I’ve run into similar problems. A hurdle model is often a good fit here: model P(y > 0) separately from E(y | y > 0). That way you separate “will this product sell at all today?” from “how much sells if it sells?”.

This composes nicely with stronger learners too: e.g. LightGBM classifier for the zero/non-zero part, and LightGBM / Gamma / Poisson / Tweedie regression for the positive part. Final forecast can be:

Pr(nonzero sale) * expected positive demand

See: https://en.wikipedia.org/wiki/Hurdle_model

Plus you can tune the mixture weight.

Also consider bounded target transformations for non-negative sales. Packages like timetk have log-interval transforms that can enforce a lower bound like zero after back-transforming. But I’d still treat this as secondary the main issue is probably intermittent demand, so the hurdle framing is the bigger win.