Manufacturing Excellence

Digital Transformation & AI Integration

Objectives

  • Reduce scrap and rework
  • Improve QA and traceability
  • Optimize cut planning and logistics
  • Enable AI-driven decision making

Focus Areas

  • IoT & shop-floor connectivity
  • AI/ML for defects & scrap prediction
  • MES & SAP integration
  • Security, compliance & reliability

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🚀 Implementation Roadmap
Digital Transformation & AI Integration
Manufacturing Excellence Initiative
Implementation Overview
5 Phase Journey to Digital Excellence
0
Assessment & Quick Wins
Foundation setup and proof-of-concept
1
Data Collection & MES Baseline
Deploy infrastructure and tracking systems
2
First AI Models & Automation
Launch vision defect and scrap prediction models
3
Optimization & Integration
SAP integration and cut-length optimization
4
Scale & Reliability
Full production deployment and continuous improvement
2 / 12
Architectural Design
IoT Architecture
IoT Architecture Diagram
3 / 12
Architectural Design
AI / ML Architecture
AI/ML Architecture Diagram
4 / 12
Architectural Design
MES & SAP Integration
MES SAP Integration Diagram
5 / 12
Phase 0
Assessment & Quick Wins
0
Key Activities
  • Inventory of existing PLC tags, sensors, QA bins, SAP interfaces
  • Implement basic MQTT broker and edge gateway proof-of-concept on 1 line
  • Start historian and Grafana dashboards for key metrics (diameter, tension, scrap)
📦 Deliverables
PLC tag list • Initial dashboards • MQTT topic specification
6 / 12
Phase 1
Data Collection & MES Baseline
1
Key Activities
  • Deploy edge gateway to send normalized telemetry
  • Integrate MES with historian for WIP and QA logging
  • Add barcode/RFID for spool tracking
  • Start image capture on one critical line
📦 Deliverables
Data lake • MES logs • Sample labeled QA images
7 / 12
Phase 2
First AI Models & Automation
2
Key Activities
  • Build and deploy vision defect model in shadow mode
  • Build scrap prediction model (tabular)
  • Implement auto-alerts and conditional holds for high-risk batches
📦 Deliverables
Edge inference service • Scoring API • Operator UI for review
8 / 12
Phase 3
Optimization & Integration
3
Key Activities
  • Implement cut-length optimizer integrated into MES
  • SAP integration for automatic order push/pull and inventory update
  • Expand to more production lines
📦 Deliverables
Cut plan service • SAP-MES sync • Improvement KPIs
9 / 12
Phase 4
Scale & Reliability
4
Key Activities
  • Full production deployment across all lines
  • Model retraining pipeline and drift detection
  • Explainability dashboards for transparency
  • Implement optimization of logistics with AGV/RFID if needed
📦 Deliverables
SLA dashboards • Model ops platform • Continuous improvement framework
10 / 12
Security, Compliance & Reliability
🔐
Authentication
Mutual TLS and device certificates (PKI) for device authentication
👥
Access Control
Role Based Access Control (RBAC) for MES and dashboards
🔒
Encryption
Data encryption at rest (data lake) and in transit
💾
Backup & Recovery
Regular backups of historian and model artifacts
⚖️
Compliance
GDPR and local data rules for personal data - scrub or mask customer info
🛡️
Security Monitoring
Continuous monitoring and threat detection across all systems
11 / 12
Success Metrics
KPIs to Track Our Progress
📉
Scrap Reduction
Kg of waste reduced per month
🎯
Defect Detection
Precision and recall metrics
♻️
Material Reuse Rate
Percentage of leftover material reused
🧪
Testing Efficiency
Reduction in sample tests and bin wastage
🚚
On-Time Delivery
Delivery rate improvement
⚙️
Machine Uptime
Uptime percentage and MTTR reduction
12 / 12