创建一列以根据 FIFO 计算每批货物的余额部分答案

【问题标题】：Create a column to calculate the portion of balance for every shipment according to FIFO创建一列以根据 FIFO 计算每批货物的余额部分
【发布时间】：2025-12-29 14:45:12
【问题描述】：

我有一个查询来获取库存项目及其余额及其接收交易代码及其数量按日期排序，并且我想根据先进先出创建一个只有每个 RS 代码的比例余额的列原则。作为附件示例中的示例

我们有 StockItemId = (2222,2262,2263)。

并且预期的结果将是：如图所示，RS_Portion 中每一行的余额取决于代码的数量，每个项目的 RS_Portion 总和应等于同一项目的 GlobalBalance。

这是我的代码：

WITH M AS (

            SELECT WHS.StockItemId,SUM(WHS.OnHandBalance) AS GlobalBalance
            FROM Warehouse.WarehouseStocks WHS 
            GROUP BY WHS.StockItemId
            HAVING SUM(WHS.OnHandBalance) > 0 
            )
SELECT M.StockItemId,M.GlobalBalance,WHWorkOrderHeader.Code,WHWorkOrderDetails.Quantity,WHWorkOrderHeader.Date
FROM M
LEFT OUTER JOIN Warehouse.WHWorkOrderDetails
ON WHWorkOrderDetails.StockItemId = M.StockItemId
LEFT OUTER JOIN  Warehouse.WHWorkOrderHeader
ON  WHWorkOrderHeader.ID = WHWorkOrderDetails.WHWorkOrderHeaderId
WHERE WHWorkOrderHeader.Type = 'RS' AND M.StockItemId IN (2222,2262,2263)
ORDER BY M.StockItemId ASC, WHWorkOrderHeader.Date DESC

StockItemId,GlobalBalance,Code,Quantity,Date
2222,158.0000,RS-1-1543,1,2017-12-13 07:25:29.727
2222,158.0000,RS-1-1471,77,2017-08-22 14:53:11.880
2222,158.0000,RS-1-1470,77,2017-08-22 14:53:09.920
2222,158.0000,RS-1-1409,5,2017-02-16 13:41:00.740
2222,158.0000,RS-1-1409,5,2017-02-16 13:41:00.740
2222,158.0000,RS-1-1231,150,2015-09-29 15:41:45.000
2222,158.0000,RS-1-1226,100,2015-09-21 09:50:37.000
2262,23.0000,RS-14-371,20,2016-10-16 09:11:57.670
2262,23.0000,RS-14-334,30,2016-08-04 16:16:48.803
2262,23.0000,RS-14-303,18,2016-03-08 13:17:17.023
2262,23.0000,RS-14-301,70,2016-03-01 13:45:49.767
2262,23.0000,RS-14-298,30,2016-02-18 10:10:03.973
2262,23.0000,RS-14-286,2,2016-02-08 10:18:14.203
2262,23.0000,RS-14-285,30,2016-02-07 07:14:01.000
2262,23.0000,RS-14-280,3,2016-02-02 15:11:12.220
2262,23.0000,RS-14-276,1,2016-01-18 12:13:37.860
2262,23.0000,RS-14-274,2,2016-01-14 14:33:53.863
2262,23.0000,RS-14-273,1,2016-01-14 13:25:20.457
2262,23.0000,RS-14-271,1,2016-01-12 16:43:30.397
2262,23.0000,RS-14-270,4,2016-01-12 15:54:43.380
2262,23.0000,RS-14-268,1,2016-01-11 16:43:36.843
2262,23.0000,RS-14-267,1,2016-01-10 13:19:42.617
2262,23.0000,RS-14-266,1,2016-01-06 15:58:00.513
2262,23.0000,RS-14-261,1,2016-01-03 15:20:07.410
2262,23.0000,RS-14-259,6,2015-12-30 13:58:46.217
2262,23.0000,RS-14-258,1,2015-12-30 10:59:23.120
2262,23.0000,RS-14-250,3,2015-12-17 16:32:29.937
2262,23.0000,RS-14-245,1,2015-12-10 14:19:14.910
2262,23.0000,RS-14-240,1,2015-12-06 13:13:45.847
2262,23.0000,RS-14-236,1,2015-11-30 15:36:41.233
2262,23.0000,RS-14-233,4,2015-11-26 12:44:22.067
2262,23.0000,RS-14-228,1,2015-11-23 11:38:35.553
2262,23.0000,RS-14-226,1,2015-11-23 10:11:49.393
2262,23.0000,RS-14-223,2,2015-11-10 13:04:17.540
2263,25.0000,RS-14-301,60,2016-03-01 13:45:49.767
2263,25.0000,RS-14-298,20,2016-02-18 10:10:03.973
2263,25.0000,RS-14-295,1,2016-02-11 17:04:54.423
2263,25.0000,RS-14-294,1,2016-02-10 16:06:13.090
2263,25.0000,RS-14-293,2,2016-02-10 15:58:40.353
2263,25.0000,RS-14-276,1,2016-01-18 12:13:37.860
2263,25.0000,RS-14-274,2,2016-01-14 14:33:53.863
2263,25.0000,RS-14-271,1,2016-01-12 16:43:30.397
2263,25.0000,RS-14-268,1,2016-01-11 16:43:36.843
2263,25.0000,RS-14-267,1,2016-01-10 13:19:42.617
2263,25.0000,RS-14-266,1,2016-01-06 15:58:00.513
2263,25.0000,RS-14-259,6,2015-12-30 13:58:46.217
2263,25.0000,RS-14-258,1,2015-12-30 10:59:23.120
2263,25.0000,RS-14-250,3,2015-12-17 16:32:29.937
2263,25.0000,RS-14-240,1,2015-12-06 13:13:45.847
2263,25.0000,RS-14-236,1,2015-11-30 15:36:41.233
2263,25.0000,RS-14-223,2,2015-11-10 13:04:17.540

【问题讨论】：

你能解释一下，如何获得 RS_Portion 吗？还有 RS-1-1231 和 RS-1-1226 会发生什么？为什么它没有包含在预期结果中？
@Squirrel RS_Portion 根据每个 RS 代码的数量计算对于第一项 2222，最后一个 RS 代码是 1，第二个是 77，第三个是 77，最后一个是有 5 但 RS_Portion 的总和应该只等于 GloblaBalance 所以 158 = 1+77+77+3... 剩余的 RS 代码将有 0 因为我们在 RS-1-1231结束时得到 GlobalBalance 的值

标签： sql-server tsql sql-server-2016 fifo cumulative-sum

【解决方案1】：

这将为您提供您提供的输出：

WITH DataSource AS
(
    SELECT *
          ,[GlobalBalance] - SUM([Quantity]) OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC) AS [Dif]
          ,ROW_NUMBER() OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC) [RecordID]
    FROM @DataSource
), DataSourceWithPrevDiff AS
(
    SELECT *
          ,LAG([Dif], 1, NULL) OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC) AS [PrevDif]
    FROM DataSource
)
SELECT [StockItemId]
      ,[GlobalBalance]
      ,[Date]
      ,[Code]
      ,[Quantity]
     ,CASE WHEN [Dif] > 0 THEN [Quantity] 
           WHEN [RecordID] = 1 THEN [Quantity] + [Dif]
           WHEN [PrevDif] > 0 THEN [PrevDif]
      END AS [RS_Portion]
FROM DataSourceWithPrevDiff
WHERE [Dif] > 0 
    OR [PrevDif] > 0
    OR [RecordID] = 1
ORDER BY [StockItemId]
        ,[Date] DESC;

当然，您可以按部分拆分查询。这个想法是计算每个库存项目-余额对的总和，直到当前行。此外，我们还需要获取每对的第一项。然后在最后的查询中，我们显示第一个项目，即到目前为止总和为正的项目，或者之前总和为正的项目。

这是完整的工作示例：

DECLARE @DataSource TABLE
(
    [StockItemId] INT
   ,[GlobalBalance] INT
   ,[Code] VARCHAR(32)
   ,[Quantity] INT
   ,[Date] DATETIME2
);

INSERT INTO @DataSource ([StockItemId], [GlobalBalance], [Code], [Quantity], [Date])
VALUES   ('2222', '158', 'RS-1-1543', '1', '2017-12-13 07:25:29.727')
        ,('2222', '158', 'RS-1-1471', '77', '2017-08-22 14:53:11.880')
        ,('2222', '158', 'RS-1-1470', '77', '2017-08-22 14:53:09.920')
        ,('2222', '158', 'RS-1-1409', '5', '2017-02-16 13:41:00.740')
        ,('2222', '158', 'RS-1-1409', '5', '2017-02-16 13:41:00.740')
        ,('2222', '158', 'RS-1-1231', '150', '2015-09-29 15:41:45.000')
        ,('2222', '158', 'RS-1-1226', '100', '2015-09-21 09:50:37.000')
        ,('2262', '23', 'RS-14-371', '20', '2016-10-16 09:11:57.670')
        ,('2262', '23', 'RS-14-334', '30', '2016-08-04 16:16:48.803')
        ,('2262', '23', 'RS-14-303', '18', '2016-03-08 13:17:17.023')
        ,('2262', '23', 'RS-14-301', '70', '2016-03-01 13:45:49.767')
        ,('2262', '23', 'RS-14-298', '30', '2016-02-18 10:10:03.973')
        ,('2262', '23', 'RS-14-286', '2', '2016-02-08 10:18:14.203')
        ,('2262', '23', 'RS-14-285', '30', '2016-02-07 07:14:01.000')
        ,('2262', '23', 'RS-14-280', '3', '2016-02-02 15:11:12.220')
        ,('2262', '23', 'RS-14-276', '1', '2016-01-18 12:13:37.860')
        ,('2262', '23', 'RS-14-274', '2', '2016-01-14 14:33:53.863')
        ,('2262', '23', 'RS-14-273', '1', '2016-01-14 13:25:20.457')
        ,('2262', '23', 'RS-14-271', '1', '2016-01-12 16:43:30.397')
        ,('2262', '23', 'RS-14-270', '4', '2016-01-12 15:54:43.380')
        ,('2262', '23', 'RS-14-268', '1', '2016-01-11 16:43:36.843')
        ,('2262', '23', 'RS-14-267', '1', '2016-01-10 13:19:42.617')
        ,('2262', '23', 'RS-14-266', '1', '2016-01-06 15:58:00.513')
        ,('2262', '23', 'RS-14-261', '1', '2016-01-03 15:20:07.410')
        ,('2262', '23', 'RS-14-259', '6', '2015-12-30 13:58:46.217')
        ,('2262', '23', 'RS-14-258', '1', '2015-12-30 10:59:23.120')
        ,('2262', '23', 'RS-14-250', '3', '2015-12-17 16:32:29.937')
        ,('2262', '23', 'RS-14-245', '1', '2015-12-10 14:19:14.910')
        ,('2262', '23', 'RS-14-240', '1', '2015-12-06 13:13:45.847')
        ,('2262', '23', 'RS-14-236', '1', '2015-11-30 15:36:41.233')
        ,('2262', '23', 'RS-14-233', '4', '2015-11-26 12:44:22.067')
        ,('2262', '23', 'RS-14-228', '1', '2015-11-23 11:38:35.553')
        ,('2262', '23', 'RS-14-226', '1', '2015-11-23 10:11:49.393')
        ,('2262', '23', 'RS-14-223', '2', '2015-11-10 13:04:17.540')
        ,('2263', '25', 'RS-14-301', '60', '2016-03-01 13:45:49.767')
        ,('2263', '25', 'RS-14-298', '20', '2016-02-18 10:10:03.973')
        ,('2263', '25', 'RS-14-295', '1', '2016-02-11 17:04:54.423')
        ,('2263', '25', 'RS-14-294', '1', '2016-02-10 16:06:13.090')
        ,('2263', '25', 'RS-14-293', '2', '2016-02-10 15:58:40.353')
        ,('2263', '25', 'RS-14-276', '1', '2016-01-18 12:13:37.860')
        ,('2263', '25', 'RS-14-274', '2', '2016-01-14 14:33:53.863')
        ,('2263', '25', 'RS-14-271', '1', '2016-01-12 16:43:30.397')
        ,('2263', '25', 'RS-14-268', '1', '2016-01-11 16:43:36.843')
        ,('2263', '25', 'RS-14-267', '1', '2016-01-10 13:19:42.617')
        ,('2263', '25', 'RS-14-266', '1', '2016-01-06 15:58:00.513')
        ,('2263', '25', 'RS-14-259', '6', '2015-12-30 13:58:46.217')
        ,('2263', '25', 'RS-14-258', '1', '2015-12-30 10:59:23.120')
        ,('2263', '25', 'RS-14-250', '3', '2015-12-17 16:32:29.937')
        ,('2263', '25', 'RS-14-240', '1', '2015-12-06 13:13:45.847')
        ,('2263', '25', 'RS-14-236', '1', '2015-11-30 15:36:41.233')
        ,('2263', '25', 'RS-14-223', '2', '2015-11-10 13:04:17.540');

SELECT *
      ,SUM([Quantity]) OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC)
      ,[GlobalBalance] - SUM([Quantity]) OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC)
FROM @DataSource
ORDER BY [StockItemId]
        ,[Date] DESC;

WITH DataSource AS
(
    SELECT *
          ,[GlobalBalance] - SUM([Quantity]) OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC) AS [Dif]
          ,ROW_NUMBER() OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC) [RecordID]
    FROM @DataSource
), DataSourceWithPrevDiff AS
(
    SELECT *
          ,LAG([Dif], 1, NULL) OVER (PARTITION BY [StockItemId], [GlobalBalance] ORDER BY [Date] DESC) AS [PrevDif]
    FROM DataSource
)
SELECT [StockItemId]
      ,[GlobalBalance]
      ,[Date]
      ,[Code]
      ,[Quantity]
     ,CASE WHEN [Dif] > 0 THEN [Quantity] 
           WHEN [RecordID] = 1 THEN [Quantity] + [Dif]
           WHEN [PrevDif] > 0 THEN [PrevDif]
      END AS [RS_Portion]
FROM DataSourceWithPrevDiff
WHERE [Dif] > 0 
    OR [PrevDif] > 0
    OR [RecordID] = 1
ORDER BY [StockItemId]
        ,[Date] DESC;

【讨论】：

太棒了，非常感谢朋友的帮助，这个查询得到了预期的结果。

【解决方案2】：

由于您没有提供任何 DDL，因此我生成了示例数据，但它显然与您的相同。我希望您能够根据自己的需要进行相应的修改。试试这个：

declare @x table (stockItemId int, globalBalance int, quantity int, [date] date)
insert into @x values
(1,158, 1,'2018-03-31'),
(1,158, 77,'2018-03-30'),
(1,158, 77,'2018-03-29'),
(1,158, 5,'2018-03-28'),
(1,158, 5,'2018-03-27'),
(1,158, 150,'2018-03-26'),
(1,158, 100,'2018-03-25'),
(2,23, 20,'2018-03-31'),
(2,23, 77,'2018-03-30'),
(2,23, 77,'2018-03-29'),
(2,23, 5,'2018-03-28'),
(2,23, 5,'2018-03-27'),
(2,23, 150,'2018-03-26'),
(2,23, 100,'2018-03-25')


select stockItemId, globalBalance, quantity,
       case when cumsum < 0 then quantity + cumSum else quantity end [RS_Portion]
from (
    select stockItemId, globalBalance, quantity,
           globalBalance - SUM(quantity) over (partition by stockitemid order by [date] desc rows between unbounded preceding and current row) [cumSum]
    from @x
) a
--where [RS_Portion] > 0 - below is equivalent
where (case when cumsum < 0 then quantity + cumSum else quantity end) > 0

要了解此查询，您可能需要阅读有关使用窗口函数的 SQL 中的窗口函数和累积和。

【讨论】：

【解决方案3】：

假设您使用的是 SQL Server 2012 及更高版本，您可以使用 FIRST_VALUE 和 SUM() OVER (PARTITION BY) 来完成此操作

我不太明白你为什么忽略代码RS-1-1231 和RS-1-1226，它们在代码RS-1-1409 之前

更新！！

Demo

DECLARE @Table TABLE (StockItemId int , GlobalBalance money,Code nvarchar(50), Quantity  INT , [Date] DATETIME)
  INSERT INTO @Table
  VALUES 
  ('2222', '158', 'RS-1-1543', '1', '2017-12-13 07:25:29.727')
    ,('2222', '158', 'RS-1-1471', '77', '2017-08-22 14:53:11.880')
    ,('2222', '158', 'RS-1-1470', '77', '2017-08-22 14:53:09.920')
    ,('2222', '158', 'RS-1-1409', '5', '2017-02-16 13:41:00.740')
    ,('2222', '158', 'RS-1-1409', '5', '2017-02-16 13:41:00.740')
    ,('2222', '158', 'RS-1-1231', '150', '2015-09-29 15:41:45.000')
    ,('2222', '158', 'RS-1-1226', '100', '2015-09-21 09:50:37.000')
    ,('2262', '23', 'RS-14-371', '20', '2016-10-16 09:11:57.670')
    ,('2262', '23', 'RS-14-334', '30', '2016-08-04 16:16:48.803')
    ,('2262', '23', 'RS-14-303', '18', '2016-03-08 13:17:17.023')
    ,('2262', '23', 'RS-14-301', '70', '2016-03-01 13:45:49.767')
    ,('2262', '23', 'RS-14-298', '30', '2016-02-18 10:10:03.973')
    ,('2262', '23', 'RS-14-286', '2', '2016-02-08 10:18:14.203')
    ,('2262', '23', 'RS-14-285', '30', '2016-02-07 07:14:01.000')
    ,('2262', '23', 'RS-14-280', '3', '2016-02-02 15:11:12.220')
    ,('2262', '23', 'RS-14-276', '1', '2016-01-18 12:13:37.860')
    ,('2262', '23', 'RS-14-274', '2', '2016-01-14 14:33:53.863')
    ,('2262', '23', 'RS-14-273', '1', '2016-01-14 13:25:20.457')
    ,('2262', '23', 'RS-14-271', '1', '2016-01-12 16:43:30.397')
    ,('2262', '23', 'RS-14-270', '4', '2016-01-12 15:54:43.380')
    ,('2262', '23', 'RS-14-268', '1', '2016-01-11 16:43:36.843')
    ,('2262', '23', 'RS-14-267', '1', '2016-01-10 13:19:42.617')
    ,('2262', '23', 'RS-14-266', '1', '2016-01-06 15:58:00.513')
    ,('2262', '23', 'RS-14-261', '1', '2016-01-03 15:20:07.410')
    ,('2262', '23', 'RS-14-259', '6', '2015-12-30 13:58:46.217')
    ,('2262', '23', 'RS-14-258', '1', '2015-12-30 10:59:23.120')
    ,('2262', '23', 'RS-14-250', '3', '2015-12-17 16:32:29.937')
    ,('2262', '23', 'RS-14-245', '1', '2015-12-10 14:19:14.910')
    ,('2262', '23', 'RS-14-240', '1', '2015-12-06 13:13:45.847')
    ,('2262', '23', 'RS-14-236', '1', '2015-11-30 15:36:41.233')
    ,('2262', '23', 'RS-14-233', '4', '2015-11-26 12:44:22.067')
    ,('2262', '23', 'RS-14-228', '1', '2015-11-23 11:38:35.553')
    ,('2262', '23', 'RS-14-226', '1', '2015-11-23 10:11:49.393')
    ,('2262', '23', 'RS-14-223', '2', '2015-11-10 13:04:17.540')
    ,('2263', '25', 'RS-14-301', '60', '2016-03-01 13:45:49.767')
    ,('2263', '25', 'RS-14-298', '20', '2016-02-18 10:10:03.973')
    ,('2263', '25', 'RS-14-295', '1', '2016-02-11 17:04:54.423')
    ,('2263', '25', 'RS-14-294', '1', '2016-02-10 16:06:13.090')
    ,('2263', '25', 'RS-14-293', '2', '2016-02-10 15:58:40.353')
    ,('2263', '25', 'RS-14-276', '1', '2016-01-18 12:13:37.860')
    ,('2263', '25', 'RS-14-274', '2', '2016-01-14 14:33:53.863')
    ,('2263', '25', 'RS-14-271', '1', '2016-01-12 16:43:30.397')
    ,('2263', '25', 'RS-14-268', '1', '2016-01-11 16:43:36.843')
    ,('2263', '25', 'RS-14-267', '1', '2016-01-10 13:19:42.617')
    ,('2263', '25', 'RS-14-266', '1', '2016-01-06 15:58:00.513')
    ,('2263', '25', 'RS-14-259', '6', '2015-12-30 13:58:46.217')
    ,('2263', '25', 'RS-14-258', '1', '2015-12-30 10:59:23.120')
    ,('2263', '25', 'RS-14-250', '3', '2015-12-17 16:32:29.937')
    ,('2263', '25', 'RS-14-240', '1', '2015-12-06 13:13:45.847')
    ,('2263', '25', 'RS-14-236', '1', '2015-11-30 15:36:41.233')
    ,('2263', '25', 'RS-14-223', '2', '2015-11-10 13:04:17.540')

;WITH Main as
(
    SELECT * , FIRST_VALUE (Quantity) OVER (PARTITION BY StockItemId ORDER BY [Date]) FirstQuantity,
            SUM(Quantity) OVER (PARTITION BY StockItemId) SumAllPerItem,
            GlobalBalance - SUM(Quantity) OVER (PARTITION BY StockItemId ORDER BY [Date] DESC ) DiffFromMOstRecent
    FROM @Table
)
,Results as
(
SELECT StockItemId , GlobalBalance , Code , Quantity , [Date], DiffFromMOstRecent , 
       ISNULL(LAG(DiffFromMOstRecent) OVER (PARTITION BY StockItemId ORDER BY [Date] DESC), DiffFromMOstRecent) PrevRunningSum
       ,CASE WHEN Quantity = FirstQuantity THEN GlobalBalance - SumAllPerItem + Quantity ELSE Quantity END RS_Portion
FROM Main

)

,Final as
(
SELECT  StockItemId , GlobalBalance , Code , Quantity , [Date]
,CASE WHEN MAX(PrevRunningSum) OVER(PARTITION BY StockItemId)  < 0 then Quantity + MAX(PrevRunningSum) OVER(PARTITION BY StockItemId) 
      WHEN DiffFromMOstRecent < 0 THEN PrevRunningSum
      ELSE RS_Portion END RS_Portion

FROM Results 
)
SELECT * 
FROM Final
WHERE RS_Portion >= 0 
ORDER BY StockItemId , [Date] Desc

【讨论】：

他是无视他们，因为直到那里的全球平衡是空的。
我忽略了代码 RS-1-1231 和 RS-1-1226 因为我们得到了 GlobalBalance 的值当我们将最新 RS 的数量相加时，我们通过这样做得到 RS_Portion：计算 RS_Portion 的第一个值(Rs1) 我们看 If GB > Quantity1 then Rs1 =Quantity1 , RS2>> If (GB-Rs1 > Quantity2 then Rs2 = Quantity2, RS3>> if (GB - (Rs1+Rs2)
这是因为我没有考虑您在回答中提供的逻辑，我会更新解决方案