OEE software + dashboards: recording and monitoring for production

Basics of OEE Data Capture

Determining Overall Equipment Effectiveness relies on precise mathematical models and standardized calculation methods, encompassing three core components. The technical implementation of data capture for these components is critical for the accuracy and reliability of OEE monitoring.

1. Availability

The availability of a system is defined as the ratio between planned production time and actual operating time, expressed by the formula:

Capture Parameters

To accurately calculate availability, the following technical parameters must be captured:

• Planned Production Time: Defined by production planning, shift models, and working time regulations
• Downtime: Time periods when the system is not operating despite planned production
• Setup Times: Time intervals for product changes, tool changes, and setup processes
• Downtime Causes: Categorized cause codes per ISO 22400 or company-specific catalogs
• Micro-Downtimes: Short interruptions below a defined threshold (typically 3-5 minutes)

Technical Detection Methods

Capturing system availability can be achieved through various technical methods:

• Direct Signal Detection: Capture of PLC signals such as "System in Automatic," "Emergency Stop," or "Fault Active"
  • Implementation Example: Connection to potential-free contacts with 24V signal level and optocouplers for galvanic isolation
  • Sampling Rate from: 10ms for precise detection of short downtimes
• Operating Time Monitoring: Measurement of drive components via current consumption or speed sensors
  • Technical Parameters: 4-20 mA current measurement with converters, frequency inputs for speed detection
  • Threshold Definition: Configurable limits to distinguish between idle and production
• Advanced State Models: Use of multi-level state models beyond simple run/stop detection
  • Model Depth: Typed states such as "Production," "Setup," "Fault," "Maintenance," "Energy-Saving"
  • State Transitions: Defined transition rules with hysteresis-based switching points

Capture Methodology and Data Quality

The technical implementation of availability capture is achieved through these methods:

• Automatic Capture:
  • Technology: Direct PLC connection via OPC UA or Modbus
  • Precision: Millisecond-accurate timestamps for state changes
  • Fault Tolerance: Local buffering with time-synchronized databases
• Semi-Automatic Capture:
  • Technical Implementation: Basic state capture automatically, cause assignment manually
  • Intelligent Pattern Recognition: Complex algorithm comparing actual cycle patterns with target patterns to identify deviations
  • Time Delay: Configurable timeout parameters for cause inputs
  • Validation: Automatic plausibility checks of entered data
• BDE Terminal-Based Capture:
  • Hardware: Industrial-grade touchscreen terminals with IP65 protection
  • User Interface: Optimized UX for shop floor environments, usable with gloves
  • Data Consistency: Integrated validation logic to prevent input errors

2. Performance

The performance component quantifies production speed relative to the theoretical maximum performance:

Capture Parameters

For precise performance calculation, the following technical parameters must be captured:

• Target Cycle Time: Reference value for the specified production speed, depending on product and technological process
• Actual Production Rate: Measured production speed in real time
• Production Quantities: Piece counts, meters, kilograms, or other product-specific units
• Cycle Rates: Frequency of production cycles in cycles per minute or seconds per cycle
• Speed Profiles: Complete speed curve for non-linear production processes

Technical Capture Methods

Precise capture of performance parameters requires specialized technical approaches:

• Direct Piece Counter:
  • Sensor Types: Photoelectric sensors, inductive sensors, barcode scanners, RFID readers
  • Signal Processing: Debouncing algorithms to eliminate bounce effects
  • Precision: Error rate <0.01% through redundant sensor configuration
• Cycle Time Capture:
  • Measurement Principle: High-precision timing between defined process steps
  • Synchronization: PTP (Precision Time Protocol) per IEEE 1588 for microsecond-accurate timing
  • Statistical Evaluation: Continuous calculation of average, standard deviation, and trend analyses
• Direct Speed Measurement:
  • Measurement Technology: Speed sensors, encoders, frequency converter feedback
  • Resolution: 12-16 bit for high-precision speed regulation
  • Scaling: Automatic conversion of drive speed into production units

Parameter Optimization and Calibration

For precise performance capture, the following technical measures are necessary:

• Reference Value Determination:
  • Methodology: Statistical analysis of historical best performances
  • Machine Learning: Continuous optimization of reference values through AI-supported models
  • Context Adaptation: Automatic adjustment to product variants, material batches, and environmental conditions
• Dynamic Target Value Adjustment:
  • Algorithm: Fuzzy logic rule set for situation-dependent target value determination
  • Parameters: Consideration of material parameters, tool wear, and ambient temperature
  • Self-Learning: Self-optimizing models for continuous improvement of reference values

3. Quality

The quality component measures the ratio of good parts to total production:

Capture Parameters

For detailed quality analysis, the following parameters must be captured:

• Total Production: Gross quantity of all produced units
• Scrap: Defective parts categorized by error types per defined error catalog
• Rework: Parts with defects that can be corrected through defined rework processes
• Error Causes: Categorized defect codes per standardized quality taxonomies
• Process Capability Metrics: Cpk, Ppk, and other statistical quality parameters
• Quality Costs: Monetary valuation of scrap and rework for ROI analyses

Technical Capture Methods

Quality capture requires specialized technical systems:

• Automatic Quality Inspection Systems:
  • Technologies: Machine vision, laser measurement systems, 3D scanning, X-ray inspection systems
  • Resolution: Micrometer-accurate measurement of critical product features
  • Error Classification: Deep learning-based detection and categorization of defects
• Inline Metrology:
  • Integration: Sensors integrated directly into the production process
  • Measurement Principles: Optical, tactile, inductive, capacitive depending on application
  • Real-Time Processing: Sub-millisecond analysis for high-speed production
• CAQ System Integration:
  • Interfaces: Standardized connection to quality management systems
  • Data Exchange: Bidirectional transfer of test plans, measurement data, and error statistics
  • Traceability: Batch- or part-specific assignment of quality data

Correlation Analyses and Predictive Quality

Advanced OEE monitoring systems offer extended analysis functions:

• Multi-Parameter Correlation:
  • Technical Basis: Big data analysis of process and quality parameters
  • Algorithms: Multivariate regression models, neural networks for defect prediction
  • Implementation: GPU-accelerated computation for complex correlation analyses
• Predictive Quality Models:
  • Functionality: Real-time prediction of quality issues based on process parameters
  • Technology: Machine learning models (Random Forests, Gradient Boosting, Deep Learning)
  • Validation: Continuous model verification and optimization through supervised learning

Technical Approaches to OEE Data Capture

Depending on the technological level of production systems and specific requirements, various technical capture methods are used. The selection and implementation of these methods directly impact the precision, timeliness, and reliability of OEE monitoring.

Manual Data Capture

Manual data capture remains a practical option in many production environments, especially for older systems without digital interface options.

Paper-Based Capture

• Documentation Standards: Standardized capture forms per ISO 22400
• Process Integration: Defined capture intervals (typically 2-4 hours)
• Data Transformation: Subsequent digitization through semi-automatic scanning processes

Digital Manual Capture

• Hardware Specifications:
  • Terminals: Industrial-grade touch panels (IP65/IP67)
  • Mobile Devices: Industrial tablets with shock-resistant housings (MIL-STD-810G)
  • Barcode Scanners: 1D/2D scanners with CMOS sensors, reading range 10-100 cm
• Software Architecture:
  • Frontends: Progressive Web Apps (PWA) for cross-device compatibility
  • Response Times: <300ms for critical UI interactions
  • Offline Capability: IndexedDB-based local data storage with synchronization mechanisms
• Capture Technologies:
  • Barcode Scanning: GS1-compliant codes for product and order tracking
  • RFID Integration: 13.56 MHz HF systems (ISO/IEC 14443) for contactless identification
  • NFC Authentication: Personalized access via NFC tokens (ISO/IEC 18092)

Technical Limitations and Error Minimization

• Data Quality Controls:
  • Plausibility Checks: Automatic validation against defined limits
  • Timestamp Accuracy: NTP-synchronized clocks for millisecond-accurate time capture
  • Duplication Detection: UUID-based records to eliminate duplicates
• Latency Aspects:
  • Data Capture Delay: Typically 5-30 minutes
  • Update Cycles: Configurable push or pull intervals (1-60 minutes)
  • Synchronization Strategies: Priority-based data transfer upon network restoration

Machine Data Capture (MDC)

Direct capture of machine signals represents the technological gold standard for OEE monitoring systems.

Direct Signal Capture

In direct signal capture, electronic signals from machine controls or sensors are captured with high precision:

• Signal Types and Technical Specifications:
  • Digital Inputs:
    • Voltage Levels: 24V DC (industry standard), 5V DC (TTL)
    • Galvanic Isolation: Optocouplers with 2.5-5 kV isolation voltage
    • Input Impedance: Typically 2.5-10 kΩ
    • Sampling Rate: 1-10 kHz for precise state capture
  • Analog Inputs:
    • Signal Ranges: 0-10V, 0-20mA, 4-20mA (industry standard)
    • Resolution: 12-16 bit for high-accuracy measurement
    • Accuracy: Typically ±0.1% of full-scale value
    • Sample & Hold architecture for precise instantaneous value capture
  • Pulse/Frequency Inputs:
    • Frequency Ranges: 0.1 Hz to 50 kHz
    • Debouncing Algorithms: Digital filtering with configurable time constant
    • Counter Data Formats: 32/64-bit for overflow protection in high-frequency counters
• Hardware Implementations:
  • I/O Modules:
    • Modular Design: DIN rail mounting per EN 60715
    • Communication Interfaces: EtherCAT, PROFINET, Modbus TCP
    • Protection Classes: IP20 (control cabinet mounting), IP67 (field mounting)
    • Temperature Ranges: -25°C to +70°C for industrial environments
  • IoT Gateways:
    • Processor Architecture: ARM Cortex-M4/A53 or Intel Atom for edge computing
    • Memory: 1-4 GB RAM, 8-32 GB flash for local data buffering
    • Connectivity: Ethernet (100/1000 Mbit), Wi-Fi (IEEE 802.11 a/b/g/n/ac), 4G/5G, LoRaWAN
    • Power Supply: 24V DC with wide-range input (18-36V) and surge protection
  • Sensor Networks:
    • Topologies: Star, mesh, or hybrid for redundant communication
    • Protocols: IO-Link (IEC 61131-9), ASi (EN 50295), Wireless HART (IEC 62591)
    • Power Supply: Power over Ethernet (PoE) per IEEE 802.3af/at or energy harvesting

OEE Monitoring: From Data to Insights

Mere data capture does not yet create value. Only through systematic monitoring and meaningful visualization do data become a valuable basis for decision-making.

Real-Time Monitoring

Modern OEE monitoring systems provide real-time insights into production status:

• Live Status Monitoring: Current operating states of all systems
• Performance Tracking: Continuous monitoring of OEE components
• Alert Systems: Automatic notifications for deviations or faults

These real-time functions enable quick responses to issues, preventing prolonged productivity losses.

Historical Analyses

In addition to real-time monitoring, analyzing historical data is crucial for continuous improvements:

• Trend Analyses: Long-term development of OEE and its components
• Loss Analyses: Identification and quantification of the largest loss factors
• Comparative Analyses: Benchmarking between systems, shifts, or sites

These analyses enable the identification of systematic issues and the derivation of targeted measures for OEE improvement.

Visualization: OEE Dashboards

The visual presentation of OEE data plays a central role in its usability:

Typical Dashboard Elements

• OEE Gauges: Tachometer-like displays for overall value and components
• Time Series Charts: Visualization of OEE trends over time
• Pareto Charts: Prioritized display of loss causes
• Heatmaps: Color-coded representation of problem areas
• Production Calendar: Overview of OEE values by days or shifts

Application-Specific Dashboards

Different dashboard variants are needed depending on the target audience and use case:

• Shopfloor Dashboards: Large display boards focusing on current performance
• Management Dashboards: Summary views focusing on KPIs
• Analysis Dashboards: Detailed evaluations for production experts
• Mobile Dashboards: Optimized display for smartphones and tablets

The proper design of these dashboards is critical for the effective use of OEE data across various business areas.

Modern OEE Software: From On-Premises to Cloud-Native

The evolution of OEE software has paralleled the broader development of IT landscapes, transitioning from traditional on-premises systems to advanced cloud-native solutions for enhanced digital manufacturing efficiency.

Legacy Systems and On-Premises Solutions

Traditional OEE systems were often implemented as on-premises solutions:

• Local Installation: Software operated on company-owned servers
• High Initial Costs: Significant investments in hardware, licenses, and implementation
• Complex Updates: Time-consuming update processes with potential downtimes
• Limited Scalability: Capacity expansions require additional hardware
• Isolated Data Silos: Limited options for cross-site data consolidation

While these systems offer high control over IT infrastructure, they pose significant drawbacks in agility, scalability, and cost structure.

Cloud-Native MES Solutions

The latest generation of Manufacturing Execution System (MES) software leverages cloud-based technologies and follows the Software-as-a-Service (SaaS) model:

• No Local Infrastructure: Operated in high-availability data centers
• Subscription-Based Pricing: Predictable monthly or annual costs instead of high upfront investments
• Automatic Updates: Continuous feature enhancements without maintenance windows
• Unlimited Scalability: Flexible growth based on demand
• Global Data Availability: Cross-site data access and analysis
• Rapid Implementation: Productive use within days instead of months

Cloud-native OEE software offers significant advantages for small to large enterprises, particularly those with multiple sites, driving OEE digital transformation.

SYMESTIC Cloud-MES: A Complete Solution for OEE Monitoring

The SYMESTIC Manufacturing Platform is a cloud-native MES solution offering comprehensive features for OEE data capture, visualization, and analysis, enhancing digital manufacturing efficiency.

Platform Architecture

The SYMESTIC solution is built on a multi-layered architecture:

1. Connectivity Layer: Various options for machine integration
   • DI Cloud Gateway: Direct signal capture via digital inputs
   • OPC-UA Cloud Gateway: Standardized connection to modern controls
   • Shopfloor Clients: Browser-based terminals for manual capture
2. Cloud Infrastructure: Highly scalable Microsoft Azure platform
   • Microservice architecture for high availability
   • Comprehensive data security per industrial standards
   • Multi-tenant capability with strict tenant separation
3. Manufacturing Apps: Modular applications for various use cases
   • MDC (Machine Data Capture): Real-time monitoring for maximum efficiency
   • BDC (Business Data Capture): Transparent and efficient data collection
   • Metrics: Data-driven decisions in real time

OEE-Specific Functionalities

The SYMESTIC platform provides specialized features for OEE monitoring:

Availability Component Capture

• Automatic State Detection: Real-time identification of production and downtime states
• Categorized Downtime Capture: Structured recording of downtime causes
• Integrated Fault Management: Workflow-based handling of fault reports
• Shift Planning: Flexible definition of production times and shift models

Performance Component Capture

• Automatic Piece Counting: Precise counting of produced units
• Dynamic Cycle Time Analysis: Detection of deviations from ideal values
• Product- and Order-Specific Targets: Flexible reference values per product

Quality Component Capture

• Integrated Quality Inspection: Direct capture of scrap and rework
• Error Cause Catalog: Standardized categorization of quality issues
• Quality Trend Analyses: Detection of systematic quality deviations

Visualization and Analysis

SYMESTIC offers powerful visualization and analysis capabilities:

• Configurable Dashboards: Customizable views for different user groups
• Real-Time Displays: Live presentation of current OEE values and production states
• Historical Analyses: Trend displays and comparative analyses over any period
• Metric Hierarchies: Drill-down from plant to machine level
• Mobile Access: Native apps for iOS and Android for location-independent access

Integration Options

The SYMESTIC platform provides extensive integration possibilities:

• ERP Integration: Bidirectional data exchange with SAP, Microsoft Dynamics, and other systems
• REST APIs: Standardized interfaces for third-party integrations
• 3rd Party Integration: Open interfaces for incorporating additional systems

Implementation Approach and ROI

Implementing a cloud-native OEE solution like SYMESTIC follows a structured process to ensure successful OEE implementation and measurable manufacturing efficiency gains.

Phased Implementation Approach

1. Evaluation: Familiarization with the platform and its functionalities
2. Pilot Plant: Implementation in a pilot area for validation
3. Global Rollout: Gradual expansion to additional sites

This approach enables low-risk introduction with rapid initial successes.

Typical ROI Factors

Users of the SYMESTIC platform report measurable OEE ROI results:

• Reduced Project Time: >90% time savings in IT/OT integration
• Time Savings in Reporting: >90% reduction in effort for KPI reports
• CAPEX Savings: >95% through SaaS model without initial investments
• Reduced Downtimes: Typically 10% through detailed data analysis
• More Efficient Reporting: 50% higher efficiency through seamlessly integrated processes

Cross-Industry Applications

The SYMESTIC solution is successfully used across various industries:

• Automotive Industry: Injection molding, assembly, forming (e.g., Yanfeng, Meleghy Automotive)
• Metal Processing: Hot pressing, Bito, forging
• Consumer Goods: Brita, Britax Römer, Neoperl
• Food Industry: Erlenbacher Backwaren, New Coffee, L.Stroetmann Saat

Unique Advantages of the SYMESTIC Platform

Compared to other OEE solutions, the SYMESTIC platform offers several distinctive advantages, making it a leading choice for cloud-native OEE monitoring.

Rapid Implementation

• Productive deployment within days instead of months-long projects
• Self-service approach enables independent expansion by customers

Flexible Connectivity

• Multiple connection options for legacy and modern machines
• Support for various communication channels (LAN, 4G/5G)

Transparent Pricing Model

• Predictable monthly subscription costs
• No hidden costs for updates or support

Mobile-First Approach

• Native apps for mobile devices (iOS, Android)
• Location-independent access to production data

Enterprise-Ready

• Global scalability for multinational companies
• Highest security standards through Microsoft Azure infrastructure

Conclusion: The Path to Data-Driven Production

Implementing modern OEE software like the SYMESTIC Manufacturing Platform marks a critical step toward data-driven production. The combination of precise data capture, insightful visualizations, and advanced analytics enables not only the measurement but also the continuous improvement of Overall Equipment Effectiveness, driving OEE excellence.

Cloud-native solutions offer particular advantages in terms of implementation speed, scalability, and cost-efficiency. The SYMESTIC platform combines these benefits with industry-specific expertise and an intuitive user interface, shortening the path from data collection to value-creating actions.

For companies aiming to enhance competitiveness through higher production efficiency, digitized OEE monitoring with a cloud-native solution provides a compelling approach with proven ROI and rapid implementation successes.