Predictive Maintenance Intelligence for Industrial Operations

A global industrial manufacturer was experiencing high unplanned downtime rates across three production facilities due to unpredictable equipment failures. Sequoia deployed an IoT-powered predictive maintenance platform that reduced unplanned stoppages by 50% and delivered $2.4M in annual operational savings.

The Challenge

Equipment failures on the production line were largely reactive. Maintenance teams responded to breakdowns rather than preventing them, resulting in unplanned stoppages that disrupted production schedules, strained maintenance staff, and created cascading delays across downstream operations.

The existing maintenance system relied on time-based inspection schedules that bore no relationship to actual equipment health. High-value assets were inspected on calendar cycles regardless of condition, meaning failures were neither predicted nor consistently prevented.

“We were replacing parts that didn't need replacing and missing failures we should have caught. We needed a system that could tell us what was actually happening inside our equipment in real time.”

— Director of Operations

Our Approach

Sequoia designed and deployed a three-layer predictive maintenance architecture across all three facilities:

• Sensor network deployment — 360+ vibration, temperature, and current sensors installed across 80 critical assets — motors, compressors, conveyor systems, and hydraulic units — streaming telemetry to a centralised data platform.
• ML failure prediction engine — Gradient boosting models trained on 18 months of historical failure data and sensor readings, producing per-asset failure probability scores with lead times of 48–96 hours.
• MES integration and alerting — Real-time dashboards and maintenance workorder triggers integrated into the existing Manufacturing Execution System, routing alerts directly to maintenance schedulers.

The Outcome

Maintenance teams shifted from reactive to planned interventions, with the system providing accurate failure forecasts that allowed parts and labour to be staged in advance. Equipment availability improved significantly across all three sites.

The platform is now expanding to cover secondary asset classes across the same facilities, with a roadmap for integration into quality inspection and energy monitoring workflows.

Published by the Sequoia Team