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Manufacturing – Production KPI Monitoring Automation.html

Architecture Level 1

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System Context

Architecture Level 2

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Container View

Architecture Level 3

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Component Flow Diagram

Production KPI Monitoring Automation

1. Executive Summary

The Production KPI Monitoring Automation system is a real-time manufacturing intelligence platform that ingests machine telemetry and downtime events from IoT sensors and PLCs, processes them through Azure Event Hub → Power Automate → Power BI, and delivers live dashboards plus instant alerts when thresholds are breached. Deployed across multiple production lines, it reduced unplanned downtime by 30 %, achieves sub-5-second latency, handles 1,000+ events/second per second per line, and was delivered in 10 weeks (1 Sep – 19 Nov 2025).

2. Architecture Overview

Event-driven, fully cloud-native pipeline on Azure:

Ingestion: IoT devices / PLCs (MQTT or OPC UA) connect to Azure IoT Hub for device registry and routing.

Streaming: Azure Event Hub manages high-throughput partitioned streams for metrics (OEE, yield) and events (faults).

Rule Engine: Power Automate and Azure Logic Apps handle workflow orchestration and threshold alerting.

Visualization: Power BI streaming datasets provide real-time visuals with automatic scaling and role-based access via Azure AD.

3. Technology Stack

  • Cloud Infrastructure: Azure IoT Hub & Event Hub
  • Automation Logic: Power Automate & Azure Logic Apps
  • BI & Analytics: Power BI Pro/Premium (Streaming Datasets)
  • Protocols: MQTT, OPC UA, REST adapters
  • Notifications: Teams, Outlook, SMS Connectors
  • Observability: Azure Monitor & Log Analytics
  • Security: Azure Key Vault & Azure AD

4. Automation Model and Features

Continuous Ingestion: Real-time tracking of cycle time, temperature, vibration, and machine counters.

KPI Calculation: Live OEE, throughput, scrap rate, and downtime duration monitoring.

Escalation Matrix: Configurable alerts (e.g., OEE < 85%) trigger Teams cards and SMS to supervisors.

Advanced Reporting: Interactive dashboards with shift-wise drill-downs and Pareto charts for root-cause analysis.

Performance: <5-second end-to-end latency with 99.7% data accuracy.

5. Data Processing

Devices authenticated via IoT Hub route data to Event Hub topics. Power Automate or Stream Analytics jobs parse the JSON, enrich it with machine master data, and apply business logic. The processed data is pushed to Power BI streaming datasets while simultaneously triggering conditional alert actions based on plant-specific partitions.

6. Project Timeline (10 Weeks)

Timeline: September 1 – November 19, 2025

  • Weeks 1–2: Discovery and KPI definition with plant engineers.
  • Weeks 3–4: Architecture design and Power BI prototype dashboards.
  • Weeks 5–8: Build ingestion pipelines, threshold rules, and live dashboards.
  • Week 9: UAT on a pilot line with 10k events/min simulated data.
  • Week 10: Factory-wide rollout, staff training, and handover.

7. Testing & Deployment

Testing: Unit testing for connectors; Load testing using Azure Load Testing; UAT verified at 99.7% accuracy against manual records.

Deployment: Managed via Azure Resource Manager (ARM) templates with blue-green workspace switching. Rollback capability in <10 minutes by reverting to legacy manual logging.

8. Monitoring & Maintenance

Azure Monitor tracks ingestion lag and throughput units, sending alerts to a dedicated Teams channel. Maintenance includes quarterly threshold recalibration and version-controlled updates via Git. Current production uptime: 99.98%.

9. Roles & Responsibilities

Methodology: Azure DevOps boards with mandatory peer reviews.

  • 🚀 Project Manager: Timeline, stakeholder alignment, and budget.
  • ⚙️ Cloud/IoT Engineers (2): Azure infra, device provisioning, and pipelines.
  • 📊 Data Analyst: Dashboard design, DAX measures, and streaming datasets.
  • 🛠️ Manufacturing Engineer: KPI definitions and threshold tuning.