Calculate out price in FIFO SQL - postgresql

Question

Using Postgres 11
Using FIFO, i would like to calculate the price of items taken from the inventory, to keep track of the value of the total inventory.
Dataset is as follows:
ID | prodno | amount_purchased | amount_taken | price | created_at
uuid 13976 10 NULL 130 <timestamp>
uuid 13976 10 NULL 150 <timestamp>
uuid 13976 10 NULL 110 <timestamp>
uuid 13976 10 NULL 100 <timestamp>
uuid 13976 NULL 14 ?? <timestamp>
Before inserting the row with amount_taken i would need to calculate what the avg price of each of the 14 items is, which in this case would be 135,71, but how to calculate this relatively efficient?
My initial idea was to delegate the rows into two temp tables, one where amount_taken is null, and one where it is not null, and then calculate all the rows down, but seeing as this table could become rather large, rather fast (since most of the time, only 1 item would be taken from the inventory), i worry this would be a decent solution in the short term, but would slow down, as the table becomes larger. So, what's the better solution internet?

Answer 1

Given this setup:
CREATE TABLE test (
id int
, prodno int
, quantity numeric
, price numeric
, created_at timestamp
);
INSERT INTO test VALUES
(1, 13976, 10, 130, NOW())
, (2, 13976, 10, 150, NOW()+'1 hours')
, (3, 13976, 10, 110, NOW()+'2 hours')
, (4, 13976, 10, 100, NOW()+'3 hours')
, (5, 13976, -14, NULL, NOW()+'4 hours')
, (6, 13976, -1, NULL, NOW()+'5 hours')
, (7, 13976, -10, NULL, NOW()+'6 hours')
;
then the SQL
SELECT id, prodno, created_at, qty_sold
-- 5
, round((cum_sold_cost - coalesce(lag(cum_sold_cost) over w, 0))/qty_sold, 2) as fifo_price
, qty_bought, prev_bought, total_cost
, prev_total_cost
, cum_sold_cost
, coalesce(lag(cum_sold_cost) over w, 0) as prev_cum_sold_cost
FROM (
SELECT id, tneg.prodno, created_at, qty_sold, tpos.qty_bought, prev_bought, total_cost, prev_total_cost
-- 4
, round(prev_total_cost + ((tneg.cum_sold - tpos.prev_bought)/(tpos.qty_bought - tpos.prev_bought))*(total_cost-prev_total_cost), 2) as cum_sold_cost
FROM (
SELECT id, prodno, created_at, -quantity as qty_sold
, sum(-quantity) over w as cum_sold
FROM test
WHERE quantity < 0
WINDOW w AS (PARTITION BY prodno ORDER BY created_at)
-- 1
) tneg
LEFT JOIN (
SELECT prodno
, sum(quantity) over w as qty_bought
, coalesce(sum(quantity) over prevw, 0) as prev_bought
, quantity * price as cost
, sum(quantity * price) over w as total_cost
, coalesce(sum(quantity * price) over prevw, 0) as prev_total_cost
FROM test
WHERE quantity > 0
WINDOW w AS (PARTITION BY prodno ORDER BY created_at)
, prevw AS (PARTITION BY prodno ORDER BY created_at ROWS BETWEEN unbounded preceding AND 1 preceding)
-- 2
) tpos
-- 3
ON tneg.cum_sold BETWEEN tpos.prev_bought AND tpos.qty_bought
AND tneg.prodno = tpos.prodno
) t
WINDOW w AS (PARTITION BY prodno ORDER BY created_at)
yields
| id | prodno | created_at | qty_sold | fifo_price | qty_bought | prev_bought | total_cost | prev_total_cost | cum_sold_cost | prev_cum_sold_cost |
|----+--------+----------------------------+----------+------------+------------+-------------+------------+-----------------+---------------+--------------------|
| 5 | 13976 | 2019-03-07 21:07:13.267218 | 14 | 135.71 | 20 | 10 | 2800 | 1300 | 1900.00 | 0 |
| 6 | 13976 | 2019-03-07 22:07:13.267218 | 1 | 150.00 | 20 | 10 | 2800 | 1300 | 2050.00 | 1900.00 |
| 7 | 13976 | 2019-03-07 23:07:13.267218 | 10 | 130.00 | 30 | 20 | 3900 | 2800 | 3350.00 | 2050.00 |
tneg contains information about quantities sold
| id | prodno | created_at | qty_sold | cum_sold |
|----+--------+----------------------------+----------+----------|
| 5 | 13976 | 2019-03-07 21:07:13.267218 | 14 | 14 |
| 6 | 13976 | 2019-03-07 22:07:13.267218 | 1 | 15 |
| 7 | 13976 | 2019-03-07 23:07:13.267218 | 10 | 25 |
tpos contains information about quantities bought
| prodno | qty_bought | prev_bought | cost | total_cost | prev_total_cost |
|--------+------------+-------------+------+------------+-----------------|
| 13976 | 10 | 0 | 1300 | 1300 | 0 |
| 13976 | 20 | 10 | 1500 | 2800 | 1300 |
| 13976 | 30 | 20 | 1100 | 3900 | 2800 |
| 13976 | 40 | 30 | 1000 | 4900 | 3900 |
We match rows in tneg with rows in tpos on the condition that cum_sold is between qty_bought and prev_bought.
cum_sold is the cumulative amount sold, qty_bought is the cumulative amount bought, and prev_bought is the previous value of qty_bought.
| id | prodno | created_at | qty_sold | cum_sold | qty_bought | prev_bought | total_cost | prev_total_cost | cum_sold_cost |
|----+--------+----------------------------+----------+----------+------------+-------------+------------+-----------------+---------------|
| 5 | 13976 | 2019-03-07 21:07:13.267218 | 14 | 14 | 20 | 10 | 2800 | 1300 | 1900.00 |
| 6 | 13976 | 2019-03-07 22:07:13.267218 | 1 | 15 | 20 | 10 | 2800 | 1300 | 2050.00 |
| 7 | 13976 | 2019-03-07 23:07:13.267218 | 10 | 25 | 30 | 20 | 3900 | 2800 | 3350.00 |
The fraction
((tneg.cum_sold - tpos.prev_bought)/(tpos.qty_bought - tpos.prev_bought)) as frac
measures how far cum_sold lies in between qty_bought and prev_bought. We use this fraction to compute
cum_sold_cost, the cumulative cost associated with buying cum_sold items.
cum_sold_cost lies frac distance between prev_total_cost and total_cost.
Once you obtain cum_sold_cost, you have everything you need to compute marginal FIFO unit prices.
For each line of tneg, the difference between cum_sold_cost and its previous value is the cost of the qty_sold.
FIFO price is simply the ratio of this cost and qty_sold.