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CASE STUDY

Smart Manufacturing IoT Platform

Centralized predictive maintenance monitoring across 20+ automated factories.

Industry: Manufacturing
Region: Global
Services: Cloud, DevOps
Duration: 15 months
BUSINESS IMPACT
-65%
↗
Downtime
+40%
↗
OEE Improvement
10K+
↗
Sensors Connected
95%
↗
Maintenance Prediction

Overview

A leading Manufacturing company, with millions of daily transactions, faced challenges with scalability, system reliability, and high infrastructure costs. Their legacy infrastructure limited innovation speed and operational efficiency.

We designed and implemented a secure, scalable, and cost-optimized cloud architecture on AWS/Cloud Platform, enabling them to focus on innovation and customer experience.

The Challenge

  • -A manufacturing company was losing ₹5 crores annually to unplanned equipment failures.
  • -With 20+ factories and thousands of machines, they had no way to predict failures or monitor production efficiency in real-time.

Our Solution

  • +We established a centralized IoT lakehouse, capturing telemetry from 10,000+ machine sensors and applying prediction algorithms (TensorFlow) to forecast mechanical failures.

The Outcome

  • ★-65% downtime achieved
  • ★+40% oee improvement achieved
  • ★10K+ sensors connected achieved
  • ★95% maintenance prediction achieved
  • ★Enhanced security and monitoring metrics

Technology Stack

Python
FastAPI
MQTT
InfluxDB
Apache Spark
TensorFlow
Multi-Cloud

Solution Architecture

Hover over the systems node modules to trace request processing connections.

SECURED CLOUD BACKEND PLATFORMGateway NodeMicroservice ClusterDatabase CacheCLIENTSGLOBAL CDNPROXY GATEAPI GATEWAYAPP CONTROLLERPRIMARY STOREREDIS CACHESYSTEM METRICS MONITORING & AUDIT LOGGING TELEMETRY PIPELINE
“

"Deploying predictive maintenance models across 20+ automated factories reduced equipment breakdowns by 65%. Unplanned downtime is no longer a threat to our bottom line."

HS

Hari Sen

Head of Factory automation

Key Results

  • Infrastructure cost reduced by 95% through right-sizing.
  • Achieved +40% system availability across multi-AZ clusters.
  • Deployment release schedules reduced from hours to minutes.
  • Improved overall telemetry visibility and incident response cycles by 60%.
  • Strengthened corporate compliance perimeters with industry best practices.

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