Order Fulfillment Forecasting at John Deere: How R Facilitates Creativity and Flexibility

Presented:	Thursday, November 8, 2012
Presenter:	Derek Hoffman, Manager of Forecast Analytics, John Deere
	Download the webinar presentation (pdf) and replay file (wmv).

Statistical analysis has been known to be invaluable to any manufactory’s quality assurance for decades. Recently the value of valid statistical analysis has also been demonstrated to radically improve the ability of a company’s ability to weather extreme peaks and valley in customer demand. John Deere has been able to adjust to commodity spikes and housing downturns much better than its competitors have. This is in part due to the implementation of statistical analysis and the use of R software in the order fulfillment function of John Deere.

In this seminar, Derek will present a selection of real-world example of how statistical analysis and R software is used in the order fulfillment forecasting group at John Deere. The wide range of example types demonstrate the need for creativity in practicing statisticians and the usefulness of a flexible and open source statistical software.

This session will cover:

Common good and bad demand forecasting methods found in the industry
Commodity forecasting, in particular crop measures
Discovering product relationships
Use of R as a tool for the data coordinator role
R-based analysis as an add-on to commercial forecasting software
Optimization of complex production with genetic algorithms written in R

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About the Speaker

Derek Hoffman has been involved in solving fun and challenging data analysis problems for the last decade. He has been working in the order fulfillment forecasting group at John Deere for the last 5 years. During that time he has grown the statistical group he manages from 1 (himself) to the current 10 individuals. His focus at Deere has been to increase the visibility and discipline of statistical forecasting and analysis in the company.

His experience spans demand forecasting, optimization, price forecasting, engineering problems, and R&D data analysis. His professional interests include regression, Bayesian statistics, optimization, time series, and risk analysis. Derek holds a Masters Degree in Statistics from Iowa State University.