What forecasting methods / business rules would be suitable in the case where the product is not present for sale all the time, and the sales are large and rare (B2B large-scale logistics)?
I would be grateful if you could direct me to scientific articles or suitable software for this use case. I've already read the documentation for SAP APO and IBP, but I'm not sure they are cut out for this.
This problem is referred to as censored demand. Indeed, this is not the sales but the demand that is of interest to be forecast. Unfortunately, there is no such thing as historical demand, only historical sales that represent a loose approximation of the demand. When a product goes out of the assortment, due to stockout or otherwise, sales drop to zero, but demand (most likely) does not.
The old school approach to address censored demand consists of iterating through the historical sales data, and replacing the zero segments with demand forecast. Unfortunately, this method is fraught with methodological issues, such as building a forecast on top of another forecast is friable. Furthermore, in the case of products that are not sold during for long periods (not just rare stockout events), say summer, forecasting a fictitious demand over those long periods is not entirely sensical.
The most commonly used technique at Lokad to deal with censored demand is loss masking, understood as from a differentiable programming perspective. This technique is detailed at:
https://tv.lokad.com/journal/2022/2/2/structured-predictive-modeling-for-supply-chain/
Hope it helps, Joannes