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https://github.com/glitzzybetty/sql-personal-project

This is a show case of my SQL super powers in an assumed fictional company
https://github.com/glitzzybetty/sql-personal-project

business-analytics business-intelligence dataanalytics database mssql-database sql

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This is a show case of my SQL super powers in an assumed fictional company

Awesome Lists containing this project

README 

          
Case Study #1 - Analyze Data in a Model Car Database 


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Contents







Introduction

As a data analyst at the fictional Mint Classics Company, am helping to analyze data in a relational database with the goal of supporting inventory-related business decisions that lead to the closure of a storage facility.





Project Scenerio

 Mint Classics Company, a retailer of classic model cars and other vehicles, is looking at closing one of their storage facilities. 

To support a data-based business decision, they are looking for suggestions and recommendations for reorganizing or reducing inventory, while still maintaining timely service to their customers. For example, they would like to be able to ship a product to a customer within 24 hours of the order being placed.





Project Objective



    1. Explore products currently in inventory.


    2. Determine important factors that may influence inventory reorganization/reduction.


    3. Provide analytic insights and data-driven recommendations.





Database Information

The Database for this project was initially configured to MySQL pattern, I had to convert it to MSSQL pattern. Re-wrote the table creations, and the data insertions with the order datails containing more that 1000 rows. I used partly AI prompting as additional assistance for ideas. It was QED.


Mint Classic Entity Relationship Diagram (ERD)

MintDB ERD



Exploring to understand some Business Demographic Information


It is very important to find out out business demography, customer segments, doing some background checks before attemping to fix underlying problems.


    


      
  1. Where are the top five customers aggregations located
    2. 

    	

    ```sql

    -- Top 5 Countries of Mint Classic customers

    select top 5

    country, 

    count(customerNumber) as [Number of Customers],

    round(avg(creditLimit),0) as [Average Credit Limit]

    from [mintclassics].[dbo].[customers]

    group by country 

    order by count(customerNumber) desc

    ```

    Answer:
    

    Top 5 customers information

      

        
    • The SQL query retrieves the customerNumber shows their country of residents and calculates the average (creditLimit) in each country group they belong to.
      • 

        
    • It presents data from the customers table.

        
      • 

    • The results are grouped by top 5 country.
      • 

        
    • The query then calculates the average creditlimit for each group of customers with the same country.
      • 

        
    • Finally, the results are sorted in descending order based on the customerNumber aggregation.
      • 

      	
    • The findings here shows that there are more customers in the USA
      • 

    


  2. Explain MintClassic Employees hierarchy, and their aggregation per location 
    3. 

    	

    ```sql

    SELECT 

        e1.jobTitle,

        COUNT(e1.employeeNumber) AS EmployeeCount,

        COALESCE(

            CONCAT(e2.firstName, ' ', e2.lastName),

            'No one' -- This will show 'No one' for the President

        ) AS [Report to Person],
    


    	COALESCE(

            e2.jobTitle,

            'No one' -- This will show 'No one' for the President

        ) AS [Report To Person Job Title],

    	COALESCE(

            o.[country],

            'No one' -- This will show 'No one' for the President

        ) AS [Report To Person Country]

    FROM 

        [mintclassics].[dbo].[employees] e1

    LEFT JOIN 

        [mintclassics].[dbo].[employees] e2 ON e1.reportsTo = e2.employeeNumber

    JOIN 

        [mintclassics].[dbo].[offices] o ON  e2.officeCode = o.officeCode

    GROUP BY 

        e1.jobTitle,

    	e2.jobTitle,

        e2.firstName, 

        e2.lastName,

    	 o.[country]

    ORDER BY 

        EmployeeCount DESC;

    ```

    Answer:
    

    Employee Hierarchy and aggregation per location

      

        
    • The SQL query retrieves the JobTitle of Employees, shows the count ofEmployeeNumber in that position and the report to person, their jobtitle and their country.
      • 

        
    • It presents data from the Employees table, self joins to get report to person and joins with officecode to get the country i.e. location of these staffs.

        
      • 

    • The results are grouped by top 5 jobTitle names and country.
      • 

        
    • The findings points out that majority of employees were from USA, pointing out the reasons more customers are the largest in US. Also, it was discovered one employee who is a sales rep reports to another sales rep in Japan, some questions needs to be asked the stakeholders, especially the VP sales.
      • 

    



Advance Questions


Critical Question are being asked to reveal Business problems, so that solution can be readily presented


    

      
  1.  Are there products with high inventory but low sales? How can we optimize the inventory of these products? 
    2. 

    	

    ```sql

    WITH product_sales AS (

        SELECT 

            p.productCode, 

            p.productName, 

            p.quantityInStock,

            ISNULL(SUM(od.quantityOrdered), 0) AS totalQuantitySold,

            ISNULL(SUM(od.quantityOrdered * od.priceEach) / NULLIF(AVG(p.quantityInStock), 0), 1) AS InventoryTurnOverRatio,

            ISNULL(AVG(p.quantityInStock) / NULLIF(SUM(od.quantityOrdered * od.priceEach), 0), 1) AS StockToSalesRatio

        FROM 

            products p

        LEFT JOIN 

            orderdetails od ON p.productCode = od.productCode

        GROUP BY 

            p.productCode, p.productName, p.quantityInStock, p.MSRP

    )

    SELECT TOP 10

        productCode, 

        productName, 

        quantityInStock, 

        totalQuantitySold,

        InventoryTurnOverRatio, 

        CAST(ROUND(StockToSalesRatio, 2) AS float) AS StockToSalesRatio

    FROM 

        product_sales

    ORDER BY 

        StockToSalesRatio DESC;

    ```

    Answer 1:
    

    Inventory vs Order

      

    • The SQL uses 
       CTE i.e. common Table Expression(product_sales):
      

      • 

    • Calculates totalQuantitySold, InventoryTurnOverRatio, and StockToSalesRatio.
      • 

    • Uses ISNULL and NULLIF to handle division by zero errors.
      • 

      Main Query:
      

    • Selects the top 10 products by StockToSalesRatio.
      • 

    • Orders the results by StockToSalesRatio in descending order to highlight products with high inventory relative to sales.
      •  

      Comments on Key Metrics:
      

    • Inventory Turnover Ratio (ITR): Measures how often inventory is sold and replaced over a period. A higher ITR indicates better performance.
      • 

    • Stock to Sales Ratio (SSR): Compares average inventory to sales. A higher SSR suggests overstocking and potential inefficiencies.
      • 

    • This script identifies products with high inventory but low sales and provide a basis for optimization strategies like targeted promotions, inventory reduction, or restocking adjustments.
      • 

    


     
  2.  Are all the warehouses currently in use still necessary? How can we review warehouses with low or inactive inventory? 
    3. 

    	

    ```sql

    WITH warehouse_inventory AS (

        SELECT 

            w.warehouseCode, 

            w.warehouseName, 

    		COUNT(DISTINCT o.customerNumber) AS [Number of Active Customers],

            SUM(p.quantityInStock) AS totalInventory,

    		ISNULL(SUM(od.quantityOrdered), 0) AS totalQuantityOrdered,

            ISNULL(SUM(od.quantityOrdered * od.priceEach) / NULLIF(AVG(p.quantityInStock), 0), 1) AS InventoryTurnOverRatio,

            ISNULL(AVG(p.quantityInStock) / NULLIF(SUM(od.quantityOrdered * od.priceEach), 0), 1) AS StockToSalesRatio

        FROM 

            warehouses w

        LEFT JOIN 

            products p ON w.warehouseCode = p.warehouseCode

    	LEFT JOIN orderdetails od ON p.productCode = od.productCode

    	LEFT JOIN orders o ON od.orderNumber = o.orderNumber

        GROUP BY 

            w.warehouseCode, w.warehouseName--, p.quantityInStock, p.MSRP

    )

    SELECT 

        warehouseCode, 

        warehouseName, 

    	[Number of Active Customers],

        totalInventory,

    	totalQuantityOrdered,

    	round(cast(InventoryTurnOverRatio as float), 2) as InventoryTurnOverRatio,

    	round(cast(StockToSalesRatio as float),4) as StockToSalesRatio

    FROM 

        warehouse_inventory

    --WHERE 

      --  totalInventory < 50;

      ORDER BY StockToSalesRatio DESC;

    ```

    Answer 2:
    

    Inventory vs Order

      

    • The SQL uses 
       CTE i.e. common Table Expression(warehouse_inventory):
      

      • 

    • Calculates totalQuantityOrdered, InventoryTurnOverRatio, and StockToSalesRatio.
      • 

      Main Query:
      

    • Selects warehouseName, Number of Active Customers, totalInventory.

      • 

    • Orders the results by StockToSalesRatio in descending order to highlight warehouses with high inventory relative to sales.
      •  

      Comments on Key Metrics:
      

    • Highest SSR on the list implies a high amount of inventory relative to sales.
      • 

    • Suggests overstocking, higher holding costs, and potential obsolescence.
      • 

    • May indicate that inventory is not turning over quickly enough, leading to inefficiency.
      • 

    •  Highest ITR Indicates high inventory turnover.
      • 

      Optimisation Strategy
      

      To Increase ITR and reduce SSR:
      

    •  Offer incentives or discounts to move excess inventory.
      • 

    • Slow down or temporarily halt new inventory orders until sales catch up with existing stock.
      • 

    • Analyze the reasons for low sales, such as product relevance, competition, or market demand, and take corrective actions.
      • 

    


  3.  Is there a relationship between product prices and their sales levels?  How can price adjustments impact sales?  
    4. 

    	

    ```sql

    SELECT Top 10

        p.productCode, 

        p.productName, 

        p.MSRP, cast(round(Avg(od.priceEach), 2) as float) as avgPriceSold,

    	Avg(od.priceEach) - p.MSRP as [Price Diff],

        SUM(od.quantityOrdered) AS totalQuantitySold

    FROM 

        products p

    JOIN 

        orderdetails od ON p.productCode = od.productCode

    GROUP BY 

        p.productCode, p.productName, p.MSRP

    ORDER BY 

        SUM(od.quantityOrdered) desc;
    


    ```

    Answer 3:
    

    Inventory vs Order

      

    • This query examines the relationship between product prices (MSRP) i.e. Manufacturer suggested Retail Price and sales volumes.
      • 

    • By analyzing the output, the company can identify if higher prices correspond to lower sales and
      • 

    • consider price adjustments to optimize sales volumes.
      • 

    

  4.  Who are the customers contributing the most to sales? How can we focus sales efforts on these valuable customers? 
    5. 

    	

    ```sql

    SELECT TOP 10

        c.customerNumber, 

        c.customerName, 

    	c.[country],

        SUM(od.quantityOrdered * p.MSRP) AS totalSales

    FROM 

        customers c

    JOIN 

        orders o ON c.customerNumber = o.customerNumber

    JOIN 

        orderdetails od ON o.orderNumber = od.orderNumber

    JOIN 

        products p ON od.productCode = p.productCode

    GROUP BY 

        c.customerNumber, c.customerName, c.[country]

    ORDER BY 

        totalSales DESC

    ```

    Answer 4:
    

    customer sales

      

    • This query identifies the top 10 customers by total sales.
      • 

    • Focusing sales efforts on these valuable customers can involve personalized marketing strategies, loyalty programs, and enhanced customer service.
      •