OVERVIEW
A global cocoa producer operating in the Food & Beverage industry, with 60 facilities across more than 40 countries, partnered with Mipu to implement an AI-powered predictive maintenance solution on their critical industrial sterilizer line. By deploying machine learning models integrated with real-time IoT sensors, the company was able to detect equipment anomalies before failure. This data-driven approach enabled planned maintenance interventions, significantly reducing unplanned downtime, improving asset reliability, and avoiding unnecessary maintenance activities that generated waste and excess costs.
INITIAL SITUATION
As one of the world’s largest cocoa producers serving the B2B Food & Beverage market, the client had launched a global digitalization initiative aimed at improving operational reliability and efficiency. Within this program, the cocoa paste sterilizer line was identified as a critical production bottleneck, with a high risk of causing extended unplanned downtime and material waste.
Maintenance operations were primarily based on scheduled, time-based maintenance strategies. This created two recurring issues for Maintenance Managers and Reliability Engineers. On one hand, components were often serviced or replaced before their actual end of life, increasing maintenance costs and generating avoidable production interruptions. On the other hand, despite regular maintenance, unexpected equipment failures still occurred, leading to unplanned production stops that disrupted the entire manufacturing flow.
The steam consumption system of the industrial sterilizers represented a major challenge. Performance variations were difficult to identify using traditional monitoring tools, and when deviations became visible through standard KPIs, component degradation was already advanced. This frequently required emergency maintenance actions. Maintenance and production teams needed a more reliable predictive maintenance system capable of monitoring asset health with greater precision, improving reliability while reducing waste and unnecessary interventions.
THE CHALLENGE
The primary objective was to develop an AI-based predictive maintenance solution to monitor steam consumption across the sterilizer line and support Maintenance Managers and Reliability Engineers in early fault detection. The system needed to distinguish normal operational variability from real anomalies indicating potential equipment failure.
From a technical perspective, the challenge was complex. The solution had to model non-linear physical parameters such as temperature and pressure across multiple interconnected industrial sterilizers. It also needed to integrate seamlessly with existing Food & Beverage production equipment while delivering real-time monitoring and actionable insights.
Time-to-value was a critical factor. The client required a validated, production-ready predictive maintenance system capable of quickly demonstrating measurable improvements in reliability and waste reduction, supporting the business case for scaling digitalization across their global facilities.
THE SOLUTION
Mipu assembled a cross-functional team combining expertise in industrial engineering and machine learning. This mix was essential to build an AI-powered predictive maintenance system aligned with both advanced data modeling requirements and the real operational constraints of industrial Food & Beverage production.
Discovery and Modeling Phase
The project started with close collaboration between Mipu’s data scientists and the client’s maintenance and reliability teams. Together, they modeled the physical behavior of the sterilizer components and steam consumption system. Historical maintenance data was analyzed to understand failure patterns, while each sensor variable was mapped within the broader production process to ensure the AI model reflected real operational dynamics.
Technical Implementation
Mipu implemented a feedforward neural network trained on historical and real-time industrial data to predict steam consumption across the sterilizer line. This algorithm was chosen for its effectiveness in modeling complex, non-linear industrial systems common in the Food & Beverage industry.
The AI model was integrated with the Rebecca IoT platform, enabling continuous real-time data acquisition from sensors installed along the sterilizer line.
Predicted steam consumption values were continuously compared with actual measurements. Deviations beyond expected thresholds were automatically flagged as potential equipment anomalies. All insights were made available through the Rebecca Artificial Intelligence platform, featuring intuitive dashboards designed for Maintenance Managers and Reliability Engineers, without requiring advanced data science skills.
Validation Process
Before full-scale deployment, the predictive maintenance model was validated using one month of real-time production data. This ensured reliability, accuracy, and robustness under real operating conditions.
RESULTS
The validated AI predictive maintenance model achieved an R² accuracy of 87%, with predicted steam consumption values deviating by no more than 11% from actual measurements. Despite data being collected at different time intervals across multiple variables, the model demonstrated strong and consistent predictive performance.
Operational Impact
Once deployed in production, the system promptly identified anomalies in two industrial sterilizers. These early warnings allowed the maintenance team to schedule targeted interventions before failures occurred, directly preventing unplanned downtime and production losses.
Key Benefits Achieved:
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Reduced unplanned downtime through early anomaly detection and proactive maintenance
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Improved reliability of critical Food & Beverage production assets
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Lower maintenance costs by avoiding unnecessary component replacements and emergency repairs
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Reduced waste by aligning maintenance actions with actual equipment condition
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Enhanced maintenance planning with data-driven scheduling instead of fixed calendars
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Greater operational visibility through real-time dashboards accessible to Maintenance Managers and Reliability Engineers
This project demonstrated that even in the early stages of digital transformation, focused AI-powered predictive maintenance applications on critical industrial assets can significantly improve reliability, reduce waste, and create a solid foundation for scalable digitalization across global Food & Beverage operations.
