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Manufacturing Industry Solution

Autonomous Predictive Maintenance

Unplanned equipment failures cause costly assembly line halts and supply chain ripple effects. Focus20 designs Edge-to-Cloud Agentic AI frameworks that actively monitor IIoT sensor telemetry (vibration, acoustics, temperature) to predict mechanical degradation before it impacts your yield.

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Business Value

Zero Unplanned Downtime

Transitioning from reactive 'break-fix' structures to an autonomous self-healing factory model completely shifts the economics of manufacturing, preserving machine life cycles and saving thousands per hour in halted production.

35%
Downtime Reduction
20%
Lower Maintenance Cost

The Agentic Workflow

1. Agentic Stack

Edge Agent: AWS IoT Greengrass Lambda.
Time-Series Agent: Amazon Timestream query.
PLC Controller: Modbus/TCP API mutator.

2. Reasoning Loop (ReAct)

> Vibration sensor detects degrading bearing
> Edge Agent confirms anomaly against historical baseline
> Triggers "Soft Degradation" workflow
> Reprograms adjacent robotic cell to absorb 30% load
> Creates automated SAP Ticket with exact part ID

Enterprise Technical Architecture

Merging operational technology (OT) with modern cloud intelligence (IT) on AWS.

Edge & Cloud Integration

  • Ingestion: AWS IoT Core handles secure MQTT brokering from thousands of industrial sensors.
  • Edge Compute: AWS IoT Greengrass provides localized machine learning inference on the factory floor, ensuring zero latency decisions.
  • Time-Series: Amazon Timestream is used for optimized storage of high-frequency chronometric telemetry.
  • Autonomous Actions: The Maintenance AI Agent seamlessly schedules repair downtime directly inside SAP.

Cloud Migration Highlight

Migrated vulnerable legacy Windows Server SCADA systems to AWS IoT Core + S3 Data Lake, drastically reducing technical debt and mitigating severe on-premise ransomware risks.

graph LR MC[Industrial Machines] -->|MQTT| IoT[AWS IoT Core] IoT --> TS[(Amazon Timestream)] IoT --> GG["IoT Greengrass
Edge ML"] TS --- SM["SageMaker
Anomaly Det."] SM --> AG[Maintenance Agent] AG -->|Create Ticket| SAP[SAP Plant Maint.] GG --> AG

Deployment Timeline

Transforming your factory floor continuously, safely, and securely.

Phase 1: Weeks 1 - 3

IoT Ingestion & Baseline

Sensors are bridged to AWS IoT Core securely. We harvest several weeks of high-frequency vibration/temperature data to establish a healthy baseline profile within Amazon Timestream.

Phase 2: Weeks 4 - 8

Cloud ML Predictions

SageMaker anomaly models begin predicting degradation. Initially, these insights generate alerts directly to Plant Managers dashboards, validating the AI's accuracy against actual physical inspections.

Phase 3: Weeks 9 - 12

Greengrass Edge Autonomy

The validated inference model is compiled and deployed directly onto factory machinery via AWS Greengrass. The agent is granted permission to perform automated "soft shutdowns" and create SAP work orders.

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