Keytree TimeSeer

Keytree TimeSeer

Keytree's TimeSeer algorithm ingests a range of data common to supply chains, such as at the point-of-sale, and supply chain shipment data. It also evaluates external demand influencing factors, such as holiday periods or weather forecast. Finally, it also ingests upstream and downstream independently generated forecasts, for example from downstream independent customer channels. A single consolidated forecast is then generated with the following outputs: Unit quantity/revenue to a given time horizon, with accuracy typically between 85-95% depending on time horizon; and expectation, indicating if the forecast is within normal bounds, or is falling outside expected ranges. The expectation signal can be highly useful in a number of applications, for instance: Judging effectiveness of promotions; detecting fraud in the supply chain; and giving an early warning of unusual consumer behavior, for instance during the recent Covid-19 driven panic buying.

*Please note that member solutions are often customizable to meet the needs of individual enterprise end users.

CONTACT COMPANY

RESOURCES

CATEGORIES

WorldwideBrazilFranceGermanyIndiaJapanKoreaMainland ChinaMexicoOther - Asia PacificOther - Europe and AfricaOther - North and South AmericaTaiwanUnited KingdomUnited StatesAI Software/SaaSManufacturingRetailData AnalyticsHybrid CloudIntel® Xeon® Scalable ProcessorIntel® Distribution for Python*Models can be trained - data input only requiredLinux ProprietaryMachine Learning