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Manufacturing

Predictive maintenance, quality control, and supply chain optimization

Predict equipment failures, automate quality inspection, and optimize supply chain logistics — with real-time monitoring, automated retraining on sensor drift, and What-If scenario testing.

The Challenges

Unplanned Downtime

Equipment failures halt production lines, costing tens of thousands per hour in lost output and emergency repairs.

Quality Variance

Subtle process parameter shifts produce defective products that are expensive to rework or scrap.

Sensor Data Volume

IoT sensors generate massive time-series streams that must be processed, cleaned, and featurized for model training.

Supply Chain Disruptions

Demand fluctuations and supplier delays require dynamic inventory optimization and lead time prediction.

How CorePlexML Helps

AutoML

Predictive Maintenance

AutoML trains on historical sensor and maintenance records to predict failures before they occur.

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MLOps

Real-Time Monitoring

MLOps monitors model predictions against sensor streams with drift detection and automated alerting.

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ML Studio

Process Optimization

ML Studio lets engineers test parameter changes (temperature, pressure, speed) and see predicted quality impact.

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Dataset Builder

Sensor Data Pipeline

Dataset Builder handles time-series feature engineering, lag creation, rolling aggregations, and outlier cleanup.

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SDK Example

example.py
from coreplexml import CorePlexMLClient

client = CorePlexMLClient(
    base_url="https://api.coreplexml.io",
    api_key="sk_your_api_key"
)

# Upload sensor data
dataset = client.datasets.upload(
    project_id="proj_maintenance",
    file_path="sensor_readings.csv",
    name="Assembly Line A Sensors"
)

# Train predictive maintenance model
experiment = client.experiments.create(
    project_id="proj_maintenance",
    dataset_version_id=dataset["dataset_version_id"],
    target_column="failure_within_7d",
    max_models=20
)

# Deploy with drift monitoring
deployment = client.deployments.create(
    project_id="proj_maintenance",
    model_id=experiment["leader_model_id"],
    strategy="direct"
)

Expected Impact

42%
Downtime Reduction
98.5%
Defect Detection Rate
35%
Maintenance Cost Savings
22% leaner
Inventory Optimization

Ready to get started?

Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.

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