FEATURIZATION
FORECASTERS
ENSEMBLER
RECONCILER
FEATURIZATION
FORECASTERS ©
ENSEMBLER ©
RECONCILER ©
ENSEMBLER
FORECASTERS
RECONCILER
THE MACRO PROBLEM
The technology that is used to make supply chain decisions is based off infrastructure that existed 30 years ago.
Most Companies are stuck in the 90's.
Increased computational capacity of supercomputers and decreased cost of memory and usage have paved the way for Predictive and Generative AI.
OUR SOLUTION
Advanced Planning Systems are limited in many ways. Noodle.ai's innovative techniques redefine what is possible in Demand Forecasting.
FEATURIZATION
FORECASTERS
ENSEMBLER
RECONCILER
OUR SOLUTION
Advanced Planning Systems are limited in many ways. Noodle.ai's innovative techniques redefine what is possible in demand planning.
FEATURIZATION ©
Noodle.ai's Featurization harnesses a variety of signals and data sets that help generate higher forecast accuracy. Using both people and technology, Noodle.ai works with customers to identify driver data sets with the greatest potential for creating lift, and automatically ingest and featurize the data sets for consumption by our library of forecasting algorithms.
FORECASTERS ©
Noodle.ai's Forecaster uses a combination of classical statistical models, Machine Learning models, and proprietary Deep Learning models. The Forecaster selects algorithms that are best suited for the various demand patterns seen across industries and products and incorporates driver data automatically. Each model is trained several times – once per forecast horizon and at multiple hierarchical levels.
ENSEMBLER ©
Noodle.ai's Ensembler finds the best combination of forecasters to use for each forecast unit, by taking into account forecaster performance, as well as portfolio and geographical context. The Ensembler also ingests other forecast streams at different hierarchical levels and learns which are most trustworthy, incorporating them into "synthetic consensus" forecast.
RECONCILER ©
Noodle.ai's Reconciler solves aggregation and disaggregation issues by taking in forecasts at all hierarchical levels and optimizing accuracy by adjusting individual forecasts based on historical error. The Reconciler uses Machine Learning to create a consensus forecast from multiple streams at different hierarchical levels.
THE MACROPROBLEM
Most companies are stuck in the 90's.
The technology that is used to make supply chain decisions is based on infrastructure that existed 30 years ago.
Increased computational capacity of supercomputers and decreased cost of memory and usage have paved the way for Predictive and Generative AI.
THE OPPORTUNITY
CERTAINTY VS UNCERTAINTY
The Fatal Flaw of every current Advanced Planning System is Deterministic Math. It forces you to chase Certainty.
THE MACROPROBLEM
Most companies are stuck in the 90's.
The technology that is used to make supply chain decisions is based off infrastructure that existed 30 years ago.
THE OPPORTUNITY
Increased computational capacity of supercomputers and decreased cost of memory and usage have paved the way
for Predictive and Generative AI.
CERTAINTY VS UNCERTAINTY
The fatal flaw of every current Advanced Planning System is Deterministic Math.
It forces you to chase Certainty.
The development of AI has unleashed the Power of Probabilities.
It enables you to embrace Uncertainty.
CERTIANTY VS UNCERTAINTY
The Fatal Flaw of every Current Advanced Planning System is Deterministic Math. It forces you to chase Certainty.
The development of AI has unleashed the #powerofprobabilities. It enables you to embrace Uncertainty.
NEW MATH
FEATURIZATION
Incorporation of data and signals, in addition to historical orders and shipment
ENSEMBLER
Blended forecast improving every prediction with every model in the library
FORECASTERS
Library of cutting-edge AI, ML, Probabilistic, and Deep Learning Models
RECONCILER
Identification of accuracy and bias reduction across product, geography, and time hierarchies
The development of AI has unleashed the Power of Probabilities. It enables you to embrace Uncertainty.
CERTAINTY VS UNCERTAINTY
The fatal flaw of every current Advanced Planning System is Deterministic Math.
It forces you to chase Certainty.
OLD MATH
OUR SOLUTION
OLD MATH
NEW MATH
Advanced Planning Systems are limited in many ways. Noodle.ai's innovative techniques redefine what is possible in Demand Forecasting.
LIMITED DATA
Forecast based on historical orders and shipments alone
FEATURIZATION
Incorporation of data and signals, in addition to historical orders and shipments
LIMITED CHOICES
Forecast using a single algorithm, often selected through best-fit
ENSEMBLER
Blended forecast improving every prediction with every model in the library
LIMITED MODELS
Forecast using only Basic Auto-Regressive Models
FORECASTERS
Library of cutting-edge AI, ML, Probabilistic, and Deep Learning Models
LIMITED RULES
Forecast limited to simple aggregation and disaggregation
RECONCILER
Identification of accuracy and bias reduction across product, geography, and time hierarchies
Make Profit Not Waste
LIMITED DATA
Forecast based on historical orders and shipments alone
LIMITED CHOICES
Forecast using a single algorithm, often selected through best-fit
LIMITED MODELS
Forecast using only Basic Auto-Regressive Models
LIMITED RULES
Forecast limited to simple aggregation and disaggregation
Advanced Planning Systems are limited in many ways. Noodle.ai's innovative techniques redefine what is possible in Demand Forecasting.
LIMITED DATA
Forecast based on historical orders and shipments alone
LIMITED MODELS
Forecast using only Basic Auto-Regressive Models
LIMITED RULES
Forecast limited to simple aggregation and disaggregation
LIMITED CHOICES
Forecast using a single algorithm, often selected through best-fit
FEATURIZATION
Incorporation of data and signals, in addition to historical orders and shipments
FEATURIZATION
Library of cutting-edge AI, ML, Probabilistic, and Deep Learning Models
ENSEMBLER
Blended forecast improving every prediction with every model in the library
RECONCILER
Identification of accuracy and bias reduction across product, geography, and time hierarchies