Overview
Delivered scalable AI and automation solutions across global industrial clients, improving operational reliability, safety, and predictive insights through advanced analytics and machine learning.
Understanding the Problem
Industrial clients faced costly unplanned downtime and reactive maintenance strategies. Traditional maintenance schedules were inefficient, leading to both over-maintenance (unnecessary costs) and under-maintenance (unexpected failures). Lack of predictive insights prevented proactive decision-making.
Building the Right Approach
Architected and implemented end-to-end predictive maintenance solutions leveraging IoT sensor data, machine learning models, and real-time analytics. Developed anomaly detection algorithms to identify equipment degradation patterns. Created dashboards for maintenance teams to prioritize interventions based on predicted failure probabilities. Integrated solutions with existing SCADA and MES systems for seamless operations.
Measurable Results
Reduced unplanned downtime by 30-40% across client facilities
Decreased maintenance costs through optimized scheduling
Improved equipment lifespan through early intervention
Enhanced safety by predicting critical equipment failures
Enabled data-driven decision making for maintenance teams
Achieved measurable ROI within 6-12 months
Tools & Platforms
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