Cutting Costs without Cutting Corners.
Case Study: European Cement Manufacturer
At a Glance:
Industry:
Cement Production
Use Case:
Energy Efficiency & Predictive Optimization of Operations
Technologies Used:
- Machine Learning
- Pattern Recognition
- Knowledge Management
- Random Forest
Impact:
€18M in Annual Savings
40% Less Energy Used
The Challenge
As Europe’s leading cement manufacturer, the company set out to solve a complex operational challenge: how to drive profitability while keeping costs in check. With energy consumption accounting for nearly 30% of production expenses, it became clear that smarter energy use was key. To tackle this, the company launched a bold initiative-standardizing and optimizing procedures across its network of manufacturing sites to unlock efficiency at scale.
Our Approach
The Spur Insights team analyzed operational patterns across the company’s network of cement production sites. The goal: identify what separates high-performing plants from the rest and scale those advantages across the business.
The team’s methodology followed a clear, structured process:
- Step 1: Historical Analysis
Mathematical models were built to explain and predict mill behavior, drawing from past production and energy data. - Step 2: Optimization
These models were paired with real operator decisions, enabling a strategy that linked expert human judgment with machine-driven insight. - Step 3: Recommended Settings
Finally, an automated machine learning layer translated those insights into plant-wide recommendations-ensuring every operator had access to best-in-class decision support.
What we uncovered
Human Expertise Was the Edge
The analysis revealed that energy efficiency wasn’t driven by technology alone. The most efficient plants consistently had one thing in common: highly experienced operators. Their deep operational knowledge enabled them to make nuanced decisions that balanced energy savings with product quality-day in and day out.
Scaling Operational Wisdom
Rather than relying on chance distribution of talent, the Spur team focused on capturing and sharing expert behaviors. These insights became the foundation for a smart advisory system that supports less-experienced operators. By delivering clear, actionable recommendations-and the reasoning behind them-the system helps every site operate closer to its full potential.
The Results
€18M in Annual Savings: Optimized energy management across the European manufacturing network unlocked substantial cost reductions.
40% Less Energy Used: Select factories outperformed industry benchmarks, cutting energy consumption by 40%-all while maintaining full production volumes. The difference? Standardized application of expert operator practices, now scaled system-wide.
What’s Next?
Building on the success of energy optimization, the company plans to expand the Al advisor system to additional facilities across its global network. Continuous learning from operator feedback will further refine recommendations, keeping performance gains sustainable and future-ready.