Forecasting research project ideas

There are some things every company should know about the nature of its business. Yet many organizations don't know these fundamentals -- either because they are short on resources, or their resources don't have the analytical skills to do the work.

The summer research projects offered by the Lancaster Centre for Forecasting, offer a cost-effective way to get yourself some answers.

Project Ideas

If you haven't done these things already, here are a few of my personal favorite projects to get started:

Compare your last year of forecasting performance to a naïve model.

This is the start of any forecasting improvement endeavor -- find out how you are doing today. Don't compare your performance to industry benchmarks, those are irrelevant. Find out whether your process performs at least as well as a simple method, such as a random walk or moving average forecast. (And don't be surprised to learn you are forecasting worse!)

Evaluate the volatility of demand for your products or services.

The Coefficient of Variation is a crude and imperfect, yet still useful indicator of the "forecastability" of your demand patterns. Low CV implies that you should be able to forecast fairly accurately with simple methods. High CV implies that you probably can't expect to forecast as accurately -- although some high CV patterns (e.g. something with lots of seasonality but stable, repeating patterns) can be forecast well.

Create the "comet chart" relating volatility to forecast accuracy.

Get a visual summary of your forecasting challenges by seeing how volatility and forecast accuracy are related. Use this as motivation to find ways to reduce the volatility of demand patterns.

Perform FVA analysis.

Map out your forecasting process, and review the last year of forecasts at each step of the process (e.g. statistical forecast, analyst override, consensus override, executive approved forecast). If, like many companies, you aren't recording the data at each step, then start doing so. Use FVA to determine which steps and participants in the forecasting process are tending to make it better. And eliminate those process steps that are just making it worse. (For more information, view the Foresight/SAS Webinar, "FVA: A Reality Check on Forecasting Practices."

Replicate Steve Morlidge's analyses of forecast quality.

In a series of articles published in Foresight, Morlidge defined the "avoidability" of forecast error, and illustrated the value of a RAE (relative absolute error) metric for evaluating performance. Read the Foresight articles, find discussion of Morlidge's methodology in  several earlier BFD posts (such as this one), and view his recording from the Foresight/SAS Webinar series, "Avoidability of Forecast Error".

Doing these will give you a good foundation on which to do further research...