Solutions:

1.
SELECT 
  SKU_Code,
  Weight,
  CASE 
    WHEN Weight < 0.3 THEN 'Light'
    WHEN Weight BETWEEN 0.3 AND 0.6 THEN 'Medium'
    ELSE 'Heavy'
  END AS Weight_Category
FROM Products;

2.
SELECT 
  Sale_ID,
  Gross_Amount,
  CASE 
    WHEN Gross_Amount > 1000 THEN 'High Value'
    WHEN Gross_Amount BETWEEN 500 AND 1000 THEN 'Medium Value'
    ELSE 'Low Value'
  END AS Value_Category
FROM Sales_Transactions;

3.
SELECT 
  CASE 
    WHEN pr.Final_MRP BETWEEN 0 AND 500 THEN '0-500'
    WHEN pr.Final_MRP BETWEEN 501 AND 1000 THEN '501-1000'
    WHEN pr.Final_MRP BETWEEN 1001 AND 2000 THEN '1001-2000'
    ELSE '2000+'
  END AS Price_Range,
  COUNT(*) AS Product_Count
FROM Products p
JOIN Pricing pr ON p.SKU_Code = pr.SKU_Code
GROUP BY 
  CASE 
    WHEN pr.Final_MRP BETWEEN 0 AND 500 THEN '0-500'
    WHEN pr.Final_MRP BETWEEN 501 AND 1000 THEN '501-1000'
    WHEN pr.Final_MRP BETWEEN 1001 AND 2000 THEN '1001-2000'
    ELSE '2000+'
  END;

4.
WITH CustomerSpending AS (
  SELECT 
    c.Customer_ID,
    c.Customer_Name,
    SUM(s.Gross_Amount) AS Total_Spent
  FROM Customers c
  JOIN Sales_Transactions s ON c.Customer_ID = s.Customer_ID
  GROUP BY c.Customer_ID, c.Customer_Name
)
SELECT 
  Customer_ID,
  Customer_Name,
  Total_Spent,
  CASE 
    WHEN Total_Spent > 5000 THEN 'Platinum'
    WHEN Total_Spent BETWEEN 2000 AND 5000 THEN 'Gold'
    ELSE 'Silver'
  END AS Customer_Tier
FROM CustomerSpending;

5.
SELECT 
  Operation_ID,
  Operation_Type,
  Quantity,
  CASE 
    WHEN (Operation_Type = 'Inbound' AND Quantity < 10) OR
         (Operation_Type = 'Outbound' AND Quantity > 50) THEN 'Yes'
    ELSE 'No'
  END AS Attention_Needed
FROM Warehouse_Ops;

6.
SELECT 
  Sale_ID,
  Gross_Amount,
  CASE 
    WHEN Gross_Amount > 2000 THEN 0.10
    WHEN Gross_Amount BETWEEN 1000 AND 2000 THEN 0.07
    ELSE 0.05
  END AS Commission_Rate,
  Gross_Amount * 
    CASE 
      WHEN Gross_Amount > 2000 THEN 0.10
      WHEN Gross_Amount BETWEEN 1000 AND 2000 THEN 0.07
      ELSE 0.05
    END AS Commission_Amount
FROM Sales_Transactions;

7.
SELECT 
  Sale_ID,
  Timestamp,
  CASE 
    WHEN EXTRACT(HOUR FROM Timestamp) BETWEEN 6 AND 11 THEN 'Morning'
    WHEN EXTRACT(HOUR FROM Timestamp) BETWEEN 12 AND 17 THEN 'Afternoon'
    WHEN EXTRACT(HOUR FROM Timestamp) BETWEEN 18 AND 23 THEN 'Evening'
    ELSE 'Night'
  END AS Time_Category
FROM Sales_Transactions;

8.
SELECT 
  SKU_Code,
  Amazon_MRP,
  Flipkart_MRP,
  CASE 
    WHEN ABS(Amazon_MRP - Flipkart_MRP) / GREATEST(Amazon_MRP, Flipkart_MRP) > 0.15 THEN 'Yes'
    ELSE 'No'
  END AS Is_Inconsistent
FROM Pricing;

9.
SELECT 
  Operation_ID,
  Operation_Type,
  Quantity,
  CASE 
    WHEN Operation_Type = 'Inbound' AND Quantity > 100 THEN 100
    WHEN Operation_Type = 'Outbound' AND Quantity > 50 THEN 50
    ELSE 0
  END AS Bonus_Amount
FROM Warehouse_Ops;

10.
WITH CustomerRegions AS (
  SELECT 
    c.Customer_ID,
    CASE 
      WHEN c.Customer_Name LIKE '%Mumbai%' OR c.Customer_Name LIKE '%Delhi%' THEN 'Metro'
      WHEN c.Customer_Name LIKE '%Bangalore%' OR c.Customer_Name LIKE '%Chennai%' THEN 'Tier 1'
      ELSE 'Tier 2'
    END AS Region
  FROM Customers c
)
SELECT 
  Region,
  COUNT(DISTINCT cr.Customer_ID) AS Customer_Count,
  SUM(s.Gross_Amount) AS Total_Sales
FROM CustomerRegions cr
JOIN Sales_Transactions s ON cr.Customer_ID = s.Customer_ID
GROUP BY Region;

11.
WITH ProductSales AS (
  SELECT 
    p.SKU_Code,
    COALESCE(SUM(s.Quantity), 0) AS Total_Sold
  FROM Products p
  LEFT JOIN Sales_Transactions s ON p.SKU_Code = s.SKU_Code
  GROUP BY p.SKU_Code
)
SELECT 
  SKU_Code,
  Total_Sold,
  CASE 
    WHEN Total_Sold > 100 THEN 3
    WHEN Total_Sold BETWEEN 50 AND 100 THEN 2
    ELSE 1
  END AS Performance_Score
FROM ProductSales;

12.
SELECT 
  Sale_ID,
  Quantity,
  CASE 
    WHEN Quantity > 5 THEN 0.20
    WHEN Quantity BETWEEN 3 AND 5 THEN 0.15
    ELSE 0.10
  END AS Discount_Rate,
  Gross_Amount * 
    CASE 
      WHEN Quantity > 5 THEN 0.20
      WHEN Quantity BETWEEN 3 AND 5 THEN 0.15
      ELSE 0.10
    END AS Discounted_Amount
FROM Sales_Transactions;

13.
WITH ProductTurnover AS (
  SELECT 
    s.SKU_Code,
    AVG(DATEDIFF(s.Date, LAG(s.Date) OVER (PARTITION BY s.SKU_Code ORDER BY s.Date))) AS Avg_Days_Between_Sales
  FROM Sales_Transactions s
  GROUP BY s.SKU_Code
)
SELECT 
  SKU_Code,
  Avg_Days_Between_Sales,
  CASE 
    WHEN Avg_Days_Between_Sales < 30 THEN 'Fast'
    WHEN Avg_Days_Between_Sales BETWEEN 30 AND 90 THEN 'Medium'
    ELSE 'Slow'
  END AS Turnover_Category
FROM ProductTurnover;

14.
WITH CustomerSpending AS (
  SELECT 
    Customer_ID,
    SUM(Gross_Amount) AS Current_Spend,
    LAG(SUM(Gross_Amount)) OVER (PARTITION BY Customer_ID ORDER BY EXTRACT(MONTH FROM Date)) AS Previous_Spend
  FROM Sales_Transactions
  GROUP BY Customer_ID, EXTRACT(MONTH FROM Date)
)
SELECT 
  Customer_ID,
  Current_Spend,
  Previous_Spend,
  ((Current_Spend - Previous_Spend) / Previous_Spend) * 100 AS Pct_Increase,
  CASE 
    WHEN ((Current_Spend - Previous_Spend) / Previous_Spend) > 2 THEN 'Yes'
    ELSE 'No'
  END AS Is_Unusual
FROM CustomerSpending
WHERE Previous_Spend IS NOT NULL;

15.
WITH SalesMetrics AS (
  SELECT 
    p.SKU_Code,
    CASE 
      WHEN COALESCE(SUM(s.Quantity), 0) > 100 THEN 3
      WHEN COALESCE(SUM(s.Quantity), 0) BETWEEN 50 AND 100 THEN 2
      ELSE 1
    END AS Sales_Score
  FROM Products p
  LEFT JOIN Sales_Transactions s ON p.SKU_Code = s.SKU_Code
  GROUP BY p.SKU_Code
),
InventoryMetrics AS (
  SELECT 
    i.SKU_Code,
    CASE 
      WHEN i.Stock_Qty > 100 THEN 3
      WHEN i.Stock_Qty BETWEEN 50 AND 100 THEN 2
      ELSE 1
    END AS Inventory_Score
  FROM Inventory i
),
PriceMetrics AS (
  SELECT 
    pr.SKU_Code,
    CASE 
      WHEN pr.Final_MRP > 2000 THEN 3
      WHEN pr.Final_MRP BETWEEN 1000 AND 2000 THEN 2
      ELSE 1
    END AS Price_Score
  FROM Pricing pr
)
SELECT 
  sm.SKU_Code,
  sm.Sales_Score,
  im.Inventory_Score,
  pm.Price_Score,
  (sm.Sales_Score + im.Inventory_Score + pm.Price_Score) AS Overall_Health
FROM SalesMetrics sm
JOIN InventoryMetrics im ON sm.SKU_Code = im.SKU_Code
JOIN PriceMetrics pm ON sm.SKU_Code = pm.SKU_Code;