When
Christopher Richins heard what his work stream would be during his
internship with Bain & Company, he knew that taking DAO had been a
good choice.
Christopher was asked to develop a pricing strategy for a new
business based on a model that would predict refrigerated produce
truck rates from the Western U.S. to the Eastern U.S.
To begin, Christopher quickly found a source of historical weekly
refrigerated truck rates and U.S. average diesel prices for the last
4 years and got to the task of building a regression model. He was
able to quickly run a regression of truck rates with respect to
diesel prices. The results were surprising – fuel price alone
resulted in an adjusted R^2 of only 44%, much lower than he
anticipated. After analyzing the residuals, he found that there was
a cyclical pattern in the results. It was clear that in addition to
the influence of fuel prices, there was a seasonal component to
truck rates.
Considering the nature of the product being shipped (fresh produce),
it came as no surprise that there would be a seasonal component to
the price. In order to capture the seasonal component of the truck
rates, he generated a dummy variable for each of the 52 weeks of the
year, and reran the regression with diesel price in the mix.
The results of the improved regression were surprising. The R^2 of
the model increased to over 94%, and the residuals looked normal.
This had significant implications for the pricing model Christopher
was developing. From the low point in March, he found that truck
rates in June could be as much as $2000 dollars higher just because
of the seasonal nature of demand (an increase of 50%).
As a result of his regression analysis, the pricing model for this
new business includes inputs for the current diesel price, and the
week of the year to accurately model produce truck rates from the
Western U.S. to the Eastern U.S. This model will enable the client
to accurately price their substitute offering and maximize the
economic value of their service. Further, because of the excellent
data analysis skills he had acquired in DAO, Christopher was able to
complete the pricing task in 40% of the time predicted for the
project.
