Ryan
first used Pivot Tables to analyze what in his words were “very
ugly” data on the sales of Coke products at Target stores and
determined at what level it was appropriate to try to forecast
sales. Next he turned to regression to identify those variables
that were important predictors, which then became inputs into the
forecasting model. To accommodate the company’s requirement to
 create a matrix of volume forecasts, Ryan conducted yet another
round of pivot table analysis. The final forecasting model or
“system” Ryan created is now being tested in parallel with the
company’s legacy system. As Ryan put it, “May the best model win.
I’m hoping it’s mine.”
Ryan’s efforts drew the following praise from his
manager:
“Ryan did an excellent job on the Volume Forecasting project. He
thoroughly researched the best practices employed by [the company]
…and tested a prototype of a working model that may prove useful in
helping us to better predict sales. Additionally, Ryan offered solid
recommendations on ways that we can improve our existing processes,
such as using a zero cost Excel add-in or training in statistical
methods.”
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