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Data-Driven Inventory Reduction for Mint Classics

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Harnessing the power of data to optimize warehouse storage and streamline operations.

This project dives into inventory management and data-driven decision-making, showcasing how a fictional retailer of classic model cars, Mint Classics, can leverage insights from a relational database to make strategic inventory decisions.

Key Project Steps:

  • Exploratory Data Analysis:

    • The script EDA Link
    • After ensuring data quality, I used these questions to investigate the data.
      • 1- How many products are stored in each warehouse?
      • 2- How many items did each warehouse serve?
      • 3- What is the percentage of moving stock for each warehouse?
      • 4- How much time does it usually take to deliver an order?
      • 5- Outlier investigation
      • 6- How much earlier than usual do we typically deliver orders?
      • 7- What is each warehouse's average delivery time (in days)?
      • 8- What types of products are stored in each warehouse?
      • 9-What subcategories do each of the products belong to?
      • 10-Where does the majority of our customer base reside?

  • Data Visulizations:

    • To get a better feel of Data I used this Query to get the data.
    • Then Excle link to the file to make this viz.
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  • Targeted Insights:

    • In this Script the analyses leverage the past steps and answer these questions.
      • 1- what are the moving stocks per year for each warehouse?
      • 2- what-if analysis: reducing the stock by 50 %
      • 3- How to rearrange the Warehouses?
      • 4- what is the capacity?
      • 5- Making the calculations
      • 6- the Recommendations

  • Recommendation:
    While reducing overall stock by 50% aligns with industry benchmarks and long-term inventory management,
    it's a significant move that requires careful planning and may not be ideal immediately. As a more immediate and actionable step,
    I recommend consolidating the inventory of warehouse D with warehouse C.
    This approach leverages C's ample capacity (124,880 items) to accommodate D's existing inventory (79,380) while simplifying operations and potentially reducing costs.
    While C's average daily delivery time is slightly longer than D's (4.6 days vs. 3.8 days),
    this minor trade-off is outweighed by the efficiency gains and cost savings of consolidation.
    This first step can then pave the way for further inventory reductions down the line, ensuring a smoother and more strategic approach to optimizing stock levels.

  • Tools and Technologies:

    • MySQL Workbench
    • SQL queries
    • Data visualization (Excel - Pivot tables)

Project Objectives:

  1. Explore current inventory composition and trends.
  2. Determine critical factors influencing inventory management.
  3. Provide actionable insights and recommendations for inventory reduction and optimization.
  4. Support a data-driven decision-making process for potential warehouse closure.

Audience:

  • Data analysts
  • Inventory management professionals
  • Business decision-makers interested in data-driven optimization
  • Anyone exploring SQL for data analysis and business problem-solving"