Choosing the correct hierarchy is the single most impactful decision in forecasting — yet it's almost always treated as an afterthought. This guide changes that.
Start Learning ↓"It's Monday morning. You run a fruit stand. How many apples do you order?"
Your fruit stand doesn't sell "fruit" — it sells specific items, in categories, rolling up to total revenue. The level you forecast at determines everything else.
Three fundamentally different strategies for navigating the hierarchy. Each makes different assumptions and suits different data realities.
Forecast every individual SKU independently, then sum them up to get category and total forecasts. You trust the data at the lowest level most.
"Strawberries have a clear early-summer spike. We can model them directly. But dragon fruit — we've only sold it 11 times. Any model we build is just noise."
Forecast at the total or category level, then disaggregate downward using allocation proportions. You trust aggregate data more than individual data.
"We're confident next week's total citrus demand is ~90 units. Allocate by historical proportions: 46% Navel, 20% Blood Orange, 34% Clementine."
Forecast at a middle tier (categories), then aggregate upward AND disaggregate downward. Balances signal strength with operational detail.
"We forecast Citrus, Berries, and Stone Fruit as categories. Aggregate upward for revenue reporting. Disaggregate downward for SKU-level ordering."
The right forecasting approach isn't fixed — it shifts as a product moves through its lifecycle. A new launch has no history. A mature product has plenty. An end-of-life SKU is sparse again. The forecast method must move with it.
Once you have a category forecast, you need a principled way to divide it into individual items. Four methods — each with a different assumption about what drives the split.
Each item gets the same share it averaged over history. Simple, stable, and easy to explain — but won't adapt to shifts in demand.
🍊 These proportions were calculated from the past 52 weeks of sales and held fixed. Reliable until the market shifts.
Each item is modeled independently first, then its share of the total is used as the weight. Reacts to current trends — heavier items pull more of the forecast.
🍓 Navels are trending up this month, so their independent model score earns them a bigger slice of the category forecast.
Proportions are recalculated on a sliding window — recent weeks matter more than distant history. Balances stability and responsiveness without overfitting to any single period.
🍑 Peach demand jumped in week 6 due to a promotion. The rolling average dampens that spike so it doesn't permanently inflate the Peach share.
Proportions are defined by the calendar. Some items are simply unavailable in certain months — their share drops to zero, and others absorb the total. This is the right method when availability, not just demand, determines the mix.
🍑 Stone fruit are simply not available in winter. In January, 100% of the Stone Fruit category forecast goes to stored/preserved items — not zero. The category total stays constant; only the mix changes.
The right approach depends on data availability, the decision context, and error tolerance at each level of the hierarchy.
Most forecasting failures aren't model failures — they're hierarchy and disaggregation decisions that were never questioned.
Everything that matters, in three ideas. The foundation of every good forecasting hierarchy decision.