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Measurable Results.
Real Customers.

From single-site manufacturing to national retail networks – these projects show how monitoring and energy management work together in practice.

Automotive · Driveshafts

Willi Elbe Gelenkwellen GmbH

Automotive supplier with multiple production sites in Germany. ISO 50001 implementation, reduction of reactive power and load profile optimisation were the key focus – with rollout to additional plants.

Multiple sites, Germany
Pilot site: 50 meters; then rollout to 2 further plants
How the Project Ran
1
Measurement Concept & Installation — Pilot site with 50 measuring points; reactive power sources identified
2
ISO 50001 Implementation — Alligator documentation as the foundation; audit-ready reports on demand
3
Group Rollout — Expansion to 2 further plants; group-wide benchmarking and CO₂ reporting
Results (Experience Value)
5–15 %
Savings potential identified
Through cross-site competition and best-practice transfer between plants
  • ISO 50001 audit-ready at the push of a button
  • Reactive power reduction via grid analysis
  • Peak loads reduced through startup control
  • Scalable to international sites without disruption
EnPIs Used: kWh per manufactured shaft kWh per operating hour Group-internal benchmarking CO₂ footprint per site
Automotive Supplier · Shopfloor

Schobert Automotive GmbH

Two production plants with 180 real-time measuring points. Goal: cross-site machine benchmarking, PV self-consumption optimisation and active shopfloor energy management.

2 plants, Germany
180 electricity measuring points in real time
How the Project Ran
1
Dense Metering — 180 measuring points on machines, plant and PV; protocol variety (Modbus, M-Bus)
2
Machine Benchmarking — identical machines at both plants live in comparison; maintenance backlogs made visible
3
PV Self-Consumption — control logic maximises self-consumption, grid consumption minimised
Results
Active Shopfloor Management
Technology → Business Economics
Energy data used directly to optimise unit costs
  • Unit cost reduction through best-practice transfer
  • PV self-consumption rate measurably increased
  • Maintenance planning based on real machine data
EnPIs Used: Energy demand per machine (direct comparison) CO₂ emissions per site PV self-consumption rate
Plastics Processing · AI Research

BIOTEC GmbH & Co. KG

Bioplastics manufacturer with 13 extrusion lines. Real-time monitoring of all extruder parameters (current, pressure, temperature, throughput) directly in the EnMS software — combined with a publicly funded AI research project.

100 measuring points, 13 extrusion lines
Funded AI research project active
How the Project Ran
1
Process Integration — energy and production data (throughput, temperature, pressure) consolidated in one system
2
EnPI Normalisation — specific energy consumption per tonne throughput; cross-line comparison
3
AI Research Project — anomaly detection via power signature for automated scrap prevention
Results
AI-Assisted Anomaly Detection
Energy + Production = One System
Power signatures detect quality issues before scrap is produced
  • Actual/target temperature comparison per extrusion line
  • Cross-line efficiency analysis
  • Recognised as official research project
EnPIs Used: kWh per tonne throughput Actual/target temperature per line Power signature anomaly index
Motorway Services · Retail Sites

Tank & Rast GmbH

250 motorway service areas nationwide in central monitoring. 30 sites with 10–15 sub-measurements for HVAC, catering and lighting. Heterogeneous metering from multiple suppliers — all in one system.

250 sites, Germany
Utility smart meters + sub-meters integrated
How the Project Ran
1
Meter Integration Without Replacement — 90%+ of existing meters integrated; no costly hardware upgrades
2
Benchmarking — cross-site comparison identifies inefficient locations; consumption variances up to 40%
3
Night Consumption Analysis — 35–50% of electricity used outside operating hours; targeted reduction initiated
Results (Experience Value)
>10 %
Energy savings
Benchmarking of inefficient sites and reduction of costly peak loads
  • Basis for targeted renovation decisions
  • Peak load reduction at 30 sites
  • Central overview for 250 locations
EnPIs Used: kWh/m² building area kWh per guest & transaction Load profile monitoring all sites
Construction & Infrastructure · Mobile System

Leading Civil Engineering Contractor

Power on construction sites is expensive, often wasted and hard to measure — conventional metering is not practical here. The mobile Alligator system (LoRaWAN, battery-powered) makes construction site power transparent for the first time.

Multiple construction sites, Germany
Mobile LoRaWAN system, network-independent
How the Project Ran
1
Mobile Installation — LoRaWAN sensors on site containers and connection points; no fixed cabling needed
2
Container Benchmarking — consumption per container type compared; night and weekend usage made transparent
3
Cost Allocation — site power now chargeable by trade and section for the first time; disputes with utilities avoidable
Results
Construction Site Power Made Transparent
No fixed infrastructure needed
Flexibly deployable at new sites — the system moves with the construction site
  • Consumption per container & trade measurable
  • Night/weekend consumption reduced
  • Billing basis for supplementary charges
Technology: LoRaWAN (battery-powered) Mobile Alligator System Network-independent
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