Supply chain | Supply Chain Management, Commerce

Demand Forecasting Algorithm Calculations Equations Are missing

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Posted on by Islam Mohab

Dears,

Hope all is well,

i am facing an issue to find the Algorithm Equations for ARIMA , ETS , STL and its combinations.

if anyone can help for that it will be so good as the customer ask frequently about the values shown in the Adjusted demand forecast from where it comes.

thanks,

Islam

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Farouk Galal 53 on at

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hi eslam those are not equations those are parameters used during forecast generation

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Drew Lencsak 411 on at

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The values that exist are baseline starting values provided by Microsoft. They are intended to be changed to match the configuration your forecast process requires. For example. Seasonality hint of "1" would look for seasonal hints for 1 years period, but if you configure that field to be "12, 3, 1" you'd be looking for yearly, quarterly, and monthly trends. Forecasting model, you would only leave as ARIMA, if that was the statistical model you would like to use for forecast generation. Keep in mind, these are baseline values but can be overridden in the Demand forecasting parameters form as well as specified individually for each Item Allocation Key. This link provides a good overview of all the parameters and configurations and what they are responsible for: Demand forecasting setup - Supply Chain Management | Dynamics 365 | Microsoft Learn

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Dogan Adiyaman 698 User Group Leader on at

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Hi,

I don't think your customer wants to look at the forecasting algorithm and understand the logic.

For example, the general equation for an ARIMA model is:
$Y_{t} = c + ϕ_{1} Y_{t - 1} + \dots + ϕ_{p} Y_{t - p} + θ_{1} e_{t - 1} + \dots + θ_{q} e_{t - q} + e_{t}$
Where:

$Y_{t}$ is the value at time $t$

$ϕ$ are the AR parameters

$θ$ are the MA parameters

$e_{t}$ is the error term

In Dynamics 365, the ARIMA, ETS, and STL models are used by Azure Machine Learning to generate demand forecasts. The Adjusted Demand Forecast values are based on the application of these algorithms to your historical data.

For customization, you can write custom R scripts in Azure ML Studio or Azure ML Designer to modify the behavior of these algorithms.

Your client can compare the results of different models and select the one that best fits their forecasting needs.

Additionally, under Master Planning > Setup > Demand Forecasting Parameters, you can configure how these algorithms behave.

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