Having access to an accurate forecast is very beneficial for businesses. If used correctly, it can provide better margins, increase market shares, and many other positive results. At a more tactical level, it can help reduce the costs associated with meeting the customer demand and make the supply chain more efficient.
In this blog we briefly cover some key insights for successful time series forecasting: (a) Profiling the Shape of the Curve is the first stage, and the first step is assessing if the time series is stationary. (b) The forecast method identified must capture the shape and be able to project the shape across time. (c) There are limits in historical and no amount of “fancy math” can overcome them.
Insight from Applied Statisticians for Forecasting: Is It Worth the Effort and the Mirage of Random Variation?
In this blog, we will illustrate through an example of these potential pitfalls (unanchored, random variation, and narrow metrics) and potential negative impact on a firm.
Here are some guidelines on selecting the right statistical forecasting methods for your business.