How does AX calculate the Cost Amount Physical on inventory transactions (Inventrans table)?
When posting packing slip in purchase order, transaction records are created in the Inventrans table, this table has a field (CostAmountPhysical)
How does AX calculate the value of this field?
Sometimes this value is different to the net amount calculated in each item of the purchase order?
The CostAmountPhysical field is calculated in the InventMovement.updateLedgerPhysical method.
There is no easy explanation of how and why it is calculated. Go and have look in the code.
Please update you cross reference, this simplifies questions like this one enormously.
Related
Im learning dimensional modeling and Im trying to create a model. I was thinking about a social media platform which rates hotels. The platform has following data:
hotel information: name and address
a user can rate hotels (1-5 points)
a user can write comments
platform stores the date of the comments
hotel can answer via comment and it stores the date of it
the platform stores the total number of each rating level (i.e.: all rates with 1 point, all rates with 2 point etc.)
platform stores information of the user: sex, name, total number of votes he/she made and address
First, I tried to define which information belongs to a dimension or fact table
(here I also checked which one is additive/semi additive/non-additive)
I realized my example is kind of difficult, because it’s hard to decide if it belongs to a fact table or dimension.
I would like to hear some advice. Would someone agree with my model?
This is how I would model it:
Hotel information -> hotel dimension
User rating -> additive fact – because I can aggregate them with all dimensions
User comment -> semi additive? – because I can aggregate them with the date dimension (I don’t know if my argument is correct, but I know I would have new comments every day, which is for me a reason to store it in a fact table
Answer as comment -> same handling like with the user comments
Date of comment-> dimension
Total Number of all votes (1/2/3/4/5) -> semi-additive facts – makes no sense to aggregate them, since its already total but I would get the average
User information sex and name, address -> user-dimension
User Information: total number of votes -> could be dimension or fact. It depends how often it changes. If it changes often, I store it in a fact. If its not that often, then dimension
I still have question, hope someone can help me:
My Question: should I create two date dimensions, or can I store both information in one date dimension?
2nd Question: each user and hotel just have one address. Are there arguments, to separate the address dimension in a own hierarchy? Can I create a 1:1 relationship to a user dimension and address dimension?
For your model, it looks well considered, but here are some thoughts:
User comment (and answers to comments): they are an event to be captured (with new ones each day, as you mention) so are factual, with dimensionality of the commenter, type of comment, date, and the measure is at least a 'count' which is additive. But you don't want to store big text in a fact so you would put that in a dimension by itself which is 1:1 with the fact, for situations where you need to query on the comment itself.
Total Number of all votes (1/2/3/4/5) are, as you say, already aggregates, mostly for performance. Totals should be easy from the raw data itself so maybe not worthwhile to store them at all. You might also consider updating the hotel dimension with columns (hotel A has 5 '1' votes and 4 '2' votes) that you'd update as you go on, for easy filtering and categorisation.
User Information: total number of votes: it is factual information about a user (dimension) and it depends on whether you always just want to 'find it out' about a person or whether you are likely to use it to filter other information (i.e. show me all reviews for users who have made 10-20 votes). In that case you might store the total in the user dimension (and/or a banding, like 'number of reviews range' with 10-20, 20-30). You can update dimensions often if you need to, but you're right, it could still just live as a fact only.
As for date dimensions, if the 'grain' is 'day' then you only need one dimension, that you refer to from multiple facts.
As for addresses, you're right that there are arguments on both sides! Many people separate addresses into their own dimension, referred to from the other dimensions that use them. Kimball suggests you can do that behind the scenes if necessary, but prefers for each dimension to have its own set of address columns(but modelled as consistently as possible).
I have transactional data for a sales team showing the transaction amount per transaction, the sales person for that transaction, his team and his salary. Every row denotes a unique transaction (please refer image). I need to make a team-level graph which shows the correlation between the salary they are paid and the revenue they generate for the company i.e. a simple stacked bar chart with salesTeam name on X axis and amounts on the y axis with every bar representing the total salary and total revenue(Amount) for a team.
In the example I've highlighted team 'Central', for which the salary paid is 25k (10k for salesperson A + 15k for salesperson B) and the revenue they make for the company is 430k. Please note that the salaries for some salespersons may be missing (eg. for E). The issue I'm facing is that sum(Salary) adds up the salaries for every row, so for salesperson A it becomes 20k instead of 10k. I tried avg(Salary) but that doesn't work as Tableau calculates the average for the entire column instead of average per salesperson. How can I solve this issue?
Thanks
Here you have a level of detail problem. Basically, Tableau will calculate a formula at the level of detail of the visualisation, so if salesperson is not in your view it will roll up the equation to calculate at the highest level. This is great when you want a dynamic calculation but that doesn't sound like what you are trying to achieve.
Your best option would be to aggregate the data into Tableau so you only have 1 line per sales person with a total of their revenue for all transactions. This would avoid the complexity of the calculated field (and make Tableau perform better).
However, if this is not possible the good news is the answer is a Level of Detail expression (i recommend doing some reading on this if you havent come across before). Basically, you tell Tableau at what level you want the calculation at.
If I understand you want to calculate the ratio of transaction amount and salary paid for each team.
So create a calculated field as follows:
{ FIXED [Team]
: sum(([Amount]))/
(sum({ FIXED [Sales person]:
AVG([Salary])}))
}
What this does is calculates for each team the ratio between the amount and the salary. The use of the second fixed equation that is nested within it (Salesperson) ensures that the salary is not summed for the number of transactions of a salesperson.
Using this I got a result of 17.2 for Central. Is this what you would expect? Do you need a way to account for salaries that are not known?
I am struggling to come up with a formula that fits certain criteria and was hoping someone with a better math brain than me might be able to help. What I have is a Google Sheets based tool that determines how much a someone has purchased of a product and then calculates the amount of times a special additional offer will be redeemed based on the amount spent.
As an example, the offer has three tiers to it. Though the actual costs will be variable for different offers let's say the first tier is gained with a $10 purchase, the second with a $20 purchase and the third with a $35 purchase (the only real relationship between the prices is that they get higher for each tier but there is no specific pattern to the costing of different offers). So if the customer bought $35 worth of goods they would get three free gifts, if they bought $45 worth they would get 4 and then an additional spend of $5 (totaling $50) would then allow them to redeem 5 gifts in total. It can be considered like filling a bucket, each time you hit the red line you get a new gift, when the bucket is full it's emptied and the process begins again.
If each tier of the offer was the same cost (e.g. $5, $10 and $15) this would be a simple case of division by the total purchase amount but as there is no specific relationship between the cost of the tiers (they are based on the value of the contents) I am having trouble coming up with a simple 'bucket filling' formula or calculation method that will work for any price ranges given to it. My current solution involved taking the modulus, subtracting offer amounts from the purchase amount etc. but provides plenty of cases where it breaks . If anyone could give me a start or provide some information that might help in my quest I would be highly appreciative and let me know if my explanation is unclear.! Thanks in advance and all the best
EDIT:
The user has three tiers and then the offer wraps around to the start after the initial three are unlocked once, looping until the offer has been maxed out. Avoiding a long sheet with a dynamic column of prices would be preferable and a small, multicell formula would be ideal
What you need is a lookup table. Create a table with the tier value in the left column, and the corresponding number of gifts for that tier value in the right column. Then you can use Vlookup to match the amount spent to correct tier.
I am not quite sure about, everything into one entire formula(is there a formula for loop and building arrays?)
from my understanding the tier amounts are viable, so every time you add a new tier with a new price limit then it must be calculated with a new limit price number...wouldn't it be much easier to write such module in javascript than in a google sheet? :o
anyways here is my workaround, that may could help you to find an idea
Example Doc
https://docs.google.com/spreadsheets/d/1z6mwkxqc2NyLJsH16NFWyL01y0jGcKrNNtuYcJS5dNw/edit#gid=0
my approach :
- enter purchases value
-> filter all items based by smaller than or equal "<=" (save all item somewhere as placeholder)
-> then decrease the purchases value by amount of existing number(max value) based on filtered items
-> save the new purchases value somewhere and begin from filtering again and decreasing the purchases value
(this needs to be done as many times again, till the purchases is empty)
after that, sums up all placeholder
I have a stock dimension set up on several items. I don't want to add dimensions I just want a current stock dimension that is enabled to be recognised during financial update of stock. I thought I could create a new dimension group and add the same dimensions as the previous one except I have checked serial numbers to be part of financial stock update. I just want to know if this is ok?
You can set up a new tracking dimension group where the flag "Financial inventory" is active for dimension "Serial number", yes. Since the gui allows you to do that, this is ok from a technical point of view (if you are asking if this makes sense as a business requirement, this would be the wrong place to ask that).
You may run into problems if you want to change the tracking dimension group on an existing product. I think you can only do this as long as the product has not been used in any transactions yet.
I am having
dimension tables
item (item_id,name,category)
Store(store_id,location,region,city)
Date(date_id,day,month,quarter)
customer(customer_id,name,address,member_card)
fact tables
Sales(item_id,store_id,date_id,customer_id,unit_sold,cost)
My question is if I want to find average sales of a location for a month Should I add average_sales column in fact table and if i want to find sales done using the membership card should I add corresponding field in fact table?
My understanding so far is only countable measures should be in fact table so I guess membership_card should not come in fact table.
Please let me know if I am wrong.
No, you should not add an average sales column to your fact table, it is a calculated value, and is not at the same "grain" as the fact table.
Your sales fact table should be as granular as possible, so it should really be sales_order_line_items, one row per sales order line item.
You want to calculate the average sales of a given store for a given month...?
First, by "sales" do you mean "revenue" (total dollars in) or "quantity sold"?
Average daily revenue?
Average monthly revenue, by month?
If you have the store id, date, quantity sold (per line item) and unit price, then it's pretty easy to figure out.
You Should not add aggregate columns In the same fact table. The measures in the fact table should be at the same grain. So if you want aggregate metrics, build a separate fact table at the required grain.
So, I might have a fact aggregate table named F_LOC_MON_AGG which has the measures aggregated at location and month level.
If you do not have aggregate tables, modern business intelligence tools such as OBIEE can do the aggregation at run time.
Vijay