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SCM Platform Implementation Plan

This document outlines the detailed implementation strategy for the Smart Chaos Machines platform. For architectural decisions and project overview, see Project Proposal. For team collaboration practices, see Ways of Working.

Work Methodology: Agile with Phased Focus

Agile Framework: 2-week sprints with clear phase boundaries

Team collaboration approach detailed in Ways of Working - includes pair programming practices, visual planning, and architecture-driven sprint planning.

Phase 1: Foundation (2 sprints)

Focus: Prove the core mechanics work

  • Sprint 1: Stable Communication (Subscribe to CMs, basic read/write) + Deployment (Containerization)
  • Sprint 2: Complete Testability SPIKE + basic Developer Observability (health checks)

Key Architecture Components: See Project Proposal for detailed architecture decisions

  • Communication Module foundation
  • Basic containerization for Kubernetes deployment

Phase 2: Core Value Loop (4 sprints)

Focus: Deliver the MVP control loop

  • Sprint 3-4: Data Management (validation, recording user changes, cloud streaming setup) + Settings Management (listen to events, apply configurations)
  • Sprint 5: Complete Settings Management (retry logic, validation) + User Observability (local dashboards)
  • Sprint 6: Developer Observability (distributed tracing, APM) + cloud data lake integration + prepare for UAT

Critical Integrations:

  • Cloud data lake streaming pipeline setup
  • OPC-UA integration with counting machines
  • Recipe API and Line Configuration integration

Phase 3: Stabilization & Testing (3 sprints)

  • Sprint 7: End-to-end testing with CM simulator, bug fixes, performance optimization
  • Sprint 8: Deploy to a UAT line in the factory + validate cloud data streaming
  • Sprint 9: Address UAT feedback, fix bugs, stabilize solution + implement global monitoring dashboards

Quality Gates:

  • 99.5% system availability target (Success Metrics)
  • Successful cloud data streaming validation
  • Operator training completion

Phase 4: Global Factory Rollout (ongoing after phase 3)

Focus: Deploy to factories across the globe using automated pipelines

  • Deployment Infrastructure: GitOps pipelines for multi-factory deployment
  • Factory Onboarding: Standardized process for new factory integration
  • Regional Rollout: Phased deployment based on rollout plan
  • Support Structure: Support model (GEUS) + factory stakeholders
  • Monitoring & Alerting: Dashboards that support high-level Observability
  • Global Analytics: Cloud-based universal monitoring and cross-factory performance analytics

Rollout Strategy: Detailed in Project Proposal

  • UAT Environment Pipeline
  • Factory Release Process with gradual activation

Phase 5: Intelligence & Scale (Future improvements)

Focus: Add ML capabilities and production readiness

  • Sprint 10-11: ML Solution foundation + MLOps Observability setup leveraging cloud data lake
  • Sprint 12+: MLOps pipeline, global ML model training, testing, production rollout

ML Integration: See Project Proposal

  • Historic data provision for ML models
  • Global data lake leveraging for cross-factory optimization

Sprint Planning Integration

Architecture-Driven Planning: Detailed methodology in Ways of Working

Each sprint planning session:

  1. Current State Review: Architecture diagram snapshot showing progress
  2. Target State Vision: Post-sprint architecture goals
  3. Gap Analysis: Components needing development
  4. Dependency Mapping: Integration points and blockers
  5. Risk Visualization: Critical paths and external dependencies

Risk Management During Implementation

Technical Risk Mitigation: Full risk analysis in Project Proposal

  • Sprint 1-2: OPC-UA compatibility testing, containerization validation
  • Sprint 3-6: Cloud integration resilience, data quality validation
  • Sprint 7-9: Production impact assessment, rollback procedures
  • Phase 4: Factory-specific deployment challenges, network connectivity

Team Health Monitoring: Practices detailed in Ways of Working

  • Pair programming knowledge distribution
  • Firefighter rotation for legacy support
  • Regular team satisfaction tracking

Success Metrics by Phase

Phase 1 Success Criteria:

  • Stable OPC-UA connection established
  • Basic containerization working
  • Test framework foundation in place

Phase 2 Success Criteria:

  • End-to-end data flow from CM to cloud data lake
  • Automated configuration application working
  • Local dashboards displaying real-time data

Phase 3 Success Criteria:

  • UAT deployment successful
  • Cloud data streaming validated
  • Operator training completed

Phase 4 Success Criteria:

  • Multi-factory deployment pipeline operational
  • Global monitoring dashboards active
  • 50% reduction in manual configuration changes (Target from Project Proposal)

Implementation Dependencies

External Integrations:

  • Recipe API team coordination
  • Line Configuration team alignment
  • ML specialist collaboration for Phase 5

Infrastructure Requirements:

  • Factory Kubernetes cluster setup
  • Cloud data lake provisioning
  • Network connectivity validation

Compliance Prerequisites:

  • GDPR compliance validation (Details in Project Proposal)
  • Privacy Impact Assessments
  • Data sovereignty verification

Related Documents:

  • Project Proposal - Complete project overview, architecture, and risk analysis
  • Ways of Working - Team collaboration practices supporting this implementation