The Smart Forecaster
Pursuing best practices in demand planning, forecasting and inventory optimization
Most demand forecasts are partial or incomplete: They provide only one single number: the most likely value of future demand. This is called a point forecast. Usually, the point forecast estimates the average value of future demand. Interval forecasts provide an estimate of the possible future range of demand (i.e. demand has a 90% chance of being between 50 – 100 units). Probabilistic forecasts take it a step further and provide additional information. Knowing more is always better than knowing less and the probabilistic forecast provides that extra information so crucial for inventory management. This video blog by Dr. Thomas Willemain explains each type of forecast and the advantages of probabilistic forecasting.
Point forecast (green) shows what is most likely to happen. The Interval Forecast shows the range (blue) of possibilities.
Probability Forecast shows the probability of each value occurring
The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three helping you to reduce inventory costs, improve on time delivery and service levels, while running a more efficient supply chain.
We just need to feed our demand histories into our new statistical methods, and we can start planning more effectively. Not quite: it’s about the technology and the process. You are investing in a new business process to develop forecasts for driving business strategy and inventory planning decisions.
No, not that kind of regime change: Nothing here about cruise missiles and stealth bombers. And no, we’re not talking about the other kind of regime change that hits closer to home: Shuffling the C-Suite at your company. In this blog, we discuss the relevance of regime change on time series data used for demand planning and forecasting.