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docs/challenging-sql-problems/problems/gold/supply-chain-network--sample-input.sql
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| ```sql | ||
| with | ||
| locations(location_id, location_type) as ( | ||
| values | ||
| (1, 'supplier'), | ||
| (2, 'supplier'), | ||
| (3, 'depot'), | ||
| (4, 'depot'), | ||
| (5, 'store') | ||
| ), | ||
| deliveries(delivery_date, from_location_id, to_location_id, product_id, quantity) as ( | ||
| values | ||
| ('2024-01-01 01:23:53'::timestamp, 1, 3, 123, 25), | ||
| ('2024-01-01 06:27:54'::timestamp, 2, 4, 123, 25), | ||
| ('2024-01-01 12:27:39'::timestamp, 4, 5, 123, 25), | ||
| ('2024-01-01 17:12:59'::timestamp, 1, 3, 123, 30), | ||
| ('2024-01-02 01:27:57'::timestamp, 3, 5, 123, 25), | ||
| ('2024-01-02 05:16:08'::timestamp, 3, 4, 123, 30), | ||
| ('2024-01-02 05:40:53'::timestamp, 2, 3, 123, 20), | ||
| ('2024-01-02 07:29:53'::timestamp, 1, 4, 123, 30), | ||
| ('2024-01-02 09:22:53'::timestamp, 3, 5, 123, 20), | ||
| ('2024-01-02 18:28:39'::timestamp, 4, 5, 123, 60) | ||
| ), | ||
| sales(sale_datetime, store_id, product_id, quantity) as ( | ||
| values | ||
| ('2024-01-01 14:56:12'::timestamp, 5, 123, 5), | ||
| ('2024-01-01 16:28:24'::timestamp, 5, 123, 3), | ||
| ('2024-01-01 16:35:38'::timestamp, 5, 123, 4), | ||
| ('2024-01-01 20:13:46'::timestamp, 5, 123, 2), | ||
| ('2024-01-02 09:37:11'::timestamp, 5, 123, 12), | ||
| ('2024-01-02 14:02:57'::timestamp, 5, 123, 30), | ||
| ('2024-01-02 14:21:39'::timestamp, 5, 123, 3), | ||
| ('2024-01-02 16:44:26'::timestamp, 5, 123, 8), | ||
| ('2024-01-02 18:28:37'::timestamp, 5, 123, 2) | ||
| ) | ||
| ``` |
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docs/challenging-sql-problems/problems/gold/supply-chain-network--sample-output.sql
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| ```sql | ||
| solution(stock_date, store_id, supplier_id, stock_volume, stock_proportion) as ( | ||
| values | ||
| ('2024-01-01'::date, 5, 1, 0, 0.00), | ||
| ('2024-01-01'::date, 5, 2, 11, 100.00), | ||
| ('2024-01-02'::date, 5, 1, 30, 49.18), | ||
| ('2024-01-02'::date, 5, 2, 31, 50.82) | ||
| ) | ||
| ``` |
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docs/challenging-sql-problems/problems/gold/supply-chain-network.md
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| # Supply chain network 🚛 | ||
|
|
||
| > [!SUCCESS] Scenario | ||
| > | ||
| > A supermarket's supply chain has three main components: stores, depots, and suppliers. | ||
| > | ||
| > In general, stock is sent from a supplier to a depot, and then from the depot to a store; however, there are cases where suppliers send stock directly to stores and depots send stock to other depots. | ||
| > | ||
| > For example: | ||
| > | ||
| > ```mermaid | ||
| > graph LR | ||
| > supplier_2 ----> store_6 | ||
| > supplier_2 ---> depot_5 | ||
| > supplier_2 ---> depot_4 | ||
| > supplier_1 --> depot_3 | ||
| > supplier_1 ---> depot_4 | ||
| > depot_5 --> depot_4 | ||
| > depot_5 ---> store_6 | ||
| > depot_5 ---> store_7 | ||
| > depot_5 ---> store_8 | ||
| > depot_4 ---> store_6 | ||
| > depot_4 ---> store_7 | ||
| > depot_4 ---> store_8 | ||
| > depot_3 --> depot_5 | ||
| > depot_3 ---> store_6 | ||
| > depot_3 ---> store_7 | ||
| > depot_3 ---> store_8 | ||
| > ``` | ||
| > | ||
| > Although the supermarket knows how much stock is transported between locations, it doesn't know how much of each stock came from each supplier. | ||
| > | ||
| > This makes it difficult to report various metrics to the suppliers, like the stock balances and sales volumes of their products. | ||
| > [!QUESTION] | ||
| > | ||
| > Determine what the most likely proportion of stock in a store at the end of each day is from each supplier. | ||
| > | ||
| > Assume that stock moves in a queue ([first in, first out](https://en.wikipedia.org/wiki/Stock_rotation)) in both the depots and the stores. | ||
| > | ||
| > The output should have a row per store per supplier per day, with the columns: | ||
| > | ||
| > - `stock_date` | ||
| > - `store_id` as the ID of the store | ||
| > - `supplier_id` as the ID of the supplier | ||
| > - `stock_volume` as the derived volume of stock in the store from the supplier at the end of the day | ||
| > - `stock_proportion` as the derived proportion of stock in the store from the supplier. Express this as a percentage rounded to two decimal places | ||
| > | ||
| > Order the output by `stock_date`, `store_id`, and `supplier_id`. | ||
| > | ||
| > You can choose to show stores that have no stock from a supplier on a given day (i.e., you can show a row with a `stock_volume` of 0 or not show the row at all, whatever is easiest for you). | ||
| <details> | ||
| <summary>Expand for the DDL</summary> | ||
| --8<-- "docs/challenging-sql-problems/problems/gold/supply-chain-network.sql" | ||
| </details> | ||
| The solution can be found at: | ||
| - [supply-chain-network.md](../../solutions/gold/supply-chain-network.md) | ||
| A worked example is provided below to help illustrate the "shuffling" within the locations. | ||
| --- | ||
| <!-- prettier-ignore --> | ||
| >? INFO: **Sample input** | ||
| > | ||
| > **Locations** | ||
| > | ||
| > | location_id | location_type | | ||
| > |------------:|:--------------| | ||
| > | 1 | supplier | | ||
| > | 2 | supplier | | ||
| > | 3 | depot | | ||
| > | 4 | depot | | ||
| > | 5 | store | | ||
| > | ||
| > **Deliveries** | ||
| > | ||
| > | delivery_date | from_location_id | to_location_id | product_id | quantity | | ||
| > |:--------------------|-----------------:|----------------:|-----------:|---------:| | ||
| > | 2024-01-01 01:23:53 | 1 | 3 | 123 | 25 | | ||
| > | 2024-01-01 06:27:54 | 2 | 4 | 123 | 25 | | ||
| > | 2024-01-01 12:27:39 | 4 | 5 | 123 | 25 | | ||
| > | 2024-01-01 17:12:59 | 1 | 3 | 123 | 30 | | ||
| > | 2024-01-02 01:27:57 | 3 | 5 | 123 | 25 | | ||
| > | 2024-01-02 05:16:08 | 3 | 4 | 123 | 30 | | ||
| > | 2024-01-02 05:40:53 | 2 | 3 | 123 | 20 | | ||
| > | 2024-01-02 07:29:53 | 1 | 4 | 123 | 30 | | ||
| > | 2024-01-02 09:22:53 | 3 | 5 | 123 | 20 | | ||
| > | 2024-01-02 18:28:39 | 4 | 5 | 123 | 60 | | ||
| > | ||
| > **Sales** | ||
| > | ||
| > | sale_datetime | store_id | product_id | quantity | | ||
| > |:--------------------|---------:|-----------:|---------:| | ||
| > | 2024-01-01 14:56:12 | 5 | 123 | 5 | | ||
| > | 2024-01-01 16:28:24 | 5 | 123 | 3 | | ||
| > | 2024-01-01 16:35:38 | 5 | 123 | 4 | | ||
| > | 2024-01-01 20:13:46 | 5 | 123 | 2 | | ||
| > | 2024-01-02 09:37:11 | 5 | 123 | 12 | | ||
| > | 2024-01-02 14:02:57 | 5 | 123 | 30 | | ||
| > | 2024-01-02 14:21:39 | 5 | 123 | 3 | | ||
| > | 2024-01-02 16:44:26 | 5 | 123 | 8 | | ||
| > | 2024-01-02 18:28:37 | 5 | 123 | 2 | | ||
| > | ||
| > **Network diagram** | ||
| > | ||
| > ```mermaid | ||
| > graph LR | ||
| > supplier_1 --> depot_3 | ||
| > supplier_1 --> depot_4 | ||
| > supplier_2 --> depot_3 | ||
| > supplier_2 --> depot_4 | ||
| > depot_3 --> depot_4 | ||
| > depot_3 --> store_5 | ||
| > depot_4 --> store_5 | ||
| > ``` | ||
| > | ||
| --8<-- "docs/challenging-sql-problems/problems/gold/supply-chain-network--sample-input.sql" | ||
| <!-- prettier-ignore --> | ||
| >? INFO: **Sample output** | ||
| > | ||
| >| stock_date | store_id | supplier_id | stock_volume | stock_proportion | | ||
| >|:-----------|---------:|------------:|-------------:|-----------------:| | ||
| >| 2024-01-01 | 5 | 1 | 0 | 0.00 | | ||
| >| 2024-01-01 | 5 | 2 | 11 | 100.00 | | ||
| >| 2024-01-02 | 5 | 1 | 30 | 49.18 | | ||
| >| 2024-01-02 | 5 | 2 | 31 | 50.82 | | ||
| > | ||
| --8<-- "docs/challenging-sql-problems/problems/gold/supply-chain-network--sample-output.sql" | ||
| <!-- prettier-ignore --> | ||
| >? TIP: **Hint 1** | ||
| > | ||
| > (to be added) | ||
| <!-- prettier-ignore --> | ||
| >? TIP: **Hint 2** | ||
| > | ||
| > (to be added) | ||
| --- | ||
| ### Worked example | ||
| To help illustrate the stock movement within the locations, consider the locations and deliveries in the **Sample input**. | ||
| We'll walk through each of the deliveries and how they contribute to end-of-day stock levels. | ||
| Since each delivery and sale correspond to the same product, we'll omit mentioning the product ID in the following walkthrough. | ||
| #### 2024-01-01 | ||
| First, consider the deliveries: | ||
| - **Supplier 1** sends 25 units to **Depot 3**; **Depot 3** has 25 units from **Supplier 1** and 0 units from **Supplier 2**. | ||
| - **Supplier 2** sends 25 units to **Depot 4**; **Depot 4** has 0 units from **Supplier 1** and 25 units from **Supplier 2**. | ||
| - **Depot 4** sends 25 units to **Store 5**; all 25 units are originally from **Supplier 2** so: | ||
| - **Store 5** has 0 units from **Supplier 1** and 25 units from **Supplier 2**. | ||
| - **Depot 4** has 0 units from either supplier. | ||
| - **Supplier 1** sends 30 units to **Depot 3**; **Depot 3** has 55 units from **Supplier 1** and 0 units from **Supplier 2**. | ||
| Then the sales, which we can roll up to the end of the day: | ||
| - **Store 5** sells 14 units throughout the day; all units are from **Supplier 2** so **Store 5** has 0 units from **Supplier 1** and 11 units from **Supplier 2**. | ||
| Therefore, at the end of 2024-01-01, the proportion for **Store 5** is 100% from **Supplier 2**: | ||
| | stock_date | store_id | supplier_id | stock_volume | stock_proportion | | ||
| | :--------- | -------: | ----------: | -----------: | ---------------: | | ||
| | 2024-01-01 | 5 | 1 | 0 | 0.00 | | ||
| | 2024-01-01 | 5 | 2 | 11 | 100.00 | | ||
| #### 2024-01-02 | ||
| First, consider the deliveries: | ||
| - **Depot 3** sends 25 units to **Store 5**; all 25 units are from **Supplier 2** so: | ||
| - **Store 5** has 0 units from **Supplier 1** and 36 units from **Supplier 2**. | ||
| - **Depot 3** has 30 units from **Supplier 1** and 0 units from **Supplier 2**. | ||
| - **Depot 3** sends 30 units to **Depot 4**; all 30 units are from **Supplier 2** so: | ||
| - **Depot 4** has 0 units from **Supplier 1** and 30 units from **Supplier 2**. | ||
| - **Depot 3** has 0 units from either supplier. | ||
| - **Supplier 2** sends 20 units to **Depot 3**; **Depot 3** has 20 units from **Supplier 2** and 0 units from **Supplier 1**. | ||
| - **Supplier 1** sends 30 units to **Depot 4**; **Depot 4** has 30 units from **Supplier 1** and 30 units from **Supplier 2**. The 30 units from **Supplier 2** and first in the queue, followed by the 30 units from **Supplier 1**. | ||
| - **Depot 3** sends 20 units to **Store 5**; all 20 units are from **Supplier 2** so: | ||
| - **Store 5** has 0 units from **Supplier 1** and 56 units from **Supplier 2**. | ||
| - **Depot 3** has 0 units from either supplier. | ||
| - **Depot 4** sends 60 units to **Store 5**; 30 units are from **Supplier 1** and 30 units are from **Supplier 2** so: | ||
| - **Store 5** has 30 units from **Supplier 1** and 86 units from **Supplier 2**. The existing 56 units from **Supplier 2** are first in the queue, followed by the new 30 units from **Supplier 2**, followed by the 30 units from **Supplier 1**. | ||
| - **Depot 4** has 0 units from either supplier. | ||
| Then the sales, which we can roll up to the end of the day: | ||
| - **Store 5** sells 55 units throughout the day; all 86 units from **Supplier 2** are first in the queue, so **Store 5** has 30 units from **Supplier 1** and 31 units from **Supplier 2**. | ||
| Therefore, at the end of 2024-01-02, the proportion for **Store 5** is 49.18% from **Supplier 1** and 50.82% from **Supplier 2**: | ||
| | stock_date | store_id | supplier_id | stock_volume | stock_proportion | | ||
| | :--------- | -------: | ----------: | -----------: | ---------------: | | ||
| | 2024-01-02 | 5 | 1 | 30 | 49.18 | | ||
| | 2024-01-02 | 5 | 2 | 31 | 50.82 | | ||
| Combined with the output from 2024-01-01, the output is the same as the **Sample output**. |
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