Reducing Unplanned Downtime Across Multi-Site Food Manufacturing with Predictive Maintenance
Customer profile
Industry
Food manufacturing
Footprint
Two large, high-throughput production sites
Operations
Multiple continuous production lines with frequent changeovers
Maintenance maturity
Combination of preventive maintenance and manual vibration inspections
The challenge
Unplanned downtime was a persistent and costly issue across both sites. Each production line outage carried a significant hourly cost, driven by lost throughput, product waste, and recovery time. Despite existing maintenance practices and inspections, many equipment failures were still being detected too late, often only once performance had already degraded or a breakdown had occurred.
- High cost of downtime on critical production lines
- Manual vibration inspections that were time-consuming and infrequent
- Hidden failure modes that developed between inspections
- Difficulty prioritising maintenance work, especially during busy production schedules
- Frequent product changeovers, making it harder to distinguish normal vs abnormal behaviour
The approach
The customer deployed Factory AI's predictive maintenance platform across a growing set of critical assets at both sites. Factory AI was positioned to complement existing maintenance practices, not replace them overnight, helping teams transition from periodic inspection to continuous condition awareness.
- Continuous vibration and temperature monitoring on motors, drives, conveyors, fans, pumps, and rotating equipment
- ML-based anomaly detection tuned to each asset's operating behaviour
- Actionable alerts that highlighted emerging issues rather than static thresholds
- Engineer feedback loops so teams could confirm findings and improve model accuracy over time
Factory AI helped us identify developing faults early enough to plan work, secure spares, and avoid unplanned downtime. It's shifted us from reacting to failures to acting with confidence.
Reliability Engineer, Arnotts
Results
Over a 12-month period, the platform generated approximately 80 actionable predictive alerts, many identifying developing issues days or weeks before failure.
Actionable Predictive Alerts
~80
Generated over 12 months across both sites
Avoided Downtime
~$500,000
Customer-estimated, based on confirmed true positives
Average Monthly Savings
~$21,000 per site
Based on site-specific downtime cost models
Average Monthly Platform Cost
~$2,400 per site
Clear positive ROI from high-impact interventions
1. Early fault detection and avoided failures
- Collapsing bearings causing increased drag and motor overheating
- Over-tensioned belts following preventive maintenance
- Progressive vibration increases indicating wear in conveyor drives
- Lubrication-related issues detected through opposing vibration and temperature trends
- Loose bearing sleeves identified before catastrophic motor failure
- In multiple cases, teams inspected assets before failure, planned work during scheduled windows, secured spares in advance, and prevented secondary damage
2. Measurable financial impact
- Customer-estimated ~$500,000 in avoided downtime over 12 months
- Average monthly savings of ~$21,000 per site
- Average monthly platform cost of ~$2,400 per site
- Strong positive ROI driven by a relatively small number of high-impact interventions
- Estimates calculated by reliability engineers using historical downtime data and typical failure impacts
3. Improved maintenance decision-making
- Better root cause analysis supported by historical trends
- Increased confidence in prioritising work orders
- Reduced reliance on blanket inspection routines
- Faster response times when anomalies appeared
- Improved collaboration between reliability, maintenance, and operations
- Alerts helped teams reframe strategy by introducing new preventive tasks and adjusting lubrication practices
Challenges and learnings
- Frequent product changeovers initially increased false positives as models required more data to establish stable baselines
- Teams needed clear alert response workflows to ensure consistent action
- Engineer feedback proved critical in refining accuracy and trust
- With a longer operating baseline, both sites are now seeing fewer false positives and higher signal confidence
What's next
- One site plans to more than double the number of monitored assets
- The second site is expanding coverage across additional production lines
- The customer continues to refine alerting and workflows with Factory AI
- Ongoing site-specific feedback loops are being used to maximize predictive value
Why it matters
- Delivers rapid ROI
- Reduces unplanned downtime without increasing maintenance burden
- Moves teams from reactive to proactive decision-making
- Scales across sites with different operating characteristics
- Protects throughput and helps teams plan work intelligently with greater confidence
Want results like this in your plant?
Book a tailored walkthrough and see how Factory AI can surface faults earlier, reduce downtime, and improve maintenance planning.