Clear examples of weighted-average analysis

When we start discussing our heavy usage of weighted averages with customers, we sometimes get a questioning look, until we make the analysis more concrete for them. People are very familiar with using averages; equity analysts regularly look at numbers such as Days of Sales Outstanding that are composite numbers for an entire company. Very often, only when segment data are provided do analysts break such numbers into more discrete groupings.

In other fields, however, the use of weighted averages is more common and the benefits obvious. In this GAO report on military truck accidents, the GAO made this plain and compelling argument:

GAO’s analysis of January 1987 through June 1998 accident data showed that, while M939s made up an average of about 9 percent of the Army motor vehicle fleet during that time, about 34 percent of the fleet’s accidents resulting in fatalities of vehicle occupants involved these trucks;

The reader understands that while there may be good reasons for there to be more accidents in M939 5-ton trucks, that’s the place to start the investigation.

Similar analysis has been done over time comparing death rates in Detroit with Baghdad, with the use of per-capita data being the critical component; per-capita is a weighted average concept.

Dr. Peter Pronovost, who has been quite popular recently [Ed.: link added], made big news with his infection-reducing checklist for use in hospitals. In a recent interview, he described a system he helped build for hospitals in Michigan. In describing the ability to check infection rates across ICUs across hospitals across health care systems across the state, Dr. Pronovost shows that he understands how to use weighted averages to identify hot spots for potential trouble areas.

With a deep understanding of finance, free cash flow, and valuation equivalents, these principles can be applied in concert with proprietary algorithms to manage a business’s generation of free cash flow.