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XLeratorDB/financial Documentation

SQL Server weighted average cost function


WACtvf
Updated: 5 Apr 2017

Use the SQL Server table-valued function WACtvf to calculate running weighted-average cost values in an ordered resultant table. WACtvf calculates balances for each value from the first value to the last value in the ordered group or partition. WACtvf returns:

  • quantity on-hand
  • inventory cost
  • cost of goods sold
  • gross margin on sale
  • gross margin percentage
  • average inventory price
  • last inventory price
  • cumulative cost of goods sold
  • cumulative gross margin on sale
  • cumulative gross margin percentage.

WACtvf supports both long and short inventory positions. In other words, if the quantity on hand falls below the zero, WACtvf calculates from the last sale or withdrawal transaction, rather than from the last purchases or additions to inventory.

WACtvf assumes that the quantity (i.e. the number of units) of the transaction and the monetary value of the transaction have the same sign. WACtvf adds NULL values to the inventory at the last price.

WACtvf requires monotonically ascending row numbers within a partition.

WACtvf resets all calculated values when the row number value is 1.

Syntax
SELECT * FROM [wct].[WACtvf] (
   <@DataQuery, nvarchar(max),>)
Arguments
Input NameDescription
@DataQueryAn SQL statement which returns a resultant table containing the unique transaction identifier, ROW_NUMBER(), quantity and amount.
Return Type
RETURNS  TABLE (
    [ID] [sql_variant] NULL,
    [QTY] [float] NULL,
    [EB] [float] NULL,
    [GM] [float] NULL,
    [COGS] [float] NULL,
    [UP] [float] NULL,
    [LP] [float] NULL,
    [COGSC] [float] NULL,
    [GMC] [float] NULL,
    [GMP] [float] NULL,
    [CGMP] [float] NULL
)
ColumnDescription
IDunique transaction identifier
QTYinventory quantity
EBinventory value
GMgross margin on sale
COGScost of goods sold
UPaverage inventory price per unit
LPlast item-added price
COGSCcumulative cost-of-goods-sold
GMCcumulative gross margin
GMPgross margin percentage
CGMPcumulative gross margin percentage
Remarks
  • If the 3rd column (quantity) of the resultant table from @DataQuery contains NULL an error will be generated.
  • This function relies on the SQL Server ROW_NUMBER() function and expects results to be returned in ascending ROW_NUMBER() order within a PARTITION.
  • When ROW_NUMBER()= 1 all inventory values are re-initialized.
  • Available in XLeratorDB / financial 2008 only
Examples
Example #1

In the following examples, we calculate weighted-average cost inventory values for stock trades in the ABC, XYZ, and GHI companies. We will create a temporary table, #c and populate it with some data. We can be either short or long the shares at any point in time.

--Create the temporary table
CREATE TABLE #c   (
      trn               int,
      sym               char(3),
      tDate             date,
      qty               money,
      price_unit        money,
      price_extended    money,
      PRIMARY KEY (trn)
      )
 
--Populate the table with some data
INSERT INTO #c VALUES (01131019,'XYZ','2013-10-19',424,25.13,10655.12)
INSERT INTO #c VALUES (02130617,'ABC','2013-06-17',313,12.93,4047.09)
INSERT INTO #c VALUES (03130308,'ABC','2013-03-08',-157,13.17,-2067.69)
INSERT INTO #c VALUES (04130516,'GHI','2013-05-16',160,34.48,5516.8)
INSERT INTO #c VALUES (05130706,'XYZ','2013-07-06',-170,23.46,-3988.2)
INSERT INTO #c VALUES (06130924,'GHI','2013-09-24',328,34.95,11463.6)
INSERT INTO #c VALUES (07130722,'ABC','2013-07-22',599,13.65,8176.35)
INSERT INTO #c VALUES (08131231,'ABC','2013-12-31',-145,13.19,-1912.55)
INSERT INTO #c VALUES (09131025,'XYZ','2013-10-25',-153,24.31,-3719.43)
INSERT INTO #c VALUES (10130908,'ABC','2013-09-08',-386,13.65,-5268.9)
INSERT INTO #c VALUES (11130906,'XYZ','2013-09-06',-13,23.97,-311.61)
INSERT INTO #c VALUES (12130621,'ABC','2013-06-21',-326,12.73,-4149.98)
INSERT INTO #c VALUES (13131221,'GHI','2013-12-21',72,34.38,2475.36)
INSERT INTO #c VALUES (14130705,'XYZ','2013-07-05',-277,25.01,-6927.77)
INSERT INTO #c VALUES (15130307,'GHI','2013-03-07',559,35.21,19682.39)
INSERT INTO #c VALUES (16131107,'ABC','2013-11-07',27,12.68,342.36)
INSERT INTO #c VALUES (17130924,'GHI','2013-09-24',-291,35.69,-10385.79)
INSERT INTO #c VALUES (18140125,'GHI','2014-01-25',-78,35.46,-2765.88)
INSERT INTO #c VALUES (19130516,'XYZ','2013-05-16',315,23.57,7424.55)
INSERT INTO #c VALUES (20130518,'ABC','2013-05-18',298,13.23,3942.54)
INSERT INTO #c VALUES (21131103,'XYZ','2013-11-03',-326,23.24,-7576.24)
INSERT INTO #c VALUES (22131012,'XYZ','2013-10-12',596,23.16,13803.36)
INSERT INTO #c VALUES (23130619,'XYZ','2013-06-19',296,23.46,6944.16)
INSERT INTO #c VALUES (24130418,'XYZ','2013-04-18',275,24.83,6828.25)
INSERT INTO #c VALUES (25130408,'ABC','2013-04-08',298,12.98,3868.04)
INSERT INTO #c VALUES (26130320,'ABC','2013-03-20',-92,13.64,-1254.88)
INSERT INTO #c VALUES (27130906,'XYZ','2013-09-06',-147,23.81,-3500.07)
INSERT INTO #c VALUES (28131209,'XYZ','2013-12-09',315,24.46,7704.9)
INSERT INTO #c VALUES (29130602,'XYZ','2013-06-02',114,24.29,2769.06)
INSERT INTO #c VALUES (30130519,'XYZ','2013-05-19',467,23.15,10811.05)
INSERT INTO #c VALUES (31140205,'XYZ','2014-02-05',42,24.39,1024.38)
INSERT INTO #c VALUES (32130310,'ABC','2013-03-10',-63,12.61,-794.43)
INSERT INTO #c VALUES (33140102,'XYZ','2014-01-02',-196,22.98,-4504.08)
INSERT INTO #c VALUES (34130507,'XYZ','2013-05-07',55,23.43,1288.65)
INSERT INTO #c VALUES (35130321,'XYZ','2013-03-21',275,24.83,6828.25)
INSERT INTO #c VALUES (36130917,'XYZ','2013-09-17',92,24.6,2263.2)
INSERT INTO #c VALUES (37130220,'XYZ','2013-02-20',528,23.54,12429.12)
INSERT INTO #c VALUES (38130311,'XYZ','2013-03-11',-193,24.9,-4805.7)
INSERT INTO #c VALUES (39130908,'ABC','2013-09-08',490,12.69,6218.1)
INSERT INTO #c VALUES (40131013,'XYZ','2013-10-13',359,23.91,8583.69)
INSERT INTO #c VALUES (41130310,'ABC','2013-03-10',463,13.38,6194.94)
INSERT INTO #c VALUES (42131011,'XYZ','2013-10-11',-250,23.12,-5780)
INSERT INTO #c VALUES (43130521,'GHI','2013-05-21',-174,34.2,-5950.8)
INSERT INTO #c VALUES (44130227,'XYZ','2013-02-27',357,22.86,8161.02)
INSERT INTO #c VALUES (45131030,'XYZ','2013-10-30',-350,23.36,-8176)
INSERT INTO #c VALUES (46130301,'ABC','2013-03-01',157,13.01,2042.57)
INSERT INTO #c VALUES (47130619,'XYZ','2013-06-19',413,25.18,10399.34)
INSERT INTO #c VALUES (48130430,'ABC','2013-04-30',229,13.32,3050.28)
INSERT INTO #c VALUES (49130508,'ABC','2013-05-08',238,12.79,3044.02)
INSERT INTO #c VALUES (50131103,'GHI','2013-11-03',-246,35.61,-8760.06)
INSERT INTO #c VALUES (51131206,'GHI','2013-12-06',85,33.64,2859.4)
INSERT INTO #c VALUES (52131014,'GHI','2013-10-14',-91,33.12,-3013.92)
INSERT INTO #c VALUES (53140102,'GHI','2014-01-02',396,35.52,14065.92)
INSERT INTO #c VALUES (54130831,'XYZ','2013-08-31',-61,23.23,-1417.03)
INSERT INTO #c VALUES (55130630,'XYZ','2013-06-30',-272,23.45,-6378.4)
INSERT INTO #c VALUES (56130419,'GHI','2013-04-19',416,34.46,14335.36)
INSERT INTO #c VALUES (57130813,'XYZ','2013-08-13',-163,23.65,-3854.95)
INSERT INTO #c VALUES (58130722,'XYZ','2013-07-22',-88,24.64,-2168.32)
INSERT INTO #c VALUES (59130320,'XYZ','2013-03-20',-20,23.59,-471.8)
INSERT INTO #c VALUES (60130419,'XYZ','2013-04-19',277,22.83,6323.91)
INSERT INTO #c VALUES (61130916,'ABC','2013-09-16',-202,12.94,-2613.88)
INSERT INTO #c VALUES (62131027,'XYZ','2013-10-27',-248,24.71,-6128.08)
INSERT INTO #c VALUES (63130806,'GHI','2013-08-06',445,33.5,14907.5)
INSERT INTO #c VALUES (64140109,'XYZ','2014-01-09',-253,24.46,-6188.38)
INSERT INTO #c VALUES (65131227,'GHI','2013-12-27',376,33.49,12592.24)
INSERT INTO #c VALUES (66140203,'XYZ','2014-02-03',459,23.7,10878.3)
INSERT INTO #c VALUES (67130302,'XYZ','2013-03-02',9,24.04,216.36)
INSERT INTO #c VALUES (68130223,'ABC','2013-02-23',238,12.78,3041.64)
INSERT INTO #c VALUES (69130403,'GHI','2013-04-03',-151,33.16,-5007.16)
INSERT INTO #c VALUES (70130702,'GHI','2013-07-02',-162,35.48,-5747.76)
INSERT INTO #c VALUES (71130731,'ABC','2013-07-31',79,13.55,1070.45)
INSERT INTO #c VALUES (72140204,'XYZ','2014-02-04',-208,24.36,-5066.88)
INSERT INTO #c VALUES (73131028,'GHI','2013-10-28',-46,34.65,-1593.9)
INSERT INTO #c VALUES (74130619,'XYZ','2013-06-19',202,22.95,4635.9)
INSERT INTO #c VALUES (75131216,'GHI','2013-12-16',500,33.55,16775)
INSERT INTO #c VALUES (76131009,'ABC','2013-10-09',249,13.47,3354.03)
INSERT INTO #c VALUES (77130825,'GHI','2013-08-25',272,33.86,9209.92)
INSERT INTO #c VALUES (78140112,'XYZ','2014-01-12',332,24.28,8060.96)
INSERT INTO #c VALUES (79131223,'XYZ','2013-12-23',-376,25.12,-9445.12)
INSERT INTO #c VALUES (80140126,'XYZ','2014-01-26',404,24.23,9788.92)
INSERT INTO #c VALUES (81131123,'GHI','2013-11-23',187,33.86,6331.82)
INSERT INTO #c VALUES (82140131,'XYZ','2014-01-31',548,23.65,12960.2)
INSERT INTO #c VALUES (83130428,'XYZ','2013-04-28',142,23.13,3284.46)
INSERT INTO #c VALUES (84140104,'XYZ','2014-01-04',261,24.46,6384.06)
INSERT INTO #c VALUES (85131002,'GHI','2013-10-02',-295,32.51,-9590.45)
INSERT INTO #c VALUES (86131114,'ABC','2013-11-14',-386,12.4,-4786.4)
INSERT INTO #c VALUES (87131116,'GHI','2013-11-16',-320,34.16,-10931.2)
INSERT INTO #c VALUES (88131009,'ABC','2013-10-09',187,13.36,2498.32)
INSERT INTO #c VALUES (89130611,'ABC','2013-06-11',130,13.02,1692.6)
INSERT INTO #c VALUES (90130430,'ABC','2013-04-30',-191,13.49,-2576.59)
INSERT INTO #c VALUES (91130615,'XYZ','2013-06-15',545,23.88,13014.6)
INSERT INTO #c VALUES (92130924,'XYZ','2013-09-24',-248,24.58,-6095.84)
INSERT INTO #c VALUES (93130622,'ABC','2013-06-22',-227,13.47,-3057.69)
INSERT INTO #c VALUES (94131117,'GHI','2013-11-17',-92,32.65,-3003.8)
INSERT INTO #c VALUES (95130908,'GHI','2013-09-08',103,35.42,3648.26)
INSERT INTO #c VALUES (96130716,'GHI','2013-07-16',-347,35.29,-12245.63)
INSERT INTO #c VALUES (97131125,'XYZ','2013-11-25',-278,23.18,-6444.04)
INSERT INTO #c VALUES (98130413,'XYZ','2013-04-13',243,24.25,5892.75)
INSERT INTO #c VALUES (99130515,'XYZ','2013-05-15',97,25.13,2437.61)
 
--Calculate the WAC Values and store in temp table
SELECT
     CAST([ID] as Int) as ID
    ,[QTY]
    ,[EB]
    ,[GM]
    ,[COGS]
    ,[UP]
    ,[LP]
    ,[COGSC]
    ,[GMC]
    ,[GMP]
    ,[CGMP]
 
INTO
    #WAC
FROM
    wct.WACtvf('SELECT trn, ROW_NUMBER() OVER (PARTITION by sym ORDER BY sym, tDate, trn),qty,price_extended FROM #c ORDER BY sym, tDate, trn')
 
--JOIN to the source data to produce the inventory output
SELECT
     c.trn
    ,c.sym
    ,c.tDate
    ,c.qty
    ,c.price_unit
    ,c.price_extended
    ,w.[QTY] as [Inventory-On-Hand]
    ,w.[EB] as [Inventory Cost]
    ,w.[GM] as [Gross Margin on Sales]
    ,w.[COGS] as [Cost-of-Goods Sold]
    ,w.[UP] as [Average Price]
    ,w.[LP] as [Last Price]
    ,w.[COGSC] as [Cumulative COGS]
    ,w.[GMC] as [Cumulative Gross Margin]
    ,w.[GMP] as [GM Percentage]
    ,w.[CGMP] as [Cumulative GM Percentage]
FROM
    #c c
INNER JOIN
    #WAC w
ON
    c.trn = w.[ID]
ORDER BY
    c.sym, c.tDate, c.trn

This produces the following result.

Example #2

In this example, we will look at a simple FX blotter. In FX trading, it is not at all unusual to switch from a long currency position to a short currency position during the course of a day. Additionally, if you are trading something like the US dollars against the Euro, if you are long US dollars, you are short the Euro. Thus, it’s critical to pay attention to the sign. We will create the #fx table and populate it with some data.

--Create the temporary table
CREATE TABLE #fx (
      trn        int,
      ccy        char(3),
      amt_ccy    money,
      rate        float,
      ctr        char(3),
      amt_ctr    money,
      PRIMARY KEY (trn)
      )
 
--Populate the table with some data
INSERT INTO #fx VALUES (101,'GBP',8000000,1.619,'USD',-12952000)
INSERT INTO #fx VALUES (102,'GBP',-10000000,1.62,'USD',16200000)
INSERT INTO #fx VALUES (103,'GBP',-4000000,1.613,'USD',6452000)
INSERT INTO #fx VALUES (104,'GBP',7000000,1.618,'USD',-11326000)
INSERT INTO #fx VALUES (105,'GBP',6000000,1.623,'USD',-9738000)
INSERT INTO #fx VALUES (106,'GBP',-5000000,1.618,'USD',8090000)
INSERT INTO #fx VALUES (107,'GBP',-10000000,1.602,'USD',16020000)
INSERT INTO #fx VALUES (108,'GBP',2000000,1.608,'USD',-3216000)
INSERT INTO #fx VALUES (109,'GBP',-2000000,1.602,'USD',3204000)
INSERT INTO #fx VALUES (110,'GBP',10000000,1.626,'USD',-16260000)
 
--Calculate the WAC Values and store in temp table
SELECT
     CAST([ID] as Int) as ID
    ,[QTY]
    ,[EB]
    ,[GM]
    ,[COGS]
    ,[UP]
    ,[LP]
    ,[COGSC]
    ,[GMC]
    ,[GMP]
    ,[CGMP]
 
INTO
    #WAC
FROM
    wct.WACtvf('SELECT trn, ROW_NUMBER() OVER (ORDER BY trn),amt_ccy,-amt_ctr FROM #fx ORDER BY trn')
 
--JOIN to the source data to produce the inventory output
SELECT
     c.trn
    ,c.ccy
    ,c.amt_ccy
    ,c.rate
    ,c.ctr
    ,c.amt_ctr
    ,w.[QTY] as [GBP Position]
    ,w.[EB] as [USD Position]
    ,w.[GM] as [P/L]
    ,w.[COGS] as [Cost-of-Goods Sold]
    ,w.[UP] as [Average Price]
    ,w.[LP] as [Last Price]
    ,w.[COGSC] as [Cumulative COGS]
    ,w.[GMC] as [Cumulative P/L]
    ,w.[GMP] as [GM Percentage]
    ,w.[CGMP] as [Cumulative GM Percentage]
FROM
    #fx c
INNER JOIN
    #WAC w
ON
    c.trn = w.[ID]
ORDER BY
    c.trn

This produces the following result.

Example #3

Using the #c temp table created in Example #1, we will insert the necessary input for the WACtvf into the #inv temp table containing an IDENTITY column which is then used to link the input data to the output data. This simplifies the @DataQuery SQL.

--put the #c data into #inv
SELECT
     IDENTITY(int,1,1) as id
    ,trn
    ,ROW_NUMBER() OVER (PARTITION by sym ORDER BY sym, tDate, trn) as rn
    ,qty
    ,price_extended
INTO
    #inv
FROM
    #c
ORDER BY
    sym, tDate, trn
 
--Calculate the WAC Values and store in temp table
SELECT
     CAST([ID] as Int) as ID
    ,[QTY]
    ,[EB]
    ,[GM]
    ,[COGS]
    ,[UP]
    ,[LP]
    ,[COGSC]
    ,[GMC]
    ,[GMP]
    ,[CGMP]
INTO
    #WAC
FROM
    wct.WACtvf('SELECT id,rn,qty,price_extended FROM #inv ORDER BY id')
 
--JOIN to the source data to produce the inventory output
SELECT
     i.trn
    ,c.sym
    ,c.tDate
    ,i.qty
    ,c.price_unit
    ,i.price_extended
    ,w.[QTY] as [Inventory-On-Hand]
    ,w.[EB] as [Inventory Cost]
    ,w.[GM] as [Gross Margin on Sales]
    ,w.[COGS] as [Cost-of-Goods Sold]
    ,w.[UP] as [Average Price]
    ,w.[LP] as [Last Price]
    ,w.[COGSC] as [Cumulative COGS]
    ,w.[GMC] as [Cumulative Gross Margin]
    ,w.[GMP] as [GM Percentage]
    ,w.[CGMP] as [Cumulative GM Percentage]
FROM
    #inv i
INNER JOIN
    #WAC w
ON
    i.ID = w.[ID]
INNER JOIN
    #c c
ON
    i.trn = c.trn
ORDER BY
    i.id

This produces the following result.

Example #4

In this example we require multiple columns to uniquely identify the inventory items and there are no unique transaction identifiers.

--Put some data into #p
SELECT
    *
INTO
    #p
FROM (VALUES
     ('ZYX','Coffee Mug','Ceramic','8 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','Black','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','8 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','White','2017-01-15',50,1.12,56)
    ,('ZYX','Coffee Mug','Plastic','8 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','Black','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','White','2017-01-15',50,0.89,44.5)
    ,('ZYX','Coffee Mug','Ceramic','8 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','8 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','12 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','16 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Ceramic','20 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','Black','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','12 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','16 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','20 oz','White','2017-02-15',50,1.15,57.5)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-16',-5,2.95,-14.75)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-23',-2,2.95,-5.9)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-01-24',-1,2.95,-2.95)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-01',-3,2.95,-8.85)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-08',-7,2.95,-20.65)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-15',-6,2.95,-17.7)
    ,('ZYX','Coffee Mug','Plastic','8 oz','White','2017-02-22',-2,2.95,-5.9)
    )n([Manufacturer],[Description],[Material],[Size],[Color],[Date],[qty],[unit price],[extended price])
 
--Copy the #p data into #p2 and put it in order for WAC processing
SELECT
     IDENTITY(int,1,1) as ID
    ,ROW_NUMBER() OVER (PARTITION BY [Manufacturer],[Description],[Material],[Size],[Color] ORDER BY [Manufacturer],[Description],[Material],[Size],[Color],[Date],qty DESC) as rn
    ,#p.*
INTO
    #p2
FROM
    #p
ORDER BY
     [Manufacturer]
    ,[Description]
    ,[Material]
    ,[Size]
    ,[Color]
    ,[Date]
    ,qty DESC
 
--Put the tvf results into the #WAC table
SELECT
     CAST([ID] as Int) as ID
    ,[QTY]
    ,[EB]
    ,[GM]
    ,[COGS]
    ,[UP]
    ,[LP]
    ,[COGSC]
    ,[GMC]
    ,[GMP]
    ,[CGMP]
INTO
    #WAC
FROM
    wct.WACtvf('SELECT ID, rn,qty,[extended price] FROM #p2 ORDER BY ID')
 
--JOIN to the source data to produce the inventory output
SELECT
     #p2.[Manufacturer]
    ,#p2.[Description]
    ,#p2.[Material]
    ,#p2.[Size]
    ,#p2.[Color]
    ,#p2.[Date]
    ,#p2.[qty]
    ,#p2.[unit price]
    ,#p2.[extended price]
    ,w.[QTY] as [Inventory-On-Hand]
    ,w.[EB] as [Inventory Cost]
    ,w.[GM] as [Gross Margin on Sales]
    ,w.[COGS] as [Cost-of-Goods Sold]
    ,w.[UP] as [Average Price]
    ,w.[LP] as [Last Price]
    ,w.[COGSC] as [Cumulative COGS]
    ,w.[GMC] as [Cumulative Gross Margin]
    ,w.[GMP] as [GM Percentage]
    ,w.[CGMP] as [Cumulative GM Percentage]
FROM
    #p2
INNER JOIN
    #WAC w
ON
    #p2.ID = w.[ID]
ORDER BY
    #p2.id

This produces the following result.

See Also


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