Big data solutions provider Lokad has introduced its second forecasting breakthrough. Quantile grids do not provide one demand forecast per product, but provide the entire probability distribution for (nearly) all possible futures.
Accurate forecasts are often a fairytale, conveniently repeated within a market overrun by underwhelming vendors. “Since we cannot predict the exact future, what about trying to assign a probability to every single possible future? That is, the probability of selling zero units, one unit, two units, etc”, says Joannes Vermorel, founder of Lokad.
This is exactly what quantile grids are about: delivering not only one forecast per product, but delivering the entire probability distribution of demand for every product. Under the hood, quantile grids are a little bit like quantile forecasts, except that a demand forecast is simultaneously computed at all service levels.
Optimizing inventory or managing supply chain is all about balancing risks and opportunities: inventory levels vs service levels, purchase price vs supplier lead time, bulk purchase vs made to order, and so on.
“While quantile forecasts can pinpoint one or two troublesome scenarios, in the end, it is just one forecast value per product, and no matter how good this value can be, this one value cannot capture all the diversity of possible business outcomes.”
In contrast, quantile grids tackle the problem head-on: all outcomes are computed and associated with their respective probabilities. This method is made possible through the combination of Machine Learning, Big Data, Cloud Computing plus some commerce-driven insights.
Image: CC-BY Amfortas 2009