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Very often histories – provided they are long enough – carry very well the information that one seeks to model and project into the future.<\/span> For example, the seasonality of a product (ice cream, soup, etc.) emerges from history.<\/span> The weather plays an obvious role but very often at the margin in relation to the decisions to be made.<\/span> In this case, we remain in a classic approach because we have the history.<\/span> In cases where the history does not exist (new products) or is too short, we need additional explanations, exogenous information because the analysis of the time series of sales (or demand) is not sufficient.<\/span> .<\/span> We must therefore flesh out the approach with advanced algorithms using AI.<\/span><\/p>\n Exogenous data: how AZAP integrates them?<\/strong><\/span><\/p>\n AZAP’s algorithms integrate this exogenous data in two complementary forms:<\/span><\/span> <\/span><\/p>\n Exogenous data: an example to help understand the difference between traditional techniques and the use of this type of information<\/span><\/span> <\/strong><\/p>\n In the case of an influenza vaccine: the time series of sales is completely seasonal, AZAP determines the average seasonality based on the histories of recent years and gives the range of uncertainty: strong year – weak year.<\/span> This is perfect for setting the medium term, planning the production and purchases of active ingredients.<\/span> <\/span><\/p>\n For the short term, this is not enough: when does the epidemic really start?<\/span> How big is the epidemic this year?<\/span> In this case, AZAP exploits exogenous information which gives the incidence rate every week … and makes it possible to adjust the short-term forecast.<\/span><\/span><\/p>\n It should be noted that this “exogenous” information are:<\/span><\/span><\/span><\/p>\n\n
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