MANUFACTURING
Connected manufacturing platform processing 2M+ sensor readings daily with predictive maintenance AI across 6 global plants.
A global manufacturer with 6 production facilities across 3 continents was losing $52M annually to unplanned equipment downtime. Their maintenance approach was reactive — they fixed things when they broke. Attempts to implement predictive maintenance had failed because their operational technology (OT) and information technology (IT) systems were completely disconnected. Sensor data existed but was trapped in proprietary SCADA systems with no path to analytics. The operations team and IT team spoke different languages and had different priorities.
Fleet Studio was engaged to bridge the OT/IT divide and build a unified platform that could ingest sensor data, apply predictive analytics, and deliver actionable insights to plant operators. We started with a single plant as a proof of value, designed the platform for multi-plant scale, and then rolled out globally.
Designed edge computing architecture that processes sensor data locally (low-latency) while streaming aggregated data to the cloud for analytics
Built protocol adapters for 7 different SCADA/PLC systems (Siemens, Rockwell, ABB, etc.)
Developed predictive maintenance models trained on 18 months of historical failure data
Created real-time equipment health scores and maintenance recommendations visible to plant operators
Implemented anomaly detection that identifies degradation patterns 2-3 weeks before failure
Built the platform on a federated architecture — each plant runs independently but reports centrally
Reduction in unplanned downtime ($12M annual savings)
Sensor readings processed daily across 6 plants
Predictive accuracy for critical equipment failures (14-day advance warning)
Overall equipment effectiveness (OEE) improvement
Time to connect all 6 plants and reach operational status
Both teams needed to understand why they should care about each other's constraints. Operations needed reliability and low-latency decision-making. IT needed to support that without owning the factory floor.
Operations teams are conservative for good reason — downtime costs money. A single successful plant became a reference point that made global rollout possible.
You can't depend on cloud connectivity for real-time control decisions. Local processing handles the real-time work; cloud handles the analytics and insights.
A critical insight: the system had to be resilient to the reality of global manufacturing — internet connectivity is not guaranteed, especially in remote facilities.
Whether you're looking to reduce downtime, improve OEE, or bridge your OT/IT gap, let's discuss how predictive analytics can transform your operations. We'll assess your current state, identify the highest-impact improvements, and outline a path to measurable value.