The credibility problem
A business forecasts total revenue for the board, regional volumes for operations and branch-level numbers for the managers. Each is produced by whoever owns that view, and none of them add up. The first hour of every planning meeting then goes on arguing about which is right.
This is a structural problem rather than an accuracy one. Independent forecasts at different levels of the same hierarchy have no mathematical reason to agree, and they will not.
Bottom-up, top-down, or both
There are three broad approaches, and the right one depends on where your data is strongest rather than on organisational preference.
| Approach | How it works | Suits |
|---|---|---|
| Bottom-up | Forecast each branch, sum upward | Branches with distinct, stable patterns |
| Top-down | Forecast the total, split by share | Noisy branch data, stable shares |
| Middle-out | Forecast at region, split down and sum up | Regional signal stronger than either end |
| Reconciled | Forecast every level, then adjust to fit | Where all levels carry real information |
Bottom-up appeals to managers because it respects local knowledge, but noise at branch level accumulates. Aggregate series are almost always smoother and easier to forecast, which is why top-down often wins on accuracy at the total even when it feels less rigorous.
What reconciliation actually does
Reconciliation takes forecasts made at every level and adjusts them so the hierarchy adds up, weighting each level by how reliable it has proven. In effect the levels lend each other information - a noisy branch borrows strength from its region.
The practical benefit is not only arithmetic consistency. Because each level contributes, the reconciled numbers are often more accurate than any single approach, particularly for the smaller, noisier units at the bottom.
Choosing the level to plan at
Before reaching for reconciliation, ask whether every level needs a forecast at all. Plenty of hierarchies have one level where decisions are actually made, and the rest are reporting views.
- If stock is held centrally and shipped on demand, the branch forecast may not drive any decision.
- If each branch orders independently, branch-level accuracy matters directly and deserves the effort.
- If staffing is rostered regionally, region is the level to optimise and branch numbers are informational.
Forecasting only where a decision depends on it is cheaper and produces less to argue about.
Presenting it so people trust it
Once the numbers reconcile, show the same set everywhere. The moment two versions of the same figure circulate, confidence goes and people revert to their own spreadsheets.
Include the accuracy history at each level, so a branch manager can see how reliable their own number has been. That does more for adoption than any amount of methodology explanation, and it surfaces genuine local knowledge where the model is consistently wrong. Our piece on why staff override predictions covers what to do with those overrides.
Numbers that do not add up get replaced by spreadsheets, whatever their accuracy.