A coherent forecast is the optimum starting point to make well-informed business decisions.
By reconciling your forecasts using hierarchical forecasting, you get a single source of truth. One that delivers highly accurate results your organization can base critical decisions on.
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We've built an econometric model (VAR) to identify leading indicators. Vector autoregression is a workhorse model in macroeconomics that defines each indicator as a function of other indicators. This way, instead of treating each indicator’s impact separately, the model captures interactions between them and their influence on your sales.
By using a lasso penalty through cross-validation, we ensure that only the relevant indicators are represented, delivering the most accurate results.
“It's been in our pipeline!
To be able to build multi-level, separate models
and aggregate them is a great way to help us achieve coherent results.”
Misaligned decision making due to incoherent forecast results
Pitfalls with bottom-up forecasting
Notoriously suboptimal at less disaggregated levels with higher volatility, thus making it harder to make an accurate prediction.
Pitfalls with top-down forecasting
Inflexibility to take into account the impact of market trend shifts on region sales, especially when the regional predictions are calculated and set using a fixed percentage of the total.
Aggregating product-level
forecasts increases the confidence interval, leading to increased uncertainty
The aggregated product-level forecasts do not add up to the total forecasted sales, and when aggregated, the forecast error is also compounded.
In an ideal situation, the predicted values at disaggregated values should add up to the aggregated forecast.
What-if series
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If you have forecasts taking you in different directions, it’s a breeding ground for organizational politics and a suboptimal basis to make well-informed decisions.
With a coherent forecast, different functionswill have the same view about the future and be better informed. This will create a stronger foundation fore aligned decisions, with all the organiational and financial benefits.
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By using optimal forecast reconciliation, forecasting every possible level in the hierarchy is possible.
These forecasts are then adjusted using a method which weights them based on their historical accuracy, a step called reconciliation.
This means that an accurate worldwide forecast will cause less precise regional forecasts to be adjusted more than the other way around. This reconciliation benefits forecasts at every level of the hierarchy in terms of accuracy.
This substantially improves the forecast accuracy over the base forecasts as it respects the hierarchy.
Your reconciled forecast is only as good as the results of your individual forecasts. Having independent forecasts is an advantage - at each node or level, forecasts can be produced separately, based on different information.
With the ability to update the individual or sub-category forecasts, you will be able to identify and select the most relevant leading indicators and apply multivariate models.
When these forecasts are reconciled, this will improve the overall forecast accuracy of the final adjusted forecast.
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Article
One step closer to a unified view of demand with hierarchical forecasting
Imagine having a single source of (data) truth with which you can make better decisions.
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We are an automated forecasting software that bridges the latest statistical models from academia with the specific leading indicators in an organization's market environment. Helping your organization achieve the highest forecast accuracy.
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