Add challenges 038 through 040

README.md

@@ -30,7 +30,7 @@ Tip: Each challenge folder includes its own README with the question, expected o
 | Revenue analytics | 005, 007, 011, 013, 014, 030 |
 | Date spines and rolling windows | 008, 016, 028 |
 | Product and customer behavior | 012, 015, 017, 018, 021, 022, 023, 024, 025, 026 |
-| Data engineering SQL patterns | 035, 036, 037 |
+| Data engineering SQL patterns | 035, 036, 037, 038, 039, 040 |
 
 For ordered study plans, see [Learning paths](docs/learning-paths.md).
 
@@ -72,6 +72,9 @@ For ordered study plans, see [Learning paths](docs/learning-paths.md).
 35. Subscription renewals + missed renewals + winback (30 days) — [challenges/035_subscription_renewal_winback](challenges/035_subscription_renewal_winback)
 36. SCD Type 2 customer dimension (history table) — [challenges/036_scd2_customer_dimension](challenges/036_scd2_customer_dimension)
 37. Incremental fact upsert (staging → fact, insert + update) — [challenges/037_incremental_fact_upsert](challenges/037_incremental_fact_upsert)
+38. Inventory stockout risk (velocity + lead time scoring) — [challenges/038_inventory_stockout_risk](challenges/038_inventory_stockout_risk)
+39. Trial to paid conversion (cohorts + conversion windows) — [challenges/039_trial_to_paid_conversion](challenges/039_trial_to_paid_conversion)
+40. Revenue leakage audit (invoice + payment reconciliation) — [challenges/040_revenue_leakage_audit](challenges/040_revenue_leakage_audit)
 
 ## How to use
 You can copy/paste the SQL into SQLite, Postgres, or any SQL runner with minor tweaks.

challenges/038_inventory_stockout_risk/README.md

@@ -0,0 +1,37 @@
+# Challenge 038: Inventory stockout risk
+
+Goal: flag products that may stock out based on recent sales velocity, current inventory, lead time, and the next open purchase order.
+
+Use an as-of date of `2024-05-15`.
+
+Rules:
+- `net_available_units` = on hand units minus reserved units
+- `recent_7d_units` = units sold from `2024-05-09` through `2024-05-15`
+- `avg_daily_units` = `recent_7d_units / 7`
+- `days_of_cover` = `net_available_units / avg_daily_units`
+- Risk is `critical` when days of cover is at or below lead time, or net availability is at or below safety stock
+- Risk is `watch` when days of cover is within 7 days after lead time
+- Products with no recent demand should be labeled `no_recent_demand`
+
+## Files
+- `schema.sql` creates product, inventory, sales, and purchase order tables
+- `solution.sql` computes stockout risk per product
+
+## Run (SQLite)
+
+Windows CMD:
+```bat
+type challenges\038_inventory_stockout_risk\schema.sql challenges\038_inventory_stockout_risk\solution.sql | sqlite3
+```
+
+## Output
+
+Columns:
+- product_id
+- sku
+- net_available_units
+- recent_7d_units
+- avg_daily_units
+- days_of_cover
+- next_open_po_date
+- risk_level

challenges/038_inventory_stockout_risk/expected.json

@@ -0,0 +1,59 @@
+{
+  "challenge": "038_inventory_stockout_risk",
+  "result_sets": [
+    {
+      "columns": [
+        "product_id",
+        "sku",
+        "net_available_units",
+        "recent_7d_units",
+        "avg_daily_units",
+        "days_of_cover",
+        "next_open_po_date",
+        "risk_level"
+      ],
+      "rows": [
+        [
+          101,
+          "WIDGET-A",
+          60,
+          70,
+          10.0,
+          6.0,
+          "2024-05-22",
+          "critical"
+        ],
+        [
+          102,
+          "FILTER-B",
+          120,
+          42,
+          6.0,
+          20.0,
+          "2024-05-25",
+          "healthy"
+        ],
+        [
+          103,
+          "PUMP-C",
+          50,
+          21,
+          3.0,
+          16.67,
+          "2024-06-01",
+          "watch"
+        ],
+        [
+          104,
+          "SENSOR-D",
+          8,
+          0,
+          0.0,
+          null,
+          "2024-05-25",
+          "no_recent_demand"
+        ]
+      ]
+    }
+  ]
+}

challenges/038_inventory_stockout_risk/schema.sql

@@ -0,0 +1,88 @@
+-- Challenge 038: Inventory stockout risk
+--
+-- Scenario:
+-- A warehouse team wants to find products that may stock out before replenishment.
+-- Combine recent sales velocity, current available inventory, supplier lead time,
+-- safety stock, and the next open purchase order.
+--
+-- Use as-of date: 2024-05-15
+-- Recent sales window: 2024-05-09 through 2024-05-15
+--
+-- Output per product:
+-- product_id | sku | net_available_units | recent_7d_units | avg_daily_units | days_of_cover | next_open_po_date | risk_level
+
+DROP TABLE IF EXISTS purchase_orders;
+DROP TABLE IF EXISTS sales;
+DROP TABLE IF EXISTS inventory;
+DROP TABLE IF EXISTS products;
+
+CREATE TABLE products (
+  product_id INTEGER PRIMARY KEY,
+  sku TEXT NOT NULL,
+  category TEXT NOT NULL,
+  lead_time_days INTEGER NOT NULL,
+  safety_stock_units INTEGER NOT NULL
+);
+
+CREATE TABLE inventory (
+  product_id INTEGER PRIMARY KEY,
+  on_hand_units INTEGER NOT NULL,
+  reserved_units INTEGER NOT NULL
+);
+
+CREATE TABLE sales (
+  sale_id INTEGER PRIMARY KEY,
+  product_id INTEGER NOT NULL,
+  sold_date TEXT NOT NULL,
+  units_sold INTEGER NOT NULL
+);
+
+CREATE TABLE purchase_orders (
+  po_id INTEGER PRIMARY KEY,
+  product_id INTEGER NOT NULL,
+  due_date TEXT NOT NULL,
+  units_ordered INTEGER NOT NULL,
+  status TEXT NOT NULL
+);
+
+INSERT INTO products (product_id, sku, category, lead_time_days, safety_stock_units) VALUES
+  (101, 'WIDGET-A', 'widgets', 10, 20),
+  (102, 'FILTER-B', 'filters', 6, 10),
+  (103, 'PUMP-C', 'pumps', 14, 15),
+  (104, 'SENSOR-D', 'sensors', 7, 5);
+
+INSERT INTO inventory (product_id, on_hand_units, reserved_units) VALUES
+  (101, 72, 12),
+  (102, 130, 10),
+  (103, 65, 15),
+  (104, 8, 0);
+
+INSERT INTO sales (sale_id, product_id, sold_date, units_sold) VALUES
+  (1, 101, '2024-05-02', 30),
+  (2, 101, '2024-05-09', 8),
+  (3, 101, '2024-05-10', 10),
+  (4, 101, '2024-05-11', 12),
+  (5, 101, '2024-05-12', 9),
+  (6, 101, '2024-05-13', 11),
+  (7, 101, '2024-05-14', 10),
+  (8, 101, '2024-05-15', 10),
+  (9, 102, '2024-05-09', 4),
+  (10, 102, '2024-05-10', 8),
+  (11, 102, '2024-05-12', 6),
+  (12, 102, '2024-05-14', 10),
+  (13, 102, '2024-05-15', 14),
+  (14, 103, '2024-05-09', 2),
+  (15, 103, '2024-05-10', 3),
+  (16, 103, '2024-05-11', 4),
+  (17, 103, '2024-05-12', 3),
+  (18, 103, '2024-05-14', 5),
+  (19, 103, '2024-05-15', 4),
+  (20, 104, '2024-05-07', 5);
+
+INSERT INTO purchase_orders (po_id, product_id, due_date, units_ordered, status) VALUES
+  (1, 101, '2024-05-22', 80, 'open'),
+  (2, 101, '2024-06-05', 120, 'open'),
+  (3, 102, '2024-05-25', 60, 'open'),
+  (4, 102, '2024-05-18', 30, 'closed'),
+  (5, 103, '2024-06-01', 40, 'open'),
+  (6, 104, '2024-05-25', 20, 'open');

challenges/038_inventory_stockout_risk/solution.sql

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+-- Output columns:
+-- product_id | sku | net_available_units | recent_7d_units | avg_daily_units | days_of_cover | next_open_po_date | risk_level
+
+WITH params AS (
+  SELECT date('2024-05-15') AS as_of_date
+),
+recent_sales AS (
+  SELECT
+    p.product_id,
+    COALESCE(SUM(
+      CASE
+        WHEN date(s.sold_date) BETWEEN date(params.as_of_date, '-6 day') AND params.as_of_date
+        THEN s.units_sold
+        ELSE 0
+      END
+    ), 0) AS recent_7d_units
+  FROM products p
+  CROSS JOIN params
+  LEFT JOIN sales s ON s.product_id = p.product_id
+  GROUP BY p.product_id
+),
+next_purchase_order AS (
+  SELECT
+    product_id,
+    MIN(date(due_date)) AS next_open_po_date
+  FROM purchase_orders
+  WHERE status = 'open'
+  GROUP BY product_id
+),
+metrics AS (
+  SELECT
+    p.product_id,
+    p.sku,
+    p.lead_time_days,
+    p.safety_stock_units,
+    i.on_hand_units - i.reserved_units AS net_available_units,
+    rs.recent_7d_units,
+    ROUND(rs.recent_7d_units / 7.0, 2) AS avg_daily_units,
+    CASE
+      WHEN rs.recent_7d_units = 0 THEN NULL
+      ELSE ROUND((i.on_hand_units - i.reserved_units) * 7.0 / rs.recent_7d_units, 2)
+    END AS days_of_cover,
+    npo.next_open_po_date
+  FROM products p
+  JOIN inventory i ON i.product_id = p.product_id
+  JOIN recent_sales rs ON rs.product_id = p.product_id
+  LEFT JOIN next_purchase_order npo ON npo.product_id = p.product_id
+),
+scored AS (
+  SELECT
+    product_id,
+    sku,
+    net_available_units,
+    recent_7d_units,
+    avg_daily_units,
+    days_of_cover,
+    next_open_po_date,
+    CASE
+      WHEN recent_7d_units = 0 THEN 'no_recent_demand'
+      WHEN net_available_units <= safety_stock_units THEN 'critical'
+      WHEN days_of_cover <= lead_time_days THEN 'critical'
+      WHEN days_of_cover <= lead_time_days + 7 THEN 'watch'
+      ELSE 'healthy'
+    END AS risk_level
+  FROM metrics
+)
+SELECT
+  product_id,
+  sku,
+  net_available_units,
+  recent_7d_units,
+  avg_daily_units,
+  days_of_cover,
+  next_open_po_date,
+  risk_level
+FROM scored
+ORDER BY product_id;

challenges/039_trial_to_paid_conversion/README.md

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+# Challenge 039: Trial to paid conversion
+
+Goal: measure trial conversion by signup cohort and trial plan using 14-day and 30-day conversion windows.
+
+For each signup cohort month and trial plan, calculate:
+- number of trials
+- users who converted to paid within 14 days
+- users who converted to paid within 30 days
+- 30-day conversion rate
+- average days to paid conversion within 30 days
+- new monthly recurring revenue from 30-day conversions
+
+## Files
+- `schema.sql` creates trial signup and paid subscription tables
+- `solution.sql` finds each user's first paid subscription and rolls conversion metrics up by cohort
+
+## Run (SQLite)
+
+Windows CMD:
+```bat
+type challenges\039_trial_to_paid_conversion\schema.sql challenges\039_trial_to_paid_conversion\solution.sql | sqlite3
+```
+
+## Output
+
+Columns:
+- cohort_month
+- trial_plan
+- trials
+- converted_14d
+- converted_30d
+- conversion_30d_pct
+- avg_days_to_paid_30d
+- new_mrr_30d