You forecast every store and also the national total, and the store forecasts do not sum to it. What do you do?
5 min readUpdated 2026-08-15
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Answer
Short answer
Forecast at whichever levels have signal, then reconcile so the numbers are coherent by construction. Bottom-up preserves detail but accumulates noise, top-down is stable but allocates by an assumed proportion, and optimal reconciliation projects independent forecasts onto the set that adds up.
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Likely follow-ups
→Half your stores have intermittent demand with many zero weeks. Which approach survives that?
→A new product has no history at all. How does it enter the hierarchy?
→Your hierarchy is store by category and also region by channel. Why is that harder?
→How would you report accuracy so that finance and the store planners both trust it?
Coherence is a requirement, not a tidiness concern
The moment the same quantity is forecast at two levels, someone downstream will
compare them. Finance builds a revenue plan from the national number, the supply
chain allocates stock from the store numbers, and if the two disagree by three per
cent then one of them is wrong and nobody knows which. The organisation resolves
this politically rather than statistically: each team uses the version that suits
it, the forecast stops being a shared input, and the model's accuracy becomes
irrelevant to the argument.
So the deliverable is not a set of accurate forecasts. It is a set of forecasts
that add up and are as accurate as possible subject to adding up. State it that
way and the rest of the answer follows, because reconciliation stops looking like
a post-processing step and becomes the design.
Where the signal lives
The reason this is not solved by simply forecasting one level is that the levels
have genuinely different statistical properties.
A single store in a single category is a thin series. Weekly units might be in the
single digits, with many zeros, and a large share of the variation is
irreducibly random — a few customers behaving individually. Seasonality is present
but noisy, and a promotional effect is hard to estimate from so few observations.
The national total for the same category is a sum over hundreds of such series,
and the independent noise largely cancels while the common structure survives. The
trend is visible, the annual seasonality is clean, and price elasticity is
estimable. It is a much easier series to forecast well and the accuracy figures
will be much better.
That asymmetry is the whole tension. The detail is where the decisions are made
and the signal is at the top.
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Read it in both directions: aggregating upwards cancels noise, and allocating
downwards has to invent the split that aggregation destroyed.
Bottom-up, top-down, middle-out
Bottom-up forecasts each leaf and sums. It is coherent by construction, it
preserves whatever local knowledge each series carries, and a store that genuinely
behaves differently is represented. The cost is that you are forecasting hundreds
of noisy series and adding their errors together, so the national number can be
materially worse than a direct model of the national series would have been. There
is partial cancellation if the errors are independent, but they rarely are — a
shared seasonality misspecification biases every leaf in the same direction and the
bias adds up rather than cancelling.
Top-down forecasts the aggregate and splits it by proportions. The top number
is as good as your best model can make it, and the leaf numbers inherit its
stability. The problem is the proportions. Historical average share is the usual
choice and it is a forecast in disguise: it assumes each store's share of the total
is stable, which is false whenever stores are opening, refitting, changing
catchment or being cannibalised by a new site down the road. Top-down also
systematically flattens genuine local behaviour, so a store with a different
seasonal shape gets the national shape scaled down, and the planner who knows
better stops trusting the system.
Middle-out picks an intermediate level where the series are still thick enough
to model well, forecasts there, then aggregates upwards and disaggregates
downwards. It is frequently the pragmatic answer, and the level to choose is the
lowest one at which the series are reliably forecastable rather than the level the
org chart suggests.
Optimal reconciliation
The modern framing removes the choice. Forecast every series at every level
independently and as well as you can — these are called base forecasts and they
will not be coherent. Then apply a linear projection that maps the full vector of
base forecasts onto the nearest set that satisfies the aggregation constraints,
weighting the adjustment by how reliable each base forecast is.
The appeal is that information flows in both directions rather than one. The clean
national seasonality informs the store forecasts, and a store with a genuinely
divergent trend pulls the total. Bottom-up and top-down are both special cases of
this projection with particular weights, which is a useful thing to be able to
say, and the general version can be shown to be no worse than either under its
assumptions. The weighting is the part that matters in practice, and it is
estimated from the in-sample errors of the base forecasts, with structural
approximations used when the full error covariance cannot be estimated reliably at
scale.
The operational costs are real. You now maintain a model for every node rather
than one level, the reconciliation step needs the hierarchy encoded as a matrix and
kept in step with a store estate that changes monthly, and explaining to a planner
why their store forecast moved when no store data changed is a genuine
change-management problem. That last one is worth raising unprompted, because it
is the reason reconciled forecasts get overridden in practice.
Which level you are judged on
The instinct is to report accuracy at the leaf level, because that is the finest
detail. It is usually the wrong emphasis, and being clear about why is what
separates a strong answer here.
Leaf-level error is dominated by irreducible randomness. A store selling four
units a week will show a large percentage error no matter how good the model is,
and improving the model barely moves that number, so it is a poor signal about
whether your work helped. Percentage-based metrics are also unusable there,
because a week with zero actual demand has no defined percentage error at all.
Meanwhile the totals drive the decisions with the largest financial consequence:
how much stock enters the country, what the revenue plan says, how much
manufacturing capacity is booked, what the cash requirement is. An error at the
top is not offset by anything, whereas an error at one store is partly absorbed by
the opposite error at the next store and by whatever safety stock the network
holds.
So report at every level and be explicit about which level each consumer is
served by. Use absolute or scaled error measures rather than percentages at the
leaves, and quote the aggregate error separately rather than letting it be implied
by an average over hundreds of series — an average leaf error can improve while
the total gets worse, and the total is what someone has committed money against.
A forecast the organisation cannot reconcile is a forecast the organisation will
not use. Decide which level each consumer reads, make the numbers coherent by
construction, and report accuracy at the aggregate separately because that is
where the money is committed.