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Hi Jan and Eugene,
I believe Eugene is referring to the bottom of the page regarding the Weighted Average Method - https://www.ibm.com/docs/en/envizi-esg-suite?topic=methodologies-weighted-average - where it states:
"If data is not available for any of the four months, then the algorithm uses the last available month, that is, the latest month that has actual data, to generate accruals. This typically happens for accounts that are missing data for more than 12 months of a period."
From my interpretation Jan, this would meet your requirement of "Use data of last available month prior to the missing period" - Does that make sense?
It would mean swapping from"Use data from the same month of last year" to the Average Weight method to enable this functionality, which would change how accruals work for your datasets where data is consistently present, so would be something for you to discuss internally.
Thanks,
Ross
Thanks for following up on this. From reading the documentation of the Weighted Average Method, I don't think that this will be applicable in our use case as it states that "If actual data is not available in the relevant month, then both the daily average and the weight are treated as having a value of 0 for that month. The calculation of the accruals is based on actual data only and are not based on any other accruals."
What we are looking for is continued accruals from the last available data for a given calendar month. E.g. (and to stick with my original example) if the last recorded actual consumption for an account was 100 in October 2023, then the method should accrue the same 100 value into October 2024, October 2025, October 2026, October 2027, ...
Thanks fo raising the enhancement request.
To accrue indefinitely, you can consider to use the 'Weighted Average' method. It is similar to the 'Same Month Last Year' method with seasonal considerations, i.e., it uses same period of last year with a weightage of 1, togeter with the same season of this year with a weightage of 3.
See this page for more details on the Weighted Average Method:
https://www.ibm.com/docs/en/envizi-esg-suite?topic=methodologies-weighted-average