
It is common for a frozen food operation to be simultaneously out of stock on some SKUs and sitting on excess cold storage inventory of others, and the root cause is almost always a forecast that is too coarse for how the business actually sells. A single aggregate forecast, or one built primarily from a spreadsheet and manual adjustment, tends to average out real differences between SKUs, channels and locations, overcorrecting for the loudest recent signal (a stockout, a big order, a promotion) while missing quieter but persistent shifts elsewhere in the range.
Because cold storage capacity is expensive and limited, that averaging error shows up immediately as both a shortage somewhere and an excess somewhere else, rather than being absorbed the way it might be in a cheaper, less constrained storage environment. Fixing it generally requires forecasting at a finer grain, by SKU, channel and sometimes location, and connecting that forecast directly to replenishment and storage decisions so that the plan reflects where demand is actually moving rather than a single blended average.