Food & Beverage ➔ SQL Analytics

Inventory & Margin Optimization

> Engineering SQL queries to bridge the gap between menu complexity and operational profitability.

Top Revenue

Italian

Cuisine Performance

Peak Volume

Lunch

Mon/Fri/Sun Demand

Star Item

Korean Beef Bowl

High Profit / High Vol

Fusion Factor

49% Drop-off

Conversion Opportunity

Query Showcase

01. Temporal Traffic Analysis

Identifying the bottleneck hours using cron-style SQL ordering.

SELECT 
    DAYNAME(order_date) AS day_of_week, 
    HOUR(order_time) AS hour_of_day, 
    COUNT(DISTINCT order_id) AS number_of_orders 
FROM fact_orders 
GROUP BY 
    DAYOFWEEK(order_date), day_of_week, hour_of_day 
ORDER BY 
    DAYOFWEEK(order_date), number_of_orders DESC;

Result: Lunch hours on Mon/Fri/Sun are critical peaks. Recommended staffing increase specifically for these windows to maintain service SLAs.

02. Strategic Menu Matrix (CTE + NTILE)

Classification of menu items into 'Stars', 'Puzzle', 'Workhorse', and 'Dud'.

WITH total_order_revenue AS (
    SELECT 
        m.item_name, 
        COUNT(o.order_id) AS total_orders, 
        SUM(m.price) AS total_revenue 
    FROM dim_menu_items m 
    INNER JOIN fact_orders o ON m.menu_item_id = o.item_id 
    GROUP BY m.item_name
), 
ItemRanks AS (
    SELECT 
        item_name, total_orders, total_revenue, 
        NTILE(2) OVER (ORDER BY total_orders DESC) AS order_ntile, 
        NTILE(2) OVER (ORDER BY total_revenue DESC) AS revenue_ntile 
    FROM total_order_revenue
) 
SELECT 
    item_name, total_orders, total_revenue, 
    CASE 
        WHEN order_ntile = 1 AND revenue_ntile = 1 THEN 'Star'
        WHEN order_ntile = 1 AND revenue_ntile = 2 THEN 'Workhorse'
        WHEN order_ntile = 2 AND revenue_ntile = 1 THEN 'Puzzle'
        WHEN order_ntile = 2 AND revenue_ntile = 2 THEN 'Dud' 
    END AS item_category 
FROM ItemRanks;

Result: Isolated 'Stars' vs 'Duds'. Revealed that the Hamburger (Volume Leader) isn't the primary Revenue Driver, prompting a re-valuation of bundling strategies.

Building the Data Pipeline

01

Schema Foundation

Constructed a robust dimensional model (Star Schema). Joined operational fact tables with menu dimension items to create a reliable playground for complex analytical queries.

02

Basket Analysis (Self-Joins)

Executed self-joins on the `fact_orders` table to uncover cross-cuisine purchasing behavior. Found that customers frequently pair American staples with Asian sides.

03

Operational Logic

Engineered CASE expressions to drive automated categorization. This removed manual guesswork for the management team, providing a live "Menu Dashboard" via SQL views.

Strategic Roadmap

  • • Promote the Italian category as the primary revenue "Puzzle" segment.
  • • Launch "Fusion Combos" based on identified basket pairing trends.

Revenue Gains

  • • Focus on "Star" items (Korean Beef Bowl) to maximize high-margin sales.
  • • Staffing optimization for peak lunchtime windows (Mon, Fri, Sun).

Technical Toolkit

MySQL Workbench Window Functions Common Table Expressions Dimensional Modeling

Analyst Insight

"Data revealed that customers are more adventurous than the menu suggests. Pairing patterns prove that 'Fusion' isn't just a trend—it's how the customer base actually eats."

Github Repository

Questions on this Analysis?