Advanced Demand Forecasting Part 2: A Technical Look
As I mentioned in Part 1, Advanced Demand Forecasting is not a one-size-fits-all solution. It is uniquely tailored for each business and requires about 6+ months to deploy. But how does it work?
Advanced Demand Forecasting leverages two techniques: Auto-Regressive Integrated Moving Average (ARIMA) or Error Trend Seasonality (ETS) analysis to produce the forecasts and residual regression analysis to determine what factors are driving demand beyond seasonal changes. ARIMA is used for making forecasts where adaptability to change is crucial and data exits at constant intervals. ETS is used when data follows unique seasonal patterns and do not have even intervals of observation.
Once the forecasting model is complete, regression analysis is performed on the model’s residuals using customer, product, and sales data to determine what factors move demand independent of yearly seasonality. This sophisticated approach has proved to show how an inch of snow above normal or dollar in price adjustment affects the demand for your business.
The techniques mentioned above all use the latest open-source Python packages from Statsmodels, Prophet, and sci-kit-learn. We also have the ability to run the modeling using Spark ML packages which allow us to run our modeling on massive datasets using the latest parallelized computing techniques with resources like Azure Databricks.
The data scientists at Neal Analytics use these techniques to look at factors that affect each individual business and develop forecasts to discover which ones have the greatest impact. By looking at how demand has changed in the past and using other company or market data from previous periods, Advanced Demand Forecasting is able to provide businesses much more accurate – and more importantly, dynamic – models.