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manufacturing

Automotive Manufacturer: IoT Predictive Maintenance Platform

Deployed edge computing and ML models to predict equipment failures 2 weeks in advance, achieving 40% reduction in unplanned downtime and $8.5M in annual savings.

Duration
12 months
Team Size
15 engineers
Investment
$1M - $2M
Location
Detroit, USA

The Challenge

An automotive assembly plant with 500+ machines experienced 15% unplanned downtime, costing $50,000 per hour in lost production.

Our Solution

We implemented a comprehensive IoT platform with vibration, temperature, and current sensors on critical equipment. ML models trained on 5 years of failure data process 10 million data points daily.

Key Features Delivered

Predictive maintenance
IoT sensors
Real-time monitoring
ML-powered analytics

Results & Impact

40%

Reduction in Downtime

$8.5M

Annual Savings

92%

Prediction Accuracy

2 weeks

Advance Warning

The predictive maintenance system paid for itself in 4 months. We now schedule maintenance proactively.

James Wilson
Plant Director
Global Automotive Manufacturer

Technologies Used

TensorFlowApache KafkaTimescaleDBKubernetesGrafanaPython

Services Provided

Industry40Iot SolutionsAi Automation

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