Background
I need to implement inventory valuation / costing using the FIFO (first-in, first-out) method.
I’m running Postgres 11 running on CentOS 7.
I’ve looked at, and tried, a fair number of hypotheses from SO and the wider internet (as well as searching my own print library which includes SQL Queries for Mere Mortals, PostgreSQL Up & Running, The SQL Cookbook, Practical Issues In Database Management, and other quality reference works), and to date, I can’t find a solution that works for closing inventory valuation.
(I’ve also tried reasoning it out on my own, but have failed to come up with a plausible appraoch)
NOTE In my case, I have permission to change the table structure, etc, of the setup, so I can add / remove / change anything in the setup as needed (such as, e.g., adding a direction column to the movements table, as some approaches I’ve tried have indicated, changing queries, etc etc)
Current setup
I have a table mockup_inv_movements:
CREATE TABLE the_schema.mockup_inv_movements (
id INTEGER NOT NULL PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
created_at TIMESTAMP WITH TIME ZONE DEFAULT now(),
sku TEXT,
adjustment_quantity NUMERIC,
unit_cost NUMERIC(19,2),
po_num INTEGER
);
and this view mockup_inv_movements_with_fifo_cost adds FIFO cost for sale / ‘out’ rows, calculated from a query (shown later below):
CREATE VIEW the_schema.mockup_inv_movements_with_fifo_cost AS (
select
i.id,
i.created_at,
i.po_num,
i.sku,
i.adjustment_quantity,
i.unit_cost,
m.fifo_unit_cost
FROM
the_schema.mockup_inv_movements i
LEFT OUTER JOIN
the_schema.fifo_hypothesis_2_mockup m
ON
i.id = m.id
ORDER BY i.id
);
Adding some test inventory movement data:
-- insert receipt / 'in' records
INSERT INTO the_schema.mockup_inv_movements (sku, adjustment_quantity, unit_cost, po_num, created_at )
VALUES ('foo_product',100,4,123, now()+'1 hour'), ('foo_product',10,3,987, now()+'2 hour'), ('foo_product',20,7,223, now()+'3 hours')
;
INSERT INTO the_schema.mockup_inv_movements (sku, adjustment_quantity, unit_cost, po_num, created_at )
VALUES ('bar_product',100,5,123, now()+'4 hours'),('bar_product',30,6,963, now()+'5 hours'),('bar_product',50,8,223, now()+'6 hours'),('bar_product',5,5,456, now()+'7 hours')
;
--insert sale / 'out' records
INSERT INTO the_schema.mockup_inv_movements (sku, adjustment_quantity, unit_cost, po_num, created_at )
VALUES ('bar_product',-50,null,null, now()+'8 hours'),('bar_product',-30, null,null, now()+'9 hours'),
('bar_product',-20,null,null, now()+'10 hours'),('bar_product',-10,null,null, now()+'11 hours')
;
INSERT INTO the_schema.mockup_inv_movements (sku, adjustment_quantity, unit_cost, po_num, created_at )
VALUES ('foo_product',-70,null,null, now()+'12 hours'), ('foo_product',-5,null,null, now()+'13 hours'),
('foo_product',-20,null,null, now()+'14 hours'),('foo_product',-10,null,null, now()+'15 hours')
;
OK, now here’s the query that calculates the ‘sale/out’ price for each, taken from this question, which seems to work; note that I’m only pulling in the column fifo_unit_cost from this query at the moment:
CREATE VIEW the_schema.fifo_hypothesis_2_mockup AS (
SELECT
id,
sku,
created_at AT TIME ZONE 'mst',
qty_sold,
-- 5
round((cumulative_sold_cost - coalesce(lag(cumulative_sold_cost) over w, 0))/qty_sold, 2) as fifo_unit_cost,
qty_bought,
prev_bought,
total_cost,
prev_total_cost,
cumulative_sold_cost,
coalesce(lag(cumulative_sold_cost) over w, 0) as prev_cumulative_sold_cost
FROM (
SELECT id,
tneg.sku,
created_at,
qty_sold,
tpos.qty_bought,
prev_bought,
total_cost,
prev_total_cost,
-- 4
round(prev_total_cost + ((tneg.cumulative_sold - tpos.prev_bought)/(tpos.qty_bought - tpos.prev_bought))*(total_cost-prev_total_cost), 2) as cumulative_sold_cost
FROM (
SELECT
id,
sku,
created_at,
-(adjustment_quantity) as qty_sold,
sum(-(adjustment_quantity)) over w as cumulative_sold
FROM the_schema.mockup_inv_movements
WHERE adjustment_quantity < 0
WINDOW w AS (PARTITION BY sku ORDER BY created_at)
-- 1
) tneg
LEFT JOIN (
SELECT
sku,
sum(adjustment_quantity) over w as qty_bought,
coalesce(sum(adjustment_quantity) over prevw, 0) as prev_bought,
adjustment_quantity * unit_cost as cost,
sum(adjustment_quantity * unit_cost) over w as total_cost,
coalesce(sum(adjustment_quantity * unit_cost) over prevw, 0) as prev_total_cost
FROM the_schema.mockup_inv_movements
WHERE adjustment_quantity > 0
WINDOW w AS (PARTITION BY sku ORDER BY created_at),
prevw AS (PARTITION BY sku ORDER BY created_at ROWS BETWEEN unbounded preceding AND 1 preceding)
-- 2
) tpos
-- 3
ON
((tneg.cumulative_sold > tpos.prev_bought )
AND ( tneg.cumulative_sold <= tpos.qty_bought ))
AND tneg.sku = tpos.sku
) t
WINDOW w AS (PARTITION BY sku ORDER BY created_at)
ORDER BY id
)
;
Now here’s the part where I’m having trouble.
I need to calculate the value of remaining stock / inventory on hand, also known as “closing stock” or “closing inventory.” I’ve tried a number of approaches including this question and this ‘set-based speed phreakery’ method, the latter of which I readily admit that I don’t fully comprehend,
The approach that has come closest to working for me is this older hypothesis from Ranjeet Rana, BUT although it does seem to assign the FIFO costs according to the correct breakdown, the sum of closing stock for each SKU does not seem to match the raw difference between ‘in’ and ‘out’ quantities.
Here’s the closing stock query adapted from Rana (comments mine; I left them in just in case they might indicate where my error is).
CREATE VIEW the_schema.closing_inv_hyp_3 AS (
select *,
case
when cumulative>0 and adjustment_quantity>=cumulative -- note that sale/out adjustment_quantity / cumulative is always less than zero
then cumulative*cost -- in this case, some amount of this row's receipt has been sold, and the remainder qty is shown in 'cumulative'
when cumulative>0 and adjustment_quantity<cumulative
then adjustment_quantity*cost -- in this case, none of this row's receipt has been sold, and so the entire adjustment amount is multiplied by unit cost
else 0 -- sale rows are assigned zero for this column
end as closing_stock
from (
select
*, -- all rows from subquery
sum(adjustment_quantity) over (order by srl) as cumulative -- THIS is the problematic column
from (
select
0 as srl, -- this ensures that all 'sale / out' rows float to the top
id,
sku,
adjustment_quantity,
COALESCE(fifo_unit_cost,unit_cost) AS cost,
created_at
from
the_schema.mockup_inv_movements_with_fifo_cost
where adjustment_quantity < 0 -- SALE / OUT only
UNION -- gets all from both queries (less any dupes)
select
row_number() over(order by created_at) as srl, -- this assigns a synthetic sequential row number to the 'PO / in' rows and ensures the are pushed to the bottom
id,
sku,
adjustment_quantity,
COALESCE(fifo_unit_cost,unit_cost) AS cost,
created_at
from
the_schema.mockup_inv_movements_with_fifo_cost
where
adjustment_quantity > 0 -- PO / IN only
ORDER BY srl
)as tab
) as maintab
);
With this in place, we should be able to get the sum of closing stock value per SKU with:
SELECT
sku,
sum(closing_stock) as closing_stock_sum_value
FROM
the_schema.closing_inv_hyp_3
WHERE closing_stock > 0
GROUP BY sku
ORDER by sku
;
However, as I mentioned, the totals do not match up with the basic inventory difference calculation (specifically in this test example, I would expect 75 units of bar_product to be represented in closing stock, whereas this query shows 100):
srl | id | sku | adjustment_quantity | cost | created_at | cumulative | closing_stock -----+-----+-------------+---------------------+------+-------------------------------+------------+--------------- 0 | 102 | foo_product | -70 | 4.00 | 2022-03-10 07:27:05.447572+00 | -215 | 0 0 | 100 | bar_product | -20 | 5.00 | 2022-03-10 05:27:05.447572+00 | -215 | 0 0 | 101 | bar_product | -10 | 6.00 | 2022-03-10 06:27:05.447572+00 | -215 | 0 0 | 103 | foo_product | -5 | 4.00 | 2022-03-10 08:27:05.447572+00 | -215 | 0 0 | 105 | foo_product | -10 | 3.50 | 2022-03-10 10:27:05.447572+00 | -215 | 0 0 | 99 | bar_product | -30 | 5.00 | 2022-03-10 04:27:05.447572+00 | -215 | 0 0 | 98 | bar_product | -50 | 5.00 | 2022-03-10 03:27:05.447572+00 | -215 | 0 0 | 104 | foo_product | -20 | 4.00 | 2022-03-10 09:27:05.447572+00 | -215 | 0 1 | 91 | foo_product | 100 | 4.00 | 2022-03-09 20:27:05.447572+00 | -115 | 0 2 | 92 | foo_product | 10 | 3.00 | 2022-03-09 21:27:05.447572+00 | -105 | 0 3 | 93 | foo_product | 20 | 7.00 | 2022-03-09 22:27:05.447572+00 | -85 | 0 4 | 94 | bar_product | 100 | 5.00 | 2022-03-09 23:27:05.447572+00 | 15 | 75.00 5 | 95 | bar_product | 30 | 6.00 | 2022-03-10 00:27:05.447572+00 | 45 | 180.00 6 | 96 | bar_product | 50 | 8.00 | 2022-03-10 01:27:05.447572+00 | 95 | 400.00 7 | 97 | bar_product | 5 | 5.00 | 2022-03-10 02:27:05.447572+00 | 100 | 25.00 (15 rows)
It seems like this would be the kind of thing that has a more-or-less standardized solution, but so far none of the resources I’ve found / tried has guided me to a working approach.
How can I accurately do FIFO closing stock / inventory valuation in Postgres?
All guidance much appreciated!
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Answer
Using “Set-based Speed Phreakery: The FIFO Stock Inventory SQL Problem” as an example, re-working that approach for Postgres and the change of table/columns produces this query:
/* Sum up the ins and outs to calculate the remaining stock level */
WITH cteStockSum
AS ( SELECT sku ,
SUM(adjustment_quantity) AS TotalStock
FROM mockup_inv_movements
GROUP BY sku
)
, cteReverseInSum
AS ( SELECT s.sku ,
s.created_at ,
( SELECT SUM(i.adjustment_quantity)
FROM mockup_inv_movements AS i
WHERE i.sku = s.sku
AND i.adjustment_quantity > 0
AND i.created_at >= s.created_at
) AS RollingStock ,
s.adjustment_quantity AS ThisStock
FROM mockup_inv_movements AS s
WHERE s.adjustment_quantity > 0
)
/* Using the rolling balance above find the first stock movement in that meets
(or exceeds) our required stock level */
/* and calculate how much stock is required from the earliest stock in */
, cteWithLastTranDate
AS ( SELECT w.sku ,
w.TotalStock ,
LastPartialStock.created_at ,
LastPartialStock.StockToUse ,
LastPartialStock.RunningTotal ,
w.TotalStock - LastPartialStock.RunningTotal
+ LastPartialStock.StockToUse AS UseThisStock
FROM cteStockSum AS w
CROSS JOIN LATERAL ( SELECT
z.created_at ,
z.ThisStock AS StockToUse ,
z.RollingStock AS RunningTotal
FROM cteReverseInSum AS z
WHERE z.sku = w.sku
AND z.RollingStock >= w.TotalStock
ORDER BY z.created_at DESC
LIMIT 1
) AS LastPartialStock
)
/* Sum up the cost of 100% of the stock movements in after the returned stockid and for that stockid we need 'UseThisStock' items' */
SELECT y.sku ,
y.TotalStock AS CurrentItems ,
SUM(CASE WHEN e.created_at = y.created_at THEN y.UseThisStock
ELSE e.adjustment_quantity
END * Price.unit_cost) AS CurrentValue
FROM cteWithLastTranDate AS y
INNER JOIN mockup_inv_movements AS e
ON e.SKU = y.SKU
AND e.created_at >= y.created_at
AND e.adjustment_quantity > 0
CROSS JOIN LATERAL (
/* Find the Price of the item in */ SELECT
p.unit_cost
FROM mockup_inv_movements AS p
WHERE p.SKU = e.SKU
AND p.created_at <= e.created_at
AND p.adjustment_quantity > 0
ORDER BY p.created_at DESC
LIMIT 1
) AS Price
GROUP BY y.sku ,y.TotalStock
ORDER BY y.sku
and from your sample data the result produced is this:
+-------------+--------------+--------------+ | sku | currentitems | currentvalue | +-------------+--------------+--------------+ | bar_product | 75 | 545.00 | | foo_product | 25 | 155.00 | +-------------+--------------+--------------+
also see: https://dbfiddle.uk/?rdbms=postgres_11&fiddle=f564a6cfda3374c2057b437f845a4bdf