TPM Integration

Suppliers

• •IoT sensors (vibration, temperature, pressure, power quality) embedded on production equipment that stream asset health data in real time to the central TPM platform.
• •MES and ERP systems providing production schedules, work orders, downtime events, and equipment genealogy to correlate maintenance actions with production impact.
• •Maintenance technician teams and operators contributing historical failure records, FMEA/RCM analysis documents, and validated spare parts inventory data.
• •Equipment OEM documentation including maintenance manuals, predictive thresholds, and recommended autonomous maintenance checklists for each asset class.

Process

• •Automated ingestion and normalization of sensor data streams, production events, and maintenance records into a unified data lake that enables cross-system correlation.
• •Real-time OEE calculation engine that disaggregates losses into the 6 Big Losses (equipment failure, setup/changeover, minor stops, reduced speed, startup losses, quality defects) with auditable logic and sensor validation.
• •Predictive degradation models that analyze sensor trends against equipment-specific FMEA decision trees and alert operators to perform autonomous maintenance tasks before functional failures occur.
• •Dynamic digital checklists and guided work instructions delivered to operator tablets that embed TPM pillar workflows (Autonomous Maintenance steps, quality checks, safety verifications) with real-time sensor feedback and task completion validation.
• •Root cause analysis workflow that automatically links downtime events to specific 6 Big Losses categories, captures technician findings, and triggers improvement initiatives when chronic loss patterns emerge.

Customers

• •Production supervisors and shift leads who consume daily OEE dashboards, autonomous maintenance alerts, and predictive maintenance recommendations to optimize equipment availability and plan preventive actions.
• •Equipment operators who receive guided autonomous maintenance checklists, sensor-driven task triggers, and real-time feedback on checklist completion and equipment health status.
• •Maintenance technicians and planners who access prioritized work orders ranked by FMEA risk level, receive predictive failure alerts, and validate OEE loss categories to refine maintenance strategy.
• •Continuous improvement and engineering teams who analyze 6 Big Losses trends, OEE root cause reports, and equipment degradation patterns to identify high-impact improvement projects and validate engineering changes.

Other Stakeholders

• •Plant management and finance teams who benefit from auditable, real-time OEE metrics and documented ROI from reduced downtime, extended equipment life, and faster problem resolution.
• •Quality assurance function that leverages OEE defect loss data and equipment condition insights to correlate maintenance events with product quality excursions and prevent escapes.
• •Human resources and training teams who use autonomous maintenance task completion data and skill assessments to identify knowledge gaps and design targeted operator development programs.
• •Supply chain and procurement teams who optimize spare parts inventory based on predictive failure forecasts and actual maintenance consumption patterns embedded in the TPM system.