Yeah, I’ve worked on a project where we implemented demand forecasting for a regional delivery network, and honestly the biggest surprise was how messy the real data was compared to what we expected. Orders weren’t just seasonal, they were influenced by things like local events, weather, and even random promotions from clients that never got properly logged. We ended up combining a simple statistical model with manual input from operations managers who had real intuition about upcoming spikes. That hybrid approach worked much better than trying to fully automate everything. Another key improvement was retraining the model frequently instead of relying on long-term predictions, because logistics patterns change faster than you think in real business environments.
