Reactive maintenance is a business risk — not a strategy
Unplanned downtime, rising costs, and increasing asset complexity are elevating operational risk across manufacturing.
Why predictive maintenance changes the risk profile
Automates data sharing in desired workflows
Automates data sharing in desired workflows
Automates data sharing in desired workflows
Maintenance Inefficiencies Impact Cost, Risk, and Performance
Unplanned downtime
Can account for up to 20% of total production cost, directly impacting profitability
Suboptimal asset performance
Inefficient operations increase energy consumption, emissions, and sustainability risk
Poor maintenance timing
Intervening too early or too late leads to unnecessary cost and avoidable fa
Inefficient use of engineering capacity
Engineers spend ~21% of their time on travel and on-site checks instead of value-added work
Limited scalability of PdM initiatives
Narrow PoCs make ROI difficult to validate and slow enterprise-wide adopt
What is Senseye Predictive Maintenance?
Automated asset intelligence focused on business outcomes.
Senseye Predictive Maintenance is an automated, scalable asset intelligence solution that predicts failures, prioritizes maintenance actions, and directs teams to where intervention creates the greatest business impact — improving uptime, reducing risk, and lowering cost.
Outcome-driven, scalable across assets, sites, and regions.
Maintaining Uptime
Precise & Correct Maintenance
Reducing Risks and Operating Costs
Supporting Remote Work
Increasing Sustainability
How Senseye Learns & Understands Normal vs Abnormal
From Insight to Action: Senseye Attention Index
Senseye translates complex asset signals into a clear, prioritized view of where maintenance attention is needed.
Combining Machine Intelligence with Human Expertise
Senseye continuously learns from both machine data and maintenance experience. This creates a feedback loop that improves predictions and prioritization over time.
End-to-End Predictive Maintenance Architecture
A scalable architecture that connects factory data, enterprise systems, and cloud-based predictive intelligence.
Sensors, machines, and operational signals
Data acquisition, preprocessing, and secure communication
Senseye Predictive Maintenance for automated learning and prioritization
CMMS / EAM integration for work orders, notifications, and maintenance events
Senseye integrates seamlessly with existing factory systems, sensors, and CMMS/EAM platforms. Machine data is processed securely from the shop floor to the cloud, where predictive intelligence continuously analyzes asset behavior and delivers prioritized insights back to maintenance and operations teams.
Proven Business Impact of Senseye Predictive Maintenance
Delivering measurable ROI through reduced downtime, optimized maintenance, and improved asset performance.
Operational Impact
- Up to 50% reduction in unplanned downtime
- 20% increase in mean time between failures (MTBF)
- 18% improvement in overall equipment effectiveness (OEE)
Financial Impact
- 25% reduction in maintenance costs
- Cost in avoided downtime at a single site
- ROI achieved in less than 3–7 months
Organizational Impact
- 15% increase in maintenance team productivity
- No additional FTEs required to scale globally
- Improved safety, compliance, and sustainability outcomes
From predictive insight to financial impact — at enterprise scale.
Step to Implement Senseye Predictive Maintenance
How much money is spent on maintenance?
How much money can be saved by Senseye implementation?
Step to Implement Senseye Predictive Maintenance
Global Automotive Manufacturer
- ROI in <3 months
- Achieved a 50% reduction in downtime
- Several tens of $m downtime avoided to date at a single site
- Global deployment – over 10,000 machines and 650+ users
Leading Corrugated Packaging Company
- Expanding globally
- Senseye Predictive Maintenance was the only solution tested that was able to achieve a downtime reduction
- 20+ different types of machines
- Working with retrofitted sensors on legacy machines
Global Steelmaking Producer
- ROI in <6 months
- Using data collected from Siemens controllers & retrofitted sensors
- Used directly by maintenance staff, no data scientists necessary
