Historical Intelligence
Retrospective Analysis
Unlock Insights from Your Historical Data
Analyze weeks, months, or years of TAG and Advanced TAG data. Identify trends, discover patterns, and make data-driven decisions. Our comprehensive historical analysis tools turn your stored data into actionable intelligence.
Core Capabilities
Powerful Historical Analysis Tools
Comprehensive Data Storage
All TAG and Advanced TAG values with SaveToDatabase enabled are automatically stored. Access complete historical records with flexible time range selection.
Advanced Analysis Algorithms
Four powerful algorithms: DIFFERENCE for change detection, LINEAR for trend analysis, AVERAGE for smoothing, and SUM for totals. Each optimized for different analysis scenarios.
Multiple Chart Types
Visualize data with Line, Bar, Area, and Pie charts. Each chart type optimized for different data patterns and analysis needs.
Flexible Time Filtering
Analyze any time period from hours to years. Compare different periods, identify seasonal patterns, and track long-term trends.
Period Comparison
Compare performance across different time periods. Identify improvements, detect anomalies, and validate optimization efforts.
Export & Reporting
Export analysis results to Excel, PDF, or CSV. Generate comprehensive reports for stakeholders and documentation.
SaveToDatabase Feature
Automatic Historical Data Storage
Every TAG and Advanced TAG with SaveToDatabase enabled automatically stores values at configured intervals. This creates a comprehensive historical database for analysis.
How Data Storage Works
- 1Enable SaveToDatabase on any TAG or Advanced TAG
- 2Configure Storage Schedule (update frequency)
- 3System automatically saves values at specified intervals
- 4Data stored with precise timestamps
- 5Historical database grows continuously
- 6Access data anytime for retrospective analysis
Flexible Storage Schedules
Configure different storage frequencies for different TAGs based on importance and data volatility.
- • Critical TAGs: Every 1-5 minutes for detailed analysis
- • Standard TAGs: Every 15-30 minutes for trend tracking
- • Slow-changing TAGs: Every hour for long-term monitoring
- • Daily summaries: Once per day for overview data
What Gets Stored
- ✓TAG values (from PLCs, sensors, devices)
- ✓Advanced TAG calculated values
- ✓Precise timestamps
- ✓Data quality indicators
- ✓Change events and transitions
Four Powerful Methods
Analysis Algorithms
Each algorithm is designed for specific analysis scenarios. Choose the right algorithm to extract maximum insights from your historical data.
DIFFERENCE Algorithm
Calculates the change between consecutive data points. Perfect for consumption analysis and change detection.
Use Cases:
- • Energy consumption (kWh used per period)
- • Water usage tracking
- • Production quantity counting
- • Material consumption analysis
Example:
If energy meter shows 1000 kWh at 08:00 and 1250 kWh at 09:00, DIFFERENCE = 250 kWh consumed in that hour
LINEAR Algorithm
Shows actual values over time. Best for trend analysis and pattern recognition.
Use Cases:
- • Temperature trends
- • Pressure monitoring
- • Speed variations
- • Performance tracking over time
Example:
Track compressor pressure throughout the day to identify peak usage times and optimization opportunities
AVERAGE Algorithm
Calculates mean values for selected time periods. Smooths out fluctuations to reveal underlying trends.
Use Cases:
- • Daily average temperature
- • Average production speed per shift
- • Mean power consumption
- • Typical operating conditions
Example:
Calculate average motor current per hour to identify abnormal operating conditions
SUM Algorithm
Totals values across time periods. Essential for accumulation and total consumption analysis.
Use Cases:
- • Total production quantity
- • Cumulative energy cost
- • Total operating hours
- • Aggregate consumption metrics
Example:
Sum all production counts per day to track daily output and identify productivity trends
Proven Applications
Real-World Analysis Scenarios
Energy Consumption Optimization
Challenge:
High energy costs with unclear consumption patterns
Analysis:
Use DIFFERENCE algorithm on energy meter TAGs to analyze hourly consumption over 3 months
Insights:
- • Identified peak consumption during off-hours
- • Discovered equipment running unnecessarily on weekends
- • Found seasonal patterns in HVAC usage
Result:
Implemented schedule changes and reduced energy costs by 22%
💰 €45,000 annual savings
Production Efficiency Analysis
Challenge:
Inconsistent production output across shifts
Analysis:
Use LINEAR algorithm to track production speed and SUM for daily totals over 2 months
Insights:
- • Morning shift consistently 15% faster
- • Equipment slowdown in afternoon hours
- • Specific days with recurring issues
Result:
Adjusted maintenance schedule and improved training
💰 18% increase in overall production efficiency
Predictive Maintenance
Challenge:
Unexpected equipment failures causing downtime
Analysis:
Use LINEAR algorithm on vibration and temperature TAGs over 6 months
Insights:
- • Gradual temperature increase before failures
- • Vibration patterns change 2-3 days before issues
- • Specific operating conditions trigger problems
Result:
Implemented predictive maintenance based on historical patterns
💰 65% reduction in unplanned downtime
Quality Control Analysis
Challenge:
Quality issues with unknown root causes
Analysis:
Use AVERAGE algorithm on process parameters during good vs bad production runs
Insights:
- • Temperature variance correlated with defects
- • Pressure instability during quality issues
- • Time-of-day patterns in quality metrics
Result:
Tightened process control parameters
💰 40% reduction in defect rate
Complementary Tools
Instant Analytics vs Retrospective Analysis
Both tools work together to provide complete data intelligence. Choose based on your analysis needs.
Instant Analytics
Focus: Real-time monitoring and immediate insights
Timeframe: Current data, live updates
- • Monitor current operations
- • Detect immediate issues
- • Real-time dashboards
- • Live alerts and notifications
Retrospective Analysis
Focus: Historical patterns and long-term trends
Timeframe: Days, weeks, months, years of data
- • Identify long-term trends
- • Root cause analysis
- • Performance comparisons
- • Strategic planning
Using Both Together
Instant Analytics alerts you to issues in real-time. Retrospective Analysis helps you understand why they happened and how to prevent them.
Expert Tips
Best Practices for Effective Analysis
- 1Enable SaveToDatabase on all important TAGs from the start - you can't analyze data you didn't store
- 2Use appropriate storage schedules - more frequent for critical TAGs, less for slow-changing values
- 3Choose the right algorithm for your question - DIFFERENCE for consumption, LINEAR for trends
- 4Analyze sufficient time periods - at least 2-4 weeks for meaningful patterns
- 5Compare similar periods - same days of week, same seasons, similar conditions
- 6Look for patterns, not just individual data points
- 7Combine multiple TAGs to understand relationships and correlations
- 8Document insights and share findings with your team
- 9Use period comparison to validate improvements after changes
- 10Export and archive important analysis results for future reference
Turn Your Historical Data into Insights
Join hundreds of industrial facilities using Retrospective Analysis to optimize operations, reduce costs, and make data-driven decisions based on proven patterns.
See historical analysis in action with your data