Analytics Applications
Converting data into insights through custom metrics and predictive models
Your Progress
Section 3 of 5From Data to Decisions
Raw data is meaningless without analytics. Industrial data systems generate terabytes daily, but value comes from converting data into actionable metrics. The analytics ladder has three rungs: 1) Descriptive analytics (what happened?)—dashboards showing energy use, production output, downtime. This is the baseline: visibility into current state. 2) Diagnostic analytics (why did it happen?)—root cause analysis identifying that a 10% efficiency drop correlates with ambient temperature above 30°C. 3) Predictive/prescriptive analytics (what will happen? what should we do?)—machine learning models forecasting equipment failure 72 hours in advance, recommending preventive maintenance. Custom metrics are the bridge: Generic data (kWh, units, hours) becomes specific KPIs (energy intensity, OEE, carbon per ton). Operators optimize what they measure. If you track energy consumption but not energy intensity (kWh/unit), efficiency gains from higher production go unnoticed. Decarbonization applications: Predictive models optimize boiler loads to minimize fuel use. Real-time emissions tracking enables dynamic carbon pricing. Digital twins simulate process changes before implementation—testing "what if we reduce steam pressure by 10%?" without risking actual operations. The limiting factor is no longer data availability—it's analytical sophistication and organizational ability to act on insights.
Interactive Metric Formula Builder
Build custom KPIs by combining operational data with simple math
🧮 Build Custom Metrics
Select two metrics, choose an operator, and calculate custom KPIs. Or load a template.
Available Metrics (select 2)
Your Formula
Common Metric Templates
Energy Intensity
Carbon Intensity
OEE (Overall Equipment Effectiveness)
First Pass Yield
Water Intensity
Key Analytics Applications
Predictive Maintenance
ML models forecast failures days/weeks ahead by analyzing vibration, temperature, and performance trends. Reduces unplanned downtime 30-50%.
Energy Optimization
Real-time algorithms adjust production schedules to minimize cost and carbon by shifting loads to off-peak hours or high-renewable grids.
Quality Control
Statistical process control detects anomalies before defects occur. Vision systems inspect 100% of products at line speed.
Supply Chain Visibility
Track materials from supplier to customer in real-time. Optimize inventory to reduce working capital 20-30%.
💡 Key Insight
The 80/20 rule applies to analytics: 80% of value comes from simple descriptive metrics (dashboards, alerts, basic KPIs). Don't over-engineer. Start with energy intensity, OEE, and carbon per ton. Graduate to predictive models only when you've exhausted low-hanging fruit. Many plants still lack basic real-time visibility—advanced ML is premature.