One of the problems with economic forecasting is that a small change in a few variables can make predictions almost impossibly complex. The maths can quickly become complicated.
Continue reading Why experts often get predictions wrong
Amazon has developed machine-learning driven models on how to forecast demand for its services and deploy infrastructure accordingly.
Continue reading Machine learning to make capacity planning decisions
A growing number of companies says their forecast accuracy, service levels and inventory efficiency metrics have hit a ceiling that they just can’t get past.
Continue reading What’s Wrong with Demand Forecasting
Need to boost forecast accuracy? Three factors have huge impact on the predictions: training, teams and tracking. It’s time to tee up!
Continue reading Three ways to give forecasters a boost
Optimism and lateral thinking are the hallmark of a “closer” and the chief attributes of a lousy forecaster, one of the great paradoxes of sales.
Continue reading “Sales Forecasting by reps is dead”
Often the biggest obstacles to S&OP excellence stems from complexity: difficulty gathering data, too many meetings, a hard-to-govern process, or difficulty analyzing key data.
Continue reading Executive S&OP: Practical Thinking
Kira Radinsky has garnered international recognition for her work forecasting real-world events, like disease outbreaks and riots.
Continue reading Predicting events using machine learning
It’s possible to get better at forecasting. Research offers some insights into the factors that make a difference to become a “super forecaster”.
Continue reading Prediction isn’t a hopeless enterprise