cancel
Showing results for 
Search instead for 
Did you mean: 

Like Model - Disagg Issue

former_member503200
Participant
0 Kudos

We recently changed our time based disagg from pro rata to based on APODPDANT.

We are having a problem, after chaning this logic. Any Like Model assigned product, the data in monthly bucket is getting split equally among all the weeks in that month, even though its a split week, its getting euqal quantity, instead of prorata. If i use a product, which doesn't have any Life Cycle assignment, its disaggregating correctly.

If a month got 30 days and quantity in that month is 3000

it got 4 full weeks and 1 week got only 2 days. The data i am seeing is 600 for each week, instead of 200 for split week. How to resolve this issue.

Any input is appreciated.

thx

Jeff

Accepted Solutions (0)

Answers (4)

Answers (4)

former_member503200
Participant
0 Kudos

Though the question is not answered. I am closing it.

former_member503200
Participant
0 Kudos

All our products use the same disagg logic, i believe it's not possible to setup a disagg logic by product. ( unless u use a custom exit). Thx for your input.

thx

Jeff

former_member503200
Participant
0 Kudos

Saritha,

How come its not affecting other products which doesn't have any like model assigned. We see this problem, only for products Like model is assigned.

Do u think, if i delete the existing Prop Factors and recreating them in a different KF, will resolve this issue.

thx

Jeff

Former Member
0 Kudos

I am guessing this is because of the proportional factors. Your other materials may not have the APODPANT based disaggregation.

Former Member
0 Kudos

We had the same issue here where when the week ended in the middle of the month then the porportional factors doubled up instead of being split throughout the month. After several iterations with SAP ( where they suggested a BADI), we created a new key firgure in place of APODPANT . Another macro was written that computed the average daily usage based on the Time bucket profile and the proportional factors were recalculated. This helped us offset the issue with quantities not getting evenly distributed thru the month.