Australian Wool Pricing
Australian wool is a big deal—or more precisely, it’s a big deal made up of lots of little deals. With an annual value of AUD 3 billion, Australia’s wool makes up 70 percent of the world’s raw wool used in clothing. It is still marketed in lots by the traditional mode, and each year more than 450,000 farm lots are sold at open cry auction. It’s a very risky marketplace for both the farmers and the buyers who contract in advance to deliver wool to processors.
Different Wools, Different Prices
It was an ideal challenge for NeuralTools. Because of detailed market recording, the number of records was large but there were also missing data; prices were dynamic; and the relationship of price to wool characteristics was nonlinear and interactive, as well as being dynamic. Kimbal began his model by training NeuralTools on a set of nearly 6,000 records from a six-month period. For this purpose he established independent category variables for such factors as place of sale, date, and qualitative aspects of the wool that affect price. He set as independent numerical variables the measurable physical characteristics of the wool. He then used the NeuralTools feature Best Net Search to determine that the best computational mode was Generalized Regression Neural Networks.
Finding Critical Factors and Predicting with Accuracy
“NeuralTools ably dealt with the complexities of the problem,” Kimbal reported, “freeing me to concentrate on the relationships it found and to compare these with our experience of the wool market.” And he had high praise for the software’s user-friendliness. “The thing I really value in an analytical package is the ability to use it to solve real problems without the process itself becoming a problem. Once I understood the analytical options and chose the appropriate set for my purpose, NeuralTools delivered.”