case-7
Case Study
Automated failure prediction to prevent unexpected production downtime
Up to 14 days early detection of high risk machines
Significant reduction in unplanned line stoppages
Predictable maintenance windows replacing reactive firefighting
About the Customer
The client is a global producer in the food and beverage sector, operating large scale manufacturing lines that run continuously and generate substantial machine, maintenance, and operational data.
Although this information existed, it was not yet used to anticipate failures or stabilise production.
Region
Western Europe
Company size
Enterprise
Industry
Production
Use Cases
Predictive maintenance
Features
- Predictive servicing
- Scheduled maintenance planning
- Spare parts forecasting
- Machine condition monitoring
- Continuous model learning
Technologies
- Fabric
- Azure ML
- Power BI
The client sought to move from reactive maintenance to a data driven predictive approach, so ARP Ideas delivered an automated machine learning solution that identifies high risk machines early and supports planned maintenance before failures occur.
Challenge
The maintenance team was forced to respond to frequent, unpredictable breakdowns that caused significant unplanned downtime across multiple production lines.
Solution
Automated predictive maintenance workflow built on Microsoft Fabric, Azure ML/AutoML, and Power BI, using historical failures and machine load data to forecast which machines require preventive servicing.
Outcomes
The client gained a measurable reduction in unexpected line stoppages through early risk detection, enabling maintenance teams to plan ahead and keep production running consistently.
- Early risk visibility – clear identification of machines likely to fail in the next 7 to 14 days
- Stabilised production continuity – fewer mid-shift interruptions and smoother production flow
- Maintenance workload control – predictable service windows instead of alarm-driven reactions
- Parts availability planning – procurement aligned with upcoming preventive tasks
- Continuous model improvement – technician feedback loop that increases prediction accuracy
Details
The client operates dozens of production lines with continuous output, each generating machine failure logs, downtime records, and service actions. While this information was captured, maintenance teams had to react to breakdowns without any early indicators, which resulted in operational disruption and production losses. The workdays were shaped by alarms, emergency responses, and shifting priorities.
ARP Ideas helped the client use this data more strategically. The process began by consolidating historical events: which machines failed, which components were affected, when failures occurred, and how long the resulting downtime lasted. This was paired with machine load data to understand operational intensity before each incident.
Microsoft Fabric served as the unified data platform, allowing all logs, sensor readings, and maintenance records to be stored and processed consistently. Using Azure ML and AutoML, ARP Ideas trained a model that predicts the probability of specific machines or components failing within the next 7 to 14 days. The outcome is a prioritised list of machines with elevated risk, guiding technicians on what to service during the next available maintenance window.
The system also integrates technician feedback. After conducting preventive work, technicians record what was replaced or adjusted, enabling continuous model refinement and improved accuracy over time. This creates a closed feedback loop that strengthens predictions with each maintenance cycle.
Power BI dashboards then present clear risk indicators, machine conditions, and upcoming service recommendations. Maintenance teams gain early visibility into failure patterns, and production teams enjoy fewer disruptions. This approach also allows earlier planning of spare parts procurement, reducing the need for urgent purchases and helping stabilise inventory.
The solution can start with a pilot on one or two production lines and scale to the entire plant as value is proven. Ultimately, the client transitions from a reactive model to a proactive, data driven maintenance strategy that improves productivity, reduces operational stress, and enhances equipment availability.
If your organisation aims to increase equipment availability, avoid costly production stops, and replace reactive maintenance with data driven automation, we are here to help. Get in touch to discover how predictive insights can strengthen your operations.
