Last updated on JULY 06, 2015
Applies to:JD Edwards EnterpriseOne Demand Consensus - Version 8.11 Base and later
JD Edwards EnterpriseOne Demand Forecasting - Version 8.11 Base and later
Information in this document applies to any platform.
*** Checked for relevance on 06-July-2014 ***
The model has "metric tons" as the Unit of measure . At the lowest level of aggregation, . Where there is even a minimal forecast, probably due to seasonal data, the exceptions are being reported as 1,000,000,000% difference or Infinity.
Regarding the 1billion % difference, my guess is that there is data in the actual Statistical Forecast that resides in the database, but it only shows to 2 decimal places in DCON. However, the calculation is made against the data that is in the database and not what appears in DCON. Regarding Infinity, I assume that the denominator in the calculation is zero and the exception returns a value of Infinity.
Are both these assumptions correct??
If yes, is there a way to generate separate Exception reports for the SKU's that show such a high difference. In both cases, I think the severity of the exception is overstated when the actual data is so small. And, you will see in the attachment how the user becomes confused when he sees in the table for the baseline forecast 0.00 and for his forecast 0.00 and the % difference is -100. Or 0.01 vs. 0.01 and the percent difference is -24. Is it feasible to establish rules within Demand Consensus to minimize the severity of the exception when the baseline forecast has zeros in some of the time buckets or perhaps have a rule that pushes all of these to a separate exceptions report file?
Because of this issue, I attempted to view exceptions at a higher level of aggregation, leaving the lower levels null, but this gives me an error: ?the number of aggregation levels changed?. To limit the exceptions to specific levels, is the only option to create demand point sets giving access to the specific levels that I want to see exceptions for?
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