DataCo Supply Chain Dataset

Dataset Description

• Dataset Name: DataCo Supply Chain Analysis
• Dataset Content: Includes key operational metrics involving transactions, customers, products, geographic locations, and order statuses.
• Dataset Purpose: Used to optimize inventory management, reduce stockouts, and improve customer satisfaction with delivery status.

Data Preparation

• Data Cleaning: Performed using Python, including removal of irrelevant columns, renaming columns, handling missing values, and applying scaling operations.
• Key Transformations:
  • Normalization: Apply Min‑Max scaling to the product_price column.
  • Standardization: Apply StandardScaler to the profit_amount column.

Data Warehouse Design

• Design Pattern: Star schema implemented in SQL Server.
• Included Tables:
  • Fact Table: Orders Fact Table
  • Dimension Tables:
    • Product Dimension
    • Customer Dimension
    • Orders Dimension
    • Date Dimension

Data Visualization

• Tool: Power BI
• Key Visualizations:
  • Product Sales: Stacked column chart by department and market.
  • Product Category Sales Volume: Horizontal stacked chart by department.
  • Delivery Status Filter: Slicer for dynamic data filtering.

Business Impact

• Inventory Strategy: Develop strategic overstock plans for key departments and markets.
• Logistics Optimization: Improve delayed deliveries in the APAC region by collaborating with reliable local carriers and establishing local distribution centers.
• High‑Demand Product Categories: Prioritize replenishment strategies for these categories to avoid potential stockouts.
• Stable‑Demand Products: Maintain optimal inventory levels to ensure continuous supply without overstocking.