As a follow-up to my previous article on smarter intermittent demand forecasting, I will now provide an algorithmic modeling approach for setting suitable safety stock levels without normality (Gaussian) assumptions. […]

Before making any modeling assumptions, we should first consider an application and examine the underlying historical data. The spreadsheet table shows the demand series for a SKU and location in […]

During the CPDF® professional development workshops on Smarter Forecasting and Planning for supply chain practitioners, I often have a discussion with the participants about measuring forecast error and forecast accuracy. I […]

If we examine demand forecasts, like the chart above, without having domain expertise or knowledge about the context of the forecasting problem, we still can explore the data patterns for […]

When was the last time you saw moving average forecasts being used for forecasting sales, inventory, production or Supply Chain training and certification programs? Hopefully, not within the past two decades, […]

One objective of a smarter forecasting process is to identify and evaluate systematically all factors, which are most likely to affect the course of demand for products and services. What […]