I have a table in Access that is setup like the one in the photo. What I need to do is this:
For each part no, I want to sum the total Qty for each month and type (Ordered and Demand). Then I need to cap the qty in the rows where the type is = to Orders to the value of the Qty where the type is = to Orders, when the sum of the Qty for Ordered is greater than Demand. Let me try to explain it another way.
I want to look at a subset of the master data, in this case the subset is by part no (rows with identical part numbers). For this subset I want to have two sets of sums. 1. The sum of qty with type = Ordered AND 2. a sum of qty with type = Demand. If the sum for Ordered is greater than demand, I want to change the Qty for Ordered to be the value of the Qty for Demand.
Essentially, the business reason is that for reporting purposes the total Qty for Ordered shouldn't be more than Demand in a given month, for a part number.
Looking at the photo, the rows in red will need to change because the sum of the qty is 30, which is greater than the sum of qty for the green rows (25). The red rows qty should be changed to 20 and 5 to match the green rows.
Whew, hope this made sense because it is hard to explain. I have tried many things for a couple weeks now, and I am a bit fuzzy on the details so I will just give a high level. Ok so what have I tried:
I have tried to join the table to iself, using part no (and date I believe) to join on, but that doesn't work because the sum would somehow be incorrect sometimes.
Pivot the table, using the transform and pivot functions in Access but it's important for me to keep the individual dates in tact and when I pivoted it I had to roll it up on a month basis. This gives me the row structure I need to make the changes but I don't know how to get back the original date format after I am done.
I am guessing I need some VBA code that loops through each part no, but I am not big on VBA code and I don't have much time to learn it. Any suggestions? I know this is long winded but its a complicated problem (at least for me). Thanks in advance.
Related
Ok, I have watched many videos and read all sorts and I think I am nearly there, but must be missing something. In the data model I am trying to add the ytd calc to my product_table. I don't have unique dates in the product_table in column a and also they are weekly dates. I have all data for 2018 for each week of this year in set rows of 20, incrementing by one week every 20 rows. E.g. rows 1-20 are 01/01/2018, rows 21-40 are 07/01/2018, and so on.
Whilst I say they are in set rows of 20, this is an example. Some weeks there are more or less than 20 so I can't use the row count function-
Between columns c and h I have a bunch of other categories such as customer age, country etc. so there isn't a unique identifier. Do I need one for this to work? Column i is the sales column with the numbers. What I would like is a new column which gives me a ytd number for each row of data which all has unique criteria between a and h. Week 1 ytd is not going to be any different. For the next 20 rows I want it to add week1 sales to week2 sales, effectively giving me the ytd.
I could sumproduct this easily in the data set but I don't want do that. I want to use dax to save space etc..
I have a date_table which does have unique dates in the main_date column. All my date columns are formatted as date in the data model.
I have tried:
=calculate(products[sales],datesytd(date_table[main_date]))
This simply replicates the numbers in the sales column, not giving me an ytd as required. I also tried
=calculate(sum(products[sales]) ,datesytd(date_table[main_date]))
I don't know if what I am trying to do is possible. All the youtube clips don't seem to have the same issues I am having but I think they have unique dates in their data sets.
Id love to upload the data but its work stuff on a work computer so cant really. Hope I've painted the picture quite clearly.
Resolved, after googling sumif dax, mike honey had a response that i have adapted to get what i need. I needed to add the filter and earlier functions to my equarion and it ended up like this
Calculate (sum(products[sales]),
filter (sales, sales[we_date] <=earlier(sales[we_date]),
filter (sales, sales[year] =earlier(sales[year]),
filter (sales, sales[customer] =earlier(sales[customer]))
There are three other filter sections i had to add, but this now gives me the ytd i needed.
Hope this helps anyone else
I have a dashboard in Tableau which shows different payments received - the amount, the date the payment was received, and a calculated field which shows the number days since the payment was received.
However, a lot of payments are the same, with the same amount, and received on the same day; so Tableau collapses these together, and adds the total days since the payments were received together in the final column, i.e. five lots of £5.50, each received on 1st January shows as below (as of 01/02/2018)
Column 1 Column 2 Column 3
£5.50 01/01/2018 155
But I need separate rows for each. Does anyone know how to stop tableau doing this, or of a workaround?
Many thanks.
You could try using RANK_UNIQUE function.
First of all, in the Analysis Menu, uncheck Aggregate Measures.
Then, starting from this data:
You can get this result:
Additionally, you may want to hide Rank from rows just not-showing header.
Is this something close to what you're looking for?
EDIT/UPDATE
In order to get all values and not just for the top rows, just move the Rank at the very beginning of the shelf:
We are trying to calculate average stock from a movements table in a single sql sentence.
As far as we are, no problem with what we thought was a standard approach, instead of adding up the daily stock and divide by the number of days, as we don’t have daily stock, we simply add (movements*remaining days) :
select sum(quantity*(END_DATE-move_date))/(END_DATE-START_DATE)
from move_table
where move_date<=END_DATE
This is a simplified example, in real life we already take care of the initial stock at the starting date. Let’s say there are no movements prior to start_date.
Quantity sign depends on move type (sale, purchase, inventory, etc).
Of course this is done grouping by product, warehouse, ... but you get the idea.
It works as expected and the calculus is fine.
But (there is always a “but”), our customer doesn’t like accounting days when there is no stock (all stock sold out). So, he doesnt like
Sum of (daily_stock) / number_of_days (which is what we calculate using a diferent math)
Instead, he would like
Sum of (daily stock) / number_of_days_in_which_stock_is_not_zero
For sure we can do this in any programming language without much effort, but I was wondering how to do it using plain sql ... and wasn’t able to come up with a solution.
Any suggestion?
Consider creating a new table called something like Stock_EndOfDay_History that has the following columns.
stock#
date
stock_count_eod
This table would get a new row for each stock item at the start of a new day for the prior day. Rows could then be purged from this table once the applicable date value went outside the date window of interest.
To get the "number_of_days_in_which_stock_is_not_zero", use this.
SELECT COUNT(*) AS 'Not_Zero_Stock_Days' FROM Stock_EndOfDay_History
WHERE stock# = <stock#_value>
AND <date_window_clause>
Other approaches might attempt to just add a new column to the existing stock table to maintain a cumulative sum of the " number_of_days_in_which_stock_is_not_zero". But inevitably, questions will be asked as to how did the non-zero stock days count get calculated? Using this new table approach will address those questions better than the new column approach.
NOTE: I am not making changes to a database. I am creating a report.
The purpose of the report is to show pending orders that need to be assembled for shipment, but not until there is enough stock to fill the order. An order includes multiple inventory items, but the inventory on hand must be >= the ordered amount per each inventory item and in order by oldest date first before the order can be added to the report.
I've written this to where it pulls the orders, but I need it to loop through to the next order and carry over the quantity of inventory On Hand from the calculation prior to this order. When the calculation is < 0, I don't need to see the order.
EXAMPLE OUTPUT:
Order Date | Order No | Item No | Quantity Ordered | On Hand | Available Qty
2015-01-01 123456 555555 50 60 10
2015-01-02 555544 555555 10 10 00
Notice On Hand says 60 for Item No 555555 in the first row. This is the actual QOH, but the report needs to subtract the amount that was ordered in the previous line from my On Hand stock, and give me the remainder, or show the new available total under On Hand. When my On Hand amount can't fulfill an order, I don't want the order to appear on my report. My current report shows On Hand to be 60 in both rows, and instead of zero, like above, it just subtracts 10 from 60, as if it's my only order.
I don't know what approach to take to do this type of incremental change in a field, but I am assuming it involves a loop and a variable (If I need to add a variable, then it needs to begin with the actual Quantity on hand), ???? Could someone please assist me with a direction? My search to answer this has only left me more unsure of how to do this. I can provide the SQL, but it is rather complicated, so I am trying to keep this on a more general level.
"Looping" should be used as a last resort in SQL. You can do so using a CURSOR but they tend to run slower and require more work than standard SQL commands.
I would recommend trying to break this problem down into smaller tables using sub-queries / CTEs (Common Table Expressions). Can you create a query that shows the total on hand amounts for each item number? Now put that into a sub-query and start building on top of it.
I have a problem and was wondering if anyone could help or if it is even possible to have an algorithm for something like this.
I need to create a predictive ordering wizard. So based on previous sales, we will determine that that a certain amount of an item is required. E.g 31 apples. Now i need to work out the number of cases that needs to be ordered. If the cases come in say 60, 30, 15, 10 apples, the order should be a case of 30 and a case of 10 apples.
The number of items that need to be ordered change in each row of the result set. The case sizes could also change for each item. So some items may have an option of 5 different cases and some items may land up with an option of only one case.
Other examples would be i need 39 cans of coke and the cases come in only 24 per case. Therefore needing 2 cases. I need 2 shots of baileys and the bottle of baileys come in 50cl or 70cl. Therefore i need the 50cl.
The results sets columns are ItemName, ItemSize, QuantityRequired, PackSize and PackSizeMultiple.
The ItemName is the item to be ordered. ItemSize is the size the item is used in eg. can of coke. QuantityRequired how man of the item, in this case cans of coke, need to be ordered. PackSize is the size of the case. PackSizeMultiple is the number to multiply the item with to work out how many of the items are in the case.
ps. this will be a query in SQL Server 2008
Sounds like you need a UOM (Unit of Measure) table and a function to calc co-pack measure count and and unit count measure qty. with UOM type based on time between orders. You would also need to create a cron cycle and freeze table managed by week/time interval in order to create a freeze view of the current qty sold each week and the number of units since last order. Based on the 2 previous orders to your prior order you would set the current prediction based on min time between the last 2 freeze cycles containing an order and the duration of days between them. based on the average time between orders and the unit qty in each order, you can create a unit decay ratio percentage based on days and store it in each slice forward. Based on a reference to this data you will be able to create a prediction that will allow you to trigger a notice to sales or a message to the client to reorder. In addition, if you engage response data from sales based on unit count feedback from the client, you can reference an actual and tune your decay rate against your prediction. You should also consider managing and rolling up these freezes by month, so that you can view historical trending and forecast revenue based on velocity of reorder and same period last year. Basically this is similar to sales forcasting and we are switching out your opportunity percentage of close with Predicted Remaining Qty. percentage remaining.