Key Takeaways

Cost Savings: Energy cost savings equivalent to 17%of furnace energy costs, delivered without replacing existing infrastructure.

Operational Flow Efficiency: 17% reduction in annual energy consumption. Savings per furnace ranged from 4% to 25%.

Carbon Reduction: 20% decrease in specific carbon emissions per part.

Real-Time Flow Visibility: Live monitoring across furnace operations identified scheduling gaps and load inefficiencies automatically.

No Rip and Replace: Mavarick's Flow Intelligence platform integrated above existing infrastructure no hardware replacement required.

EU-Funded Initiative: Supported by the European Union, aligned with global sustainability standards.

Executive Summary

Lufthansa Technik, a global leader in aerospace MRO, faced a challenge common across energy-intensive industrial manufacturing,  operational flow data existed across their furnace operations but wasn't being turned into decisions. Scheduling gaps, suboptimal load management, and rising energy costs were invisible without real-time visibility across the production flow.

Mavarick deployed its Flow Intelligence platform above Lufthansa Technik's existing infrastructure, connecting live operational data, detecting inefficiencies in real time, and delivering AI-driven recommendations for scheduling and load optimisation. The result: 17% reduction in energy consumption, 20% reduction in carbon emissions per part, and cost savings equivalent to 17% of furnace energy costs without replacing any existing infrastructure.

This project demonstrates that Flow Intelligence, real-time visibility and anomaly detection layered above existing systems, can deliver double-digit cost savings and carbon reductions across energy-intensive industrial manufacturing environments.

About the Client

Lufthansa Technik is one of the world’s leading providers of technical aircraft services, supporting airlines, OEMs, and leasing companies worldwide. Its engine component repair and overhaul operations rely on industrial heat treatment and thermal processing furnaces, which are critical to turbine component MRO but also represent a significant share of facility energy consumption, contributing over 30% of total operational energy usage.

Committed to sustainability and reducing its environmental impact, the company has consistently prioritised practical approaches to optimising energy efficiency. As part of a European Union-funded initiative, it implemented an advanced, data-driven solution to improve operational efficiency and significantly reduce carbon emissions in its furnace operations, setting new benchmarks for sustainable aerospace practices.

The Challenge

Lufthansa Technik's furnace operations created four interconnected flow challenges, all invisible without real-time intelligence across the production process.

Operational Flow Invisible Without Real-Time Data Scheduling gaps and suboptimal load management created inefficiencies across furnace operations that were impossible to detect without live visibility. Without real-time monitoring, the team had no way to identify where flow disruptions were occurring or quantify their cost impact.

Disruptions Identified Too Late Without continuous visibility across furnace operations, inefficiencies in the production flow were only visible after they had already driven up energy consumption and costs. Reactive management meant problems were addressed after impact, not before.

Manual Processes Creating Operational Blind Spots Energy and operational data collection was fragmented and resource-intensive, limiting timely insights and making proactive flow optimisation impossible. Manual processes meant the team was always catching up, never ahead.

Rising Cost Pressure Surging European energy prices made operational flow inefficiencies increasingly expensive to ignore. Every scheduling gap and suboptimal load decision Mavarick deployed its Flow Intelligence platform togive Lufthansa Technik real-time visibility across their furnace operations —detecting scheduling inefficiencies, load management gaps, and operationaldeviations before they impacted production output and cost.

Live Operational Flow Monitoring Mavarickconnected existing operational data sources and applied real-time monitoringacross furnace operations — providing continuous visibility into scheduling,load management, and energy consumption without disrupting existinginfrastructure.

Anomaly Detection Across Operational Flow Theplatform's analytics engines aggregated and processed data from devices,detecting inefficiencies and identifying patterns that manual methods could notsurface. Unnecessary energy consumption during scheduling gaps and suboptimalload cycles were flagged automatically.

Cost and Emissions Visibility Mavarick'sFlow Intelligence platform provided continuous visibility into the cost andcarbon impact of furnace operations — enabling the team to identifyinefficiencies and take targeted action before they compounded.

Actionable Flow Optimisation AI-drivenrecommendations for each furnace — optimal scheduling adjustments and loadmanagement — were dynamically updated based on real-time operational data,turning visibility into decisions.

translated directly into avoidable cost and carbon exposure.

These challenges highlighted the need for an advanced, data-driven approach to sustainability optimisation of the site’s furnaces.

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The Solution: AI-Driven Furnace Optimisation

Mavarick deployed its Flow Intelligence platform to give Lufthansa Technik real-time visibility across their furnace operations, detecting scheduling inefficiencies, load management gaps, and operational deviations before they impacted production output and cost.

Live Operational Flow Monitoring Mavarick connected existing operational data sources and applied real-time monitoring across furnace operations, providing continuous visibility into scheduling, load management, and energy consumption without disrupting existing infrastructure.

Anomaly Detection Across Operational Flow The platform's analytics engines aggregated and processed data from devices, detecting inefficiencies and identifying patterns that manual methods could not surface. Unnecessary energy consumption during scheduling gaps and suboptimal load cycles were flagged automatically.

Cost and Emissions Visibility Mavarick's Flow Intelligence platform provided continuous visibility into the cost and carbon impact of furnace operations, enabling the team to identify inefficiencies and take targeted action before they compounded.

Actionable Flow Optimisation AI-driven recommendations for each furnace, optimal scheduling adjustments and load management, were dynamically updated based on real-time operational data, turning visibility into decisions.

Adoption & Integration

The project was executed in five comprehensive steps, ensuring seamless integration and maximum impact:

System Design:

The project began with designing the Flow intelligence system required for the organisation, combining edge devices, advanced analytics, and AI models (ML and LLM). This phase involved data exploration, variable identification, and alignment with Lufthansa Technik’s operational setup to ensure compatibility and effectiveness.

System Development:

Edge devices and gateways, were configured and integrated with the existing data management infrastructure. Updates were made to Mavarick’s core analytics platform to accommodate the specific needs of furnace optimisation. Additionally, ML and LLM models were developed, trained, and benchmarked using historical data to ensure accuracy in predictions and recommendations.

Testing and Validation:

The system was deployed across the furnaces for live testing. This phase validated the accuracy of data transmission, the functionality of analytics, and the effectiveness of AI-driven recommendations under real-world conditions. User feedback was collected to identify areas for refinement.

AI Model Refinement:

Based on insights from testing, the AI models were fine-tuned to improve their prediction accuracy and adaptability. This iterative process included further data cleansing, transformation, and model optimisation to ensure the system’s reliability.

Deployment and Measurement:

The fully integrated system was rolled out across all furnaces. Energy optimisation and carbon reduction initiatives were identified based upon the AI-driven insights generated. Detailed reports quantified the achievable energy savings, emissions reductions and cost savings with operational changes.

Results and Impact

The collaboration yielded significant, measurable results:

• Cost Savings: Equivalent to 17% of furnace energy costs, delivered without replacing existing infrastructure.
• Operational Flow Efficiency: 17% decrease in total annual energy consumption. Savings per furnace ranged from 4% to 25%.
• Carbon Reduction: 20% decrease in specific carbon emissions per part.

Energy-intensive industrial manufacturing is one of the hardest environments to optimise, complex operations, legacy infrastructure, and cost pressures that leave no room for inefficiency. This project demonstrates that Flow Intelligence, real-time visibility and anomaly detection layered above existing systems without hardware replacement, can deliver double-digit cost savings and carbon reductions across industrial manufacturing operations. It provides a scalable model for how operational intelligence can protect output, reduce waste, and lower cost exposure across any energy-intensive manufacturing environment

Operational efficiency and carbon reduction aren't competing priorities, they're the same problem. With the right flow intelligence layer, Lufthansa Technik didn't just cut emissions, they eliminated operational blind spots, reduced cost exposure, and built a foundation for continuous improvement. No hardware replacement required.

Ready to see what Flow Intelligence can deliver in your operations?

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