or sensor values.

ODC is broader in scope: In addition to machine data, it also includes manual inputs from employees – such as downtime reasons, quality reports, or order feedback. ODC therefore maps the entire production process, not just the machine.

In practice, both approaches complement each other: Modern systems such as ODC by gbo datacomp combine automatic machine connectivity (MDC) with user-friendly terminals for manual input.

→ More about MDC

How Does ODC Work in Practice? Automatic vs. Manual

The specific design of an ODC solution depends on the company, the industry, and the level of automation. In principle, three types of data collection can be distinguished:

1. Manual ODC: Data Collection via Terminals

Employees enter data directly at a terminal – for example via touchscreen, barcode scanner, or RFID. Typical inputs include order start and end, downtime reasons, good and scrap quantities, and material consumption.
Advantage: Flexible use, even with older machines without digital interfaces.

2. Automatic ODC: Machine Connectivity via MDC

Data is read directly from the machine control – fully automatically and in real time. Recorded data includes machine states (production, setup, fault), cycle times, quantities, and process parameters such as temperature or pressure.
Advantage: Objective, complete data without manual effort.

3. Hybrid ODC: The Best of Both Worlds

Most modern ODC systems combine both approaches. Machine connectivity provides objective data, while terminals enable contextual additions – such as downtime reasons or quality assessments.
The result: A complete, reliable database that reflects both machine-side and human factors.

Practical Applications: How ODC Increases Your Production Efficiency

ODC is far more than a digital tally sheet. It enables concrete improvements:

• Downtime recording and analysis – ODC automatically records downtimes and prompts employees to specify the reason. Over time, reliable analyses emerge: Which machines are prone to failure? Which downtime reasons occur frequently?
• Downtime cause analysis – With standardized downtime catalogs, the reasons for production interruptions are recorded. The data shows whether technical, organizational, or personnel-related causes dominate.
• Good and scrap quantities – ODC records not only how much was produced, but also how much of it is usable. Scrap and rework rates become transparent.
• Basis for KPIs – ODC provides the raw data for key production indicators such as OEE (Overall Equipment Effectiveness), lead times, and on-time delivery. Without ODC, these KPIs remain estimates. With ODC, they become measurable – and controllable.

ODC as the Foundation for MES

ODC is the data foundation for a Manufacturing Execution System (MES) – the next stage of digital manufacturing. While ODC collects data, an MES orchestrates the entire production process: detailed planning, digital work instructions, quality management, and maintenance planning.

→ More about gboMES
→ Maintenance with gboMES

ODC vs. Excel: Why Spreadsheets Reach Their Limits

Many companies start data collection with Excel spreadsheets. This works – up to a certain point:

Excel
Real-time data: No, delayed
Data quality: Error-prone
Evaluations: Manual, time-consuming
Scalability: Limited
ERP integration: Manual transfer

ODC
Real-time data: Yes, automatic
Data quality: Integrated validation
Evaluations: Automatic
Scalability: Unlimited
ERP integration: Automatic

Conclusion: Excel is a good tool for analysis – but not a system for operational data collection. As soon as multiple shifts, machines, and employees are involved, a professional ODC solution becomes indispensable.

Industry-Specific Requirements

Plastics processing: Keeping cycle times and tool wear in view with ODC
Precise cycle times, tool wear, and downtime analysis are critical. Injection molding machines operate in seconds – small deviations quickly add up.

Medical technology: Seamless documentation for certifications thanks to ODC
Regulatory requirements (FDA, ISO 13485) demand complete documentation. ODC ensures that all data is recorded and archived in an audit-proof manner.

Metalworking and mechanical engineering: More transparency for custom orders with ODC
Customer-specific orders, complex work plans, and frequent setup processes. ODC makes it transparent where time is lost – and enables targeted improvements.

Practical Example: Schweiger Fulpmes GmbH – ODC in Metalworking

Schweiger Fulpmes GmbH, an Austrian metal processor in machine and plant engineering, faced a typical challenge: increasing product variety, growing customer requirements, and the desire to react flexibly to production changes.

The initial situation:
Production data was collected decentrally – partly manually, partly in Excel. Data exchange between the shop floor and ERP system was delayed and error-prone.

The solution: Introduction of gboMES
Schweiger Fulpmes opted for full digitalization with gboMES – including automatic machine and operational data collection. Machines were connected via standardized interfaces, and ODC terminals were installed in production.

The results:

• Increased transparency in production – real-time insight into order status and machine utilization
• Minimization of downtimes – systematic downtime analysis enables targeted countermeasures
• Improved data analysis – real-time information as a basis for decisions
• Foundation for predictive maintenance – condition data enables proactive maintenance

→ Read the full user report

Implementation Steps: How to Successfully Start with ODC

Phase 1: Analysis and goal setting – Which data is critical? Which problems should be solved?
Phase 2: System selection and design – Selection of the ODC solution, definition of data collection logic, planning of machine connectivity.
Phase 3: Pilot phase – Introduction in a partial area, employee training, adjustments based on feedback.
Phase 4: Rollout – Expansion to additional areas, ERP integration, establishment of reporting and KPIs.
Phase 5: Continuous improvement – Regular data evaluation, adjustments as needed, expansion with MES functions.

ODC and Data Security: Protecting Sensitive Production Data

Production data is sensitive – it provides insights into capacities, processes, and competitive advantages. Modern ODC systems such as the ODC system from gbo datacomp address these requirements through role-based access rights, encrypted data transmission, audit-proof logging, and optional on-premise or cloud operation.

Conclusion: ODC as a Strategic Success Factor

Operational Data Collection is the foundation for transparency, efficiency, and competitiveness in modern manufacturing. Those who consistently use ODC gain real-time insight into all production processes, reduce downtime and scrap, establish seamless documentation, and create the basis for MES, predictive maintenance, and AI-driven optimization.

The question is no longer whether ODC makes sense – but when you start.

Next Steps: Start with ODC Now

→ Learn more about ODC
→ Contact us for an individual consultation
→ Read more real-world success stories

‍