Two reports landed on the same desk, for the same department, in the same week. Finance had the category down against forecast. Merch had it up against their own plan. Nobody had made an error. They were reading two different systems, built on two different definitions of “the plan,” and neither team had any reason to know the other’s number existed until someone put both reports side by side.
This is the demand and inventory planning gap, and it’s close to universal among the brands we speak to - particularly the ones running a core system for finance alongside a separate planning process that finance never fully sees.
The target and the buy are built in different rooms
Finance sets a revenue target, usually top-down, informed by growth ambitions and last year’s numbers. Merch turns that target into an actual buy - which products, how many, in what sizes, from which suppliers - usually in a completely separate tool, on a completely separate timeline, with no shared translation layer between the two.
Both processes are reasonable on their own terms. The problem is that “reasonable on their own terms” doesn’t guarantee “adds up to the same number,” and by the time anyone notices the gap, stock has already been committed.
Amy, who owns Product Ops internally at CT, years as a merchandiser and a head of planning before this, puts it simply: the finance number and the merch number are almost never built from the same starting assumption, and nobody’s job is to reconcile the assumptions before the numbers get compared.
It’s a data integrity problem before it’s a reporting problem
The instinct, when finance and merch numbers disagree, is to build a better dashboard that reconciles them after the fact. That helps people see the disagreement faster. It doesn’t stop the disagreement from being built into the plan in the first place, because the actual root cause sits earlier - in whether both teams are working from the same, governed version of the underlying product and sales data, or from two versions that have quietly drifted apart.
That matters even more now that more of this analysis is AI-assisted. Feed a forecasting or reporting tool inconsistent, ungoverned data, and it won’t flag the inconsistency - it’ll just produce a confident, well-formatted answer built on top of it. What you put in is what you get out, whether the thing doing the calculating is a person with a spreadsheet or a model with a prompt.
Why more reporting doesn’t fix it
The actual fix sits earlier: a shared translation between the target finance sets and the buy merch makes, built on data both teams actually trust, checked at the point of planning rather than discovered at the point of reporting. That’s a process and data-integrity change before it’s a tooling change, even if the tooling ends up needing work too.
What this actually costs
Every quarter this goes unaddressed is a quarter where a board pack gets two numbers for the same department and someone has to explain, again, why they don’t match. That’s not a data hygiene issue. It’s a planning process that was never designed to produce one number in the first place.
Fix the translation layer and the underlying data agreement, and the reporting disagreement mostly disappears on its own, because there was only ever one gap to begin with - it just showed up twice.
If you want to talk it through, book time with the team