Untitled

graph TD
    A[Start: Data Collection & Preparation] --> B{Retrieve Plate Request History};
    B --> C[Extract Data from Inventory Management System (IMS)];
    C --> D[Filter and Clean Data (e.g., remove outliers, handle missing values)];
    D --> E[Aggregate Data by District Office and Time Period (e.g., weekly, monthly)];
    E --> F[Feature Engineering (e.g., create time-based features, seasonal indicators)];
    F --> G[Data Partitioning (Train/Test/Validation)];
    G --> H[Model Selection & Training];
    H --> I{Choose Forecasting Model (e.g., ARIMA, Prophet, LSTM)};
    I -- ARIMA/Prophet/LSTM --> J[Train Model with Training Data];
    J --> K[Evaluate Model Performance (using Test/Validation Data)];
    K --> L{Performance Acceptable?};
    L -- Yes --> M[Deploy Model];
    L -- No --> H;
    M --> N[Generate Forecasts (for each District Office)];
    N --> O[Visualize Forecasts (e.g., charts, tables)];
    O --> P[Provide Forecast Reports to Production Planning & Inventory Teams];
    P --> Q[Update Production Plan based on Forecasts];
    Q --> R[Update Inventory Management System with Predicted Demand];
    R --> S[End: Ongoing Monitoring & Model Retraining];
    S --> B;

    style A fill:#f9f,stroke:#333,stroke-width:2px
    style M fill:#90EE90,stroke:#333,stroke-width:2px
    style L fill:#FFFFE0,stroke:#333,stroke-width:2px
    style I fill:#FFFFE0,stroke:#333,stroke-width:2px