Real-time monitoring and logging of production KPIs

Purpose

1.1. To automate and streamline real-time monitoring and logging of production KPIs for precision optical products manufacturing.
1.2. To enable automated, continuous collection, aggregation, and reporting of data on cycle times, yield, defect rates, downtimes, and throughput across production lines.
1.3. To ensure automated alerts for anomalies, supporting rapid interventions and root cause analysis.
1.4. To automate integration of machine data, ERP, and MES systems for transparent corporate-level performance analysis.

Trigger Conditions

2.1. Automated polling of IoT sensor data in production machinery at configurable intervals (e.g., every 10 seconds).
2.2. Automated webhook triggers from MES/ERP on status changes, such as batch completion or defect detection.
2.3. Scheduling automations to run at shift changes or end-of-day for summary reports.
2.4. Automated event-based triggers when KPI thresholds (e.g., yield drops below 97%) are breached.

Platform Variants

3.1. AWS IoT Core
• Feature/Setting: Automated MQTT data ingestion and stream analytics; configure "Device Data Stream" with rule actions for DynamoDB integration.
3.2. Microsoft Power Automate
• Feature/Setting: Automate with "When a row is added/modified in SQL Server"; auto-log KPI data to Teams.
3.3. Google Cloud Pub/Sub
• Feature/Setting: Automated topic subscription for production event logs; configure "Push Subscription" for real-time notifications.
3.4. Siemens MindSphere
• Feature/Setting: Configure "Asset Health Monitoring" for automated KPI logging and real-time event triggers.
3.5. Ignition by Inductive Automation
• Feature/Setting: Automated Tag Change Scripts to capture KPI shifts; configure "Tag Historian" for logging.
3.6. Splunk
• Feature/Setting: Automated Data Inputs from REST API endpoint; configure alerts on anomalous KPI trends.
3.7. Azure Logic Apps
• Feature/Setting: "When an HTTP request is received" to automate data input; "Send email" on KPI deviation.
3.8. SAP Manufacturing Integration and Intelligence (MII)
• Feature/Setting: Automated Data Connector for real-time line data; KPIs computed and logged to SAP ERP.
3.9. Tableau
• Feature/Setting: Automated scheduled data extract from production database; real-time dashboards auto-updated.
3.10. Datadog
• Feature/Setting: Automated log ingestion; configure "Monitor" and "Alert" for KPI thresholds.
3.11. Grafana
• Feature/Setting: Automated datasource connections; configure "Alert Rules" for visual KPI monitoring.
3.12. Twilio SMS
• Feature/Setting: Automated SMS alerts via "Messages API" when KPIs breach set limits.
3.13. Slack
• Feature/Setting: Automated posting with "Incoming Webhooks" for shift reports and anomaly alerts.
3.14. PostgreSQL
• Feature/Setting: Automate data insertion with REST API; scheduled automated queries to extract KPIs.
3.15. MongoDB Stitch
• Feature/Setting: Automated triggers on data insert/update; configure "Functions" to log and process KPIs.
3.16. ThingSpeak
• Feature/Setting: Automated channels for IoT data; automated alerts via MATLAB Analysis app.
3.17. Salesforce
• Feature/Setting: Automated KPI sync via "Platform Events"; dashboards reflect real-time updates.
3.18. Power BI
• Feature/Setting: Automated dataset refresh; set "Data Alerts" for production KPI variances.
3.19. ServiceNow
• Feature/Setting: Configure "Flow Designer" for automated incident creation on KPI breaches.
3.20. Zapier
• Feature/Setting: Automated workflow with scheduled polling; action steps to email, SMS, or apps.

Benefits

4.1. Automated, granular KPI monitoring enhances production transparency.
4.2. Automating anomaly detection supports proactive maintenance and intervention.
4.3. Automated logging supports regulatory and quality standards in the optical industry.
4.4. Corporate executives automatedly receive real-time insights for faster, data-driven decisions.
4.5. Automation reduces manual errors, enabling scalable monitoring across multiple sites and lines.