Supply Chain V1 - magicplatforms/ai-workflows GitHub Wiki

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Sequence Diagram: Step 2 – Generate Predictive Insights

sequenceDiagram
    participant DataLake as Cloud Data Lake/Warehouse
    participant ML as Embedded ML Module
    participant Insights as Insights Store

    DataLake->>ML: Trigger continuous model run (demand, ETA, risk)
    ML->>ML: Apply forecasting algorithms
    ML->>Insights: Write predictions (demand forecast, ETA, risk scores)
    Insights--)Stakeholders: (for dashboards, historical comparison)

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Sequence Diagram: Step 3 – Optimize & Decide

sequenceDiagram
    participant Insights as Insights Store
    participant Optimize as Optimization Engine
    participant Recommendations as Recommendation Store
    participant Platform as SaaS Platform

    Insights->>Optimize: Send predictive inputs (demand, ETA, capacity)
    Optimize->>Optimize: Run optimization logic (replenish, consolidate, reroute)
    Optimize->>Recommendations: Generate actionable recommendations
    Recommendations--)Platform: Make recommendations available for review

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Sequence Diagram: Step 4 – Auto-Execute Transactions

sequenceDiagram
    participant Recommendations as Recommendation Store
    participant API as API/EDI Connector
    participant ERP as Connected ERP/TMS/WMS

    Recommendations->>API: Send transaction instructions (POs, tenders, load builds)
    API->>ERP: Auto-create POs, send tenders, update inventory
    ERP--)API: Acknowledge receipt
    API--)Recommendations: Confirm execution status

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Sequence Diagram: Step 5 – Monitor in Real Time

sequenceDiagram
    participant IoT as IoT Sensors/GPS
    participant ERP as ERP/TMS/WMS
    participant Control as Control Tower
    participant Stakeholders as Stakeholders

    IoT->>Control: Stream real-time status (location, temperature)
    ERP->>Control: Update committed vs. actual (inventory, shipments)
    Control->>Stakeholders: Refresh dashboards (orders, KPIs, exceptions)
    Stakeholders--)Control: Acknowledge or annotate status

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Sequence Diagram: Step 6 – Detect Exceptions & Orchestrate Response

sequenceDiagram
    participant Control as Control Tower
    participant Exception as Exception Engine
    participant Ticketing as Incident Ticketing System
    participant Stakeholders as Stakeholders
    participant Correction as Correction Orchestrator

    Control->>Exception: Forward deviations (late shipment, defect, spike)
    Exception->>Exception: Evaluate rule/AI‐driven thresholds
    alt Exception Flagged
        Exception->>Ticketing: Create incident ticket
        Exception->>Stakeholders: Send alert (Teams/Slack/Email)
        Exception->>Correction: Suggest or auto‐launch corrective action
        Correction--)Stakeholders: Confirm action taken (expedite, re‐book, etc.)
    else No Exception
        Exception--)Control: Continue monitoring
    end

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Sequence Diagram: Step 7 – Learn & Refine

sequenceDiagram
    participant Outcome as Transaction/Service Outcome
    participant Feedback as Feedback Loop
    participant ModelTrain as Model Training Pipeline
    participant ML as Embedded ML Module

    Outcome->>Feedback: Send actual results (service level, cost, delay)
    Feedback->>ModelTrain: Update training dataset
    ModelTrain->>ModelTrain: Retrain or reweight features
    ModelTrain->>ML: Deploy updated model parameters
    ML--)Optimize: Improved accuracy for next cycle

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Each of these diagrams highlights the key participants and interactions for that step. You can collapse or expand them as needed in your GitHub Wiki.