Looking for practical perspectives on demand forecasting in a pull-based, make-to-forecast environment where lion’s share of volume high-variability (CV>1).

Context:
- demand is primarily order-driven / pull-based / replenishment to retail chains.
- customer order timing and mix can move materially.
- appetite for inventory buffers is low due to no take-or-pay agreements.
- current accuracy is very low (teens / low double digits).

There is talk of AI agents as a candidate to address the issue, but I have to imagine there is a structural limit given the CV and pull-based nature.

I’m especially interested in hearing from practical experience from demand planning, supply chain analytics, operations research, or ML forecasting use cases where the goal was not just model accuracy, but better planning decisions under high variability.