Efficient analysis of production processes – The path to measurable improvements

Production Analysis Today: Why Manual Data Collection Reaches Its Limits

In modern manufacturing, transparency and fast responsiveness are crucial for competitiveness. Yet many companies still work with Excel spreadsheets, manual reports, or isolated solutions: data is captured with delays, relationships remain hidden, and decisions are based on incomplete information. The result is unplanned downtime, hidden bottlenecks, and untapped optimization potential.

Efficient analysis of production processes requires a systematic, data-driven approach. The MES (Manufacturing Execution System) forms the central control and analysis layer of production. ODC and MDC are functional data collection areas through which production and machine data is captured in a structured way and made available to the MES in real time.

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What Is an MES – and What Role do ODC and MDC Play?

The Manufacturing Execution System (MES) is the leading operational layer between ERP and the shop floor. It aggregates data from ODC and MDC, places it in a production context, calculates relevant KPIs, and enables data-driven control of manufacturing.

Operational Data Collection (ODC) is a core module within an MES and enables structured capture of manual feedback directly on the shop floor. Via terminals, employees report items such as order starts, quantities, downtimes, or quality deviations. The data is available to the MES in real time and in a standardized structure—a clear improvement over paper-based or retrospective data capture.

Machine Data Collection (MDC) complements manual feedback as another MES module by automatically connecting machines and equipment. Via interfaces, operating states, run and downtime, process parameters, and—depending on the technical integration—additional measurement values are recorded.

Only the combination of ODC and MDC within the MES enables a consistent and complete picture of production and provides the foundation for reliable analyses and well-informed decisions.

The Shift to Data-Driven Production Analysis

With an MES, process analysis is not only digitized but strategically advanced. The integrated modules ODC and MDC automatically capture data that comes directly from ongoing production and is consolidated within the MES.

Among other things, the following is recorded:

• Actual run times and cycle times
• Quantities and scrap volumes
• Downtime causes and durations
• Process parameters (temperature, pressure, speed)
• Quality data and inspection results

This transparency is created through ODC and MDC. The data forms the basis for structured analyses such as bottleneck identification, downtime evaluation, or OEE optimization.

Real-Time Data vs. Manual Recording: What Makes the Difference?

The key difference is not only speed, but the ability to act. Real-time data enables proactive action instead of reactive problem handling.

For those responsible for production and planning, this means:

• Problems become visible while they occur—not hours or days later
• Relationships become recognizable—e.g., between shift changes and quality fluctuations
• Decisions are based on facts—not estimates or memory

In food production, this advantage is particularly clear: temperature deviations or process time overruns must be documented immediately. Manual recording carries risks due to transfer errors or forgotten entries. Automatic systems ensure seamless traceability and compliance.

In mechanical engineering with complex variant manufacturing, real-time data capture enables precise post-calculations and realistic quotations based on actual processing times.

How Does Bottleneck Analysis Work in Practice?

Bottlenecks limit overall production throughput and are often difficult to identify. A systematic bottleneck analysis is based on capturing throughput times, waiting times, and utilization across all relevant stations.

The MES visualizes this data and automatically identifies:

• Stations with above-average processing times
• Areas with frequent waiting times
• Fluctuations between shifts or product variants

By correlating with order data, staffing levels, and material availability, the real root causes can be narrowed down. Is a machine overloaded? Are qualified employees missing? Are there material shortages?

Based on these insights, targeted measures can be developed:

• Create additional capacity
• Redesign processes
• Adjust buffer stocks

The impact of each measure can then be quantified.

Downtime Analysis: How Can Unplanned Downtime Be Reduced?

Unplanned downtime is among the most costly problems in manufacturing. Effective downtime analysis categorizes all interruptions by cause, duration, and frequency.

The system records not only that a machine is stopped, but also:

• Why – technical defect, material defect, tool change, quality issue
• How long – from short stops to multi-hour outages
• When – patterns by shift, weekday, or product change

The evaluation reveals typical patterns: recurring technical problems indicate maintenance needs; frequent setups point to suboptimal batch-size planning; organizational downtimes indicate process weaknesses. In automotive supplier companies—where process reliability and cycle optimization are critical—downtime analysis enables precise availability forecasts and predictive maintenance through continuous Process Data Collection (PDC).

Which KPIs Matter – and How Are They Calculated Automatically?

Overall Equipment Effectiveness (OEE)

OEE is the key KPI for evaluating equipment effectiveness. It combines three dimensions:

• Availability – how much time is the equipment actually available?
• Performance – how fast does the equipment produce relative to the target speed?
• Quality – how many good parts are produced relative to total output?

Long-standing gbo datacomp customers report average OEE improvements of up to 38% after implementation. These significant gains result from the MES as a central data hub that creates transparency, enables faster decisions, and optimizes processes through real-time monitoring.

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Scrap Rate and First-Pass Yield

The scrap rate measures the proportion of defective products. First-pass yield indicates how many products pass quality inspection without rework. Both KPIs are directly linked to costs: every scrap part consumes material, machine time, and energy without generating value.

Cycle Times and Lead Times

Cycle times measure the time between two consecutive units and show how well theoretical capacities are utilized. Lead times cover the entire duration from order start to completion, including waiting and transport times.

Long lead times with short processing times indicate organizational inefficiencies. In practice, gboMES reduces lead times by up to 19% through automation and optimization of production workflows. Early identification of maintenance issues prevents unplanned downtime and optimizes machine utilization. Paperless manufacturing reduces manual processes and results in time savings, higher data accuracy, and increased transparency.

Setup Times

In variant manufacturing, setup times play a central role. Capturing them shows where standardization, better tool preparation, or training can deliver improvements.

Fully Integrated Analysis Instead of Isolated Solutions

A modern MES integrates all analysis processes centrally. Production data, quality information, machine states, and order data are available within one system and clearly assigned.

In the era of Industry 4.0 and IoT, gboMES is the central link between ERP, PPS, and other production systems. Networking minimizes manual intervention, reducing errors and increasing production speed. Cross-functional data flow consolidates all real-time information from every production area in one central place.

As a result, everyone works with the same data foundation:

• Production managers see current utilization and bottlenecks
• Quality managers detect deviations in real time
• Maintenance receives early indicators of issues
• Executive management has consolidated KPIs for strategic decisions

Manual lists, media breaks, or parallel systems become unnecessary. All relevant information is available consistently and in a structured format—the basis for fast, well-founded decisions.

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Practical Example: Hans Berg GmbH – 80% Fewer Disruption Sources in Automotive Manufacturing

Hans Berg—market and technology leader in connection technology and producer of precision and safety components for the automotive industry—identified a critical discrepancy in the production of chassis and airbag components: the theoretical cycle time did not match the actual number of parts produced.

The problem:
Machine stoppages under ten minutes were not recorded, and the causes of disruptions remained unclear. Targeted optimization was impossible.

The solution:
Hans Berg implemented gboMES with a two-stage data capture logic. Machines were connected via PLC modules and automatically differentiate between “in production” and “downtime.” When cycle time limits are exceeded, the system sets the machine to “undefined interruption”—and the operator enters the reason with two clicks at the terminal.

Measurable results:

• Reduction of disruption sources by more than 80%
• Improvement of cycle time by 20%
• Full transparency across all production downtimes

Dashboards in the production halls visualize equipment status, orders, and remaining run times in real time. In morning shop-floor meetings, the gboMES logs are analyzed—the foundation for continuous improvement.

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How Do I Start Data-Driven Production Optimization?

Create transparency: The first step is a structured assessment: where do losses occur? Which processes are critical? Which data is already being captured? An on-site workshop with experienced consultants provides clarity on potential and priorities.

Implement data capture systems: Based on the analysis, suitable systems are selected and introduced step by step. Employee involvement is crucial—training and continuous support ensure high acceptance and strong data quality.

Establish analyses and improve continuously: Once reliable data is available, systematic analysis begins. Regular reviews identify improvement potential, measures are prioritized and implemented. Continuous success tracking shows which interventions work.

What matters is establishing a culture of continuous improvement. Data-driven analysis becomes a fixed part of leadership routines.

Conclusion: From Reactive Troubleshooting to Strategic Process Optimization

With modern systems for Operational Data Collection (ODC) and Machine Data Collection (MDC), production analysis evolves from a reactive task into a strategic discipline. The combination of real-time data, automatic KPI calculation, structured analysis workflows, and integrated processes lays the foundation for measurable, sustainable improvements.

In short: modern MES systems don’t just digitize production analysis—they turn it into a strategic competitive advantage.

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