OVERVIEW
Menz & Gasser, an 80-year-old premium fruit preserves manufacturer in northern Italy, partnered with Mipu to transform raw energy data into actionable intelligence. By implementing artificial intelligence-powered energy models across their biomass plant and production facilities, the food manufacturer moved from basic consumption monitoring to predictive energy management, enabling real-time efficiency optimization and proactive maintenance decisions.
INITIAL SITUATION
Founded in 1935 in Lana, South Tyrol, Menz & Gasser has built its reputation on high-quality fruit preserves and semi-finished products. Operating in over 50 countries across four continents, the food manufacturing company manages complex energy requirements spanning multiple energy vectors throughout its production processes.
The company faced a dual challenge in energy management. On one hand, they needed to efficiently manage the complexity of diverse energy sources required for food production operations. On the other, the ownership demonstrated strong commitment to rational energy use and sustainability, pushing for more than superficial improvements.
Despite having already installed numerous electrical and thermal consumption meters throughout their facilities, Menz & Gasser found that simple data storage and visualization weren’t delivering the insights needed. The collected data sat unused, representing a missed opportunity for systematic consumption reduction and meaningful sustainability improvements in their manufacturing operations. The company needed a solution that would transform their existing measurements into actionable intelligence for energy optimization.
THE CHALLENGE
Menz & Gasser’s primary objective was developing a comprehensive Energy Management System that could deliver continuous, measurable reductions in energy consumption while enhancing operational sustainability across their food manufacturing facilities.
The initial focus centered on a biomass wood plant within the facility, which presented significant technical complexity. Calculating efficiency proved challenging due to multiple, non-directly measurable energy contributions and losses, including flue gas losses, radiation losses, and energy content in ash residues.
The company required a system delivering immediate feedback on plant performance while providing predictive support for maintenance decisions and energy efficiency initiatives. Beyond technical requirements, the solution needed to create energy awareness and engagement across the entire organization, transforming energy management from a technical function into a shared cultural priority in the food industry context.
THE SOLUTION
Mipu and Menz & Gasser launched a pilot project focused on the biomass plant, establishing a foundation for enterprise-wide energy intelligence. The implementation centered on centralizing existing data streams and leveraging them to create behavioral energy models, AI-powered simulations predicting how each major system component should perform under varying operational conditions.
The technical approach employed machine learning algorithms when complexity demanded it, while using mathematical models for simpler scenarios. Critically, Mipu’s platform abstracted the computational complexity, enabling Menz & Gasser’s technical team to create and manage models autonomously without writing code, a key factor in ensuring long-term sustainability of the artificial intelligence solution.
The team initially developed approximately ten energy models covering critical plant components. These models served three strategic functions: predicting future energy consumption based on technical, environmental, and production variables; enabling what-if scenario analysis for cost forecasting based on factors like scheduling, weather conditions, and planned maintenance; and detecting malfunctions early through energy consumption signals, what Mipu terms Energy-Based Reliability Management.
Each model operates under continuous monitoring through control charts that automatically detect deviations between actual and expected behavior. Positive drift indicates potential equipment issues requiring investigation, while negative drift signals efficiency improvements worth analyzing and stabilizing. This approach quantifies the exact impact of any activity on energy performance, communicating results simply and visually to the entire team and potentially to external stakeholders.
Following successful validation on the biomass plant, Menz & Gasser extended the energy intelligence framework across their manufacturing facilities, scaling the artificial intelligence application throughout their food production operations.
RESULTS
The implementation delivered transformative capabilities for energy management in Menz & Gasser’s food manufacturing operations, though specific quantitative savings weren’t detailed in the source materials.
The artificial intelligence models enabled development of dynamic energy budgets calibrated to actual operating conditions, rather than static historical averages. This capability allows the energy management team to evaluate the economic and environmental impact of operational decisions before implementation, facilitating data-driven discussions with all stakeholders using rigorous yet immediately understandable metrics.
The control chart system provides real-time visibility into energy performance across the facility, enabling rapid identification and quantification of both efficiency improvements and emerging equipment issues. This transforms energy data from a lagging indicator reviewed monthly into a live operational signal supporting daily decision-making.
Perhaps most significantly, the models became what Menz & Gasser terms an “engine of shared knowledge”, helping the organization understand their industrial systems more deeply, make strategic choices with confidence, and continuously evaluate the technical, economic, and environmental impact of their decisions. The solution successfully bridged the gap between energy management as a specialized technical function and energy consciousness as an organization-wide cultural value in their food and beverage operations.
