on 12-05-2013 6:01 PM
Hello All,
We have implemented DP BOM functionality. If there is Consensus Forecast for Header SKU, then Dependent Demand gets calculated for components. So sum of Dependent Demand for components should match Consensus Forecast for the Header SKU. This is how DP BOM should work.
We have noticed a strange issue, There are a few SKUs where we do not see it is working - Consensus Forecast for Header SKU does not equals to Sum of Dependent Demand for components.
For such SKU I zeroed out the Consensus Forecast at Header level and noticed there is negative number which exist in Dependent Demand key figure for one particular Component. This is causing the difference. Ideally when Consensus Forecast was zeroed out, then Dependent Demand should have been zeroed out as well, but we have some negative value in it. I would like to fix this negative values from Dependent Demand key figure so that it will match Consensus Forecast.
Consensus Fcst Semantics 401
Dependent Demand Semantics 501 (That's why it is non-editable as well)
I have already run the Planning Area Consistency Check and DP Timeseries check using OM17..
Requesting your input to resolve this issue.
Thank you
Satish Waghmare
Hi Satish,
Let me take one example : Single level BOM
X1 : Independent product ( Finished product )
Y1 dependent product( sub assembly)
If 2 components of Y1 is required to manufacture 1 final product ie X1 then i dont expect the dependent demand will be equal to independent demand.
If demand is 100 for X1 then it should be 200 for Y1 . Please check the BOM design.
Let me know your thoughts on this.
Regards
Uday
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Hi Satish,
With the given situation that the headear sku has zero consensus forecast value, we will certainly expect that the comonent value to become zero.
However, in your system, it looks there are still negative values at the component level.
Do you expect to have negative values at component level ?
If not, then for the specific KF you may think of setting "Negative values not allowed " .
Of couse this is not the solution for the original problem, you have narrated.
In APO DP, in disaggregation, there may be small anamolies ( as you may be disaggregating based on say proportional factors at header level). We have observed mumbers mismatches for small values of forecasts.
regards
Datta
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