AI AgentsManufacturingIoTPredictive Maintenance

Predictive Maintenance with AI & IoT — Preventing Equipment Failures Before They Happen

Industry: ManufacturingLocation: IndiaDuration: 14 weeksTeam: 4 engineers + 1 industrial specialist

“A manufacturing company was losing heavily to unplanned equipment failures. Production lines going down at random, emergency repairs, missed deadlines. We deployed IoT sensors and an AI system that predicts failures days in advance — dramatically reducing unplanned downtime and saving significant costs.”

The Results

~90%↓

Unplanned Downtime

Unplanned production stops reduced dramatically

Significant

Cost Savings

Major annual savings from prevented failures and optimized scheduling

90%+ accuracy

Prediction

Machine failure predictions with high accuracy, days in advance

~40%↓

Parts Inventory

Spare parts inventory reduced through just-in-time ordering

The Problem

What Was Going Wrong

The company operates multiple plants with hundreds of pieces of heavy machinery. Equipment failures were unpredictable and expensive — a single production line going down meant lost output, overtime repair costs, and contract penalties. The maintenance approach was entirely reactive.

Significant annual losses from unplanned equipment downtime

Dozens of unplanned production stops per quarter

Entirely reactive maintenance — fix it when it breaks

Large spare parts inventory sitting idle 'just in case'

Major client contracts at risk due to delivery delays from breakdowns

The Solution

What We Built

We deployed IoT sensors across critical machinery and built an AI system that continuously monitors vibration, temperature, pressure, and acoustic patterns to predict failures days in advance — giving the maintenance team time to schedule repairs during planned downtime.

Hundreds of IoT sensors across critical machines monitoring multiple parameters in real-time

Anomaly detection model trained on historical failure data and sensor readings

Multi-day predictive window — enough time to order parts and schedule maintenance

Automated work order generation with recommended procedures and required parts

Plant manager dashboard showing machine health scores and predicted failure timelines

Tech Stack

PythonTensorFlowApache KafkaInfluxDBGrafanaReactMQTTAWS IoT

The Transformation

Before vs After

Before

Dozens of unplanned stops per quarter

After

Rare unplanned stops

Before

Reactive: fix when broken

After

Predictive: fix before failure

Before

Significant downtime losses

After

Dramatically reduced losses

Before

Large idle spare parts inventory

After

Optimized just-in-time inventory

“The system flagged our main motor with no visible signs of trouble. We pulled it for inspection and found a bearing that would have seized within days. That's a massive save from one alert. The system pays for itself every month.”

— Plant Director

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