John Deere utilizes statistical forecasting, enhanced by the R programming language, to match supply with demand effectively across its agricultural, turf, and construction divisions. The company emphasizes the importance of accurate forecasting to avoid financial losses, manage inventory, and plan workforce needs while benefiting from R's open-source features and strong user support. Despite being a leader in forecasting applications, John Deere recognizes the need for continued growth in data usage and analytics capabilities compared to industry giants like Amazon and Walmart.

1. Valid Statistical Analysis at John
Deere and Use of the R
Programming Language
Derek Hoffman
Nov-8-2012

2. A bit about your speaker…
• BS in Statistics and
Material Science
@ Winona State
University
• Masters in Statistics
@ Iowa State
University
• 5 Years @ John Deere

3. Forecasting Group in 2012
• Improvements due to the science of forecasting
• Explosion in value and statistician hiring
• Increase in problem solving flexibility due to use of R
• Huge company saving with dropping flop forecasting software

4. • Revenue of roughly 35
billion, 8.7% profit
• Has been a Fortune 500
company for the last 56
years, roughly 94th in
rank.
• Employs about 50,000
people world wide –
roughly 5,000 of them in
the Moline headquarters.

5. Deere & Company – 3 parts
• Agriculture ~70%
• Turf~15%
• Construction
~15%

6. Why does Deere hire forecasters?
• Availability needs to match demand OR you
lose market share
• Inventory needs to stay low OR you pay lots
in taxes and storage costs
• New factories need to be built at the right
size and time OR you made a multi million
dollar mistake.
• Work force needs to be hired/cut depending
on production plans OR you lose tons
training and severance.

7. My group’s reach at John Deere
CEO, Flexibility of
Presidents, Inventory
Financials Next Month
Forecasts
Factory Shifts
New Markets,
and
10 Years Out
Production

8. My group’s reach at John Deere
CEO, Flexibility of
Presidents, Inventory
Financials Next Month
Forecasts
Factory Shifts
New Markets,
and
10 Years Out
Production

9. Why do statisticians love R?
• Common statistical methods are available as
packages (advantage over C++)
• Large support group of users worldwide
• Credibility due to submission standards and
university usage.
• Often the program of choice during education
• Easy to send results to another person (even
if just text files for data and code)

10. Why does Deere love R?
• The cost is right
• Open source – no black box mysteries, no
propriety lock downs
• Easy to share across the business
• Relatively easy to learn
• Often works better or faster than microsoft
products for data and analysis
• Infinitely customizable to your problem and
your products – vertical integration

11. Case Studies at John Deere
• Short Term Demand Forecasting
• Crop Forecasting
• Long Term Demand Forecasting
• Parts Decision Tree (APO)
• Order Line Up
• Data Coordinator

12. Short Term Demand Forecasting
Marketing Potential Good:
Forecast
Factory •Multiple view points
Forecast
•Buy-in from all players
•Disciplined in forecast creation
Estimate
Group
Forecast Potential Bad:
•Group-think
•Pressures other than accuracy
•Poor information digestion
Composite Forecast

13. Bad Forecasting Philosophies
Executive Override Gut Feel / Art Blackbox Forecasts
News,
News,
Experience, Last History
Experience YR’s #’s
Experience + Math Comparisons,
Feelings on that Finical Forecasting,
Day + Outside Experience, ?
pressures Outside forecasts
Forecasts (NO
“Forecasts” and
estimates of
directives and Forecasts
accuracy, NO
goals
interpretation)

14. Forecasting Philosophies
Statistical Models Assumption Models Economic Models
Historical Data Assumptions Data, Assumptions,
(user generated News, ???,
(known because is in the
assumptions about the
past or current)
future)
Outside Forecasts
Data + Data + Data + Economics
Math/Statistics Math/Statistics + ???
as calculated by a as calculated by a as created by a
trained statistician trained statistician trained economist
Forecasts and Forecasts and Forecasts,
MEANINGFUL Analysis of Outside
plus/minus Forecast Error Forecasts,
intervals Contributions by Current Economic
(flexibility and bad
forecast detection)
Assumptions News

15. Use of Data-Driven Analysis
Analysis done in
my group using R
and company data.

16. Case Studies at John Deere
• Short Term Demand Forecasting
• Crop Forecasting
• Long Term Demand Forecasting
• Parts Decision Tree (APO)
• Order Line Up
• Data Coordinator

17. Crop Yields Forecasting

18. Relative Land Area and Use
Circle = Total Land

19. Acres in Major World Crops
Circle = Total Crop Land

20. Crop Yields Forecasting

21. Crop Yields Forecasting
History 2nd Year OUT
1 Year OUT 3rd Year OUT
The whole time, calculating the valid forecast error and influences.
A large computational task, heavily using programs written in R.

22. Changes in Crop Splits

23. Corn Yields

24. Case Studies at John Deere
• Short Term Demand Forecasting
• Crop Forecasting
• Long Term Demand Forecasting
• Parts Decision Tree (APO)
• Order Line Up
• Data Coordinator

25. The Wrong way – Growth f(t)
• The problem really is that we are looking at a
correlation with time, not a causation. Also
we will always be extrapolating (because the
future value of time is outside the our
historical data set).

26. What are Likely Causes?
• Crop Yields
• Planted Acres
• Crop Prices
• Population
• Gross Domestic Product
• Farm Size
• Government
• Mechanization Level of Farming
• Crop Choices (Corn damages combines faster than
wheat.)

27. Example of Calculations
The whole time, calculating the valid forecast error and influences.
A large computational task, heavily using programs written in R.

28. Case Studies at John Deere
• Short Term Demand Forecasting
• Crop Forecasting
• Long Term Demand Forecasting
• Parts Decision Tree (APO)
• Order Line Up
• Data Coordinator

29. Parts Forecasting
• Tons of parts, need direction
how to best forecast with
SAP.

30. Parts Forecasting – Trilingual?

31. Case Studies at John Deere
• Short Term Demand Forecasting
• Crop Forecasting
• Long Term Demand Forecasting
• Parts Decision Tree (APO)
• Order Line Up
• Data Coordinator

32. Order Scheduling

33. Order Scheduling
Restraint on
Feature A:
At most 2
per 4 in a
row.
We’re OK!

34. Order Scheduling
Restraint on
Feature A:
At most 2
per 4 in a
row.
We’re OK!

35. Order Scheduling
Restraint on
Feature B:
At most 1
per 3 in a
row.
We’re OK!

36. Order Scheduling
Restraint on
Feature A:
At most 1
per 3 in a
row.
We’re got a
problem!
Have to
move Matt
or Shawn’s
tractor to
another spot
and recheck
it all!

37. Harvester Lineup – Random Guess

38. Harvester Lineup – Program Results

39. Order Scheduling – Time

40. Order Scheduling = $$$
• Old Process • Derek’s Process
– Done manually by – Automates the process
hand – Duration: 1.5-2 hours
– Weekly – Human time:15 mins
– Duration: 8 Hours
– Not necessarily perfect – Saves about 8 hours
per week
– Saves ~$12K per year,
per product
implementation

41. Case Studies at John Deere
• Short Term Demand Forecasting
• Crop Forecasting
• Long Term Demand Forecasting
• Parts Decision Tree (APO)
• Order Line Up
• Data Coordinator

42. Data Coordinator Uses
Scheduled
Tasks
Multiples
Data Multiple
sources and ODBC DB2
Batch
Data types Connections File
execution
DB2
Single R Export
source Code Channels
SQL
DB2
Oracle

43. A forecast of “Analytics”
• A short history of “cool topics”
• The future of forecasters
• The coming data flood and analytics boom
increase in scalpels ≠ increase in surgeons

44. The cool word of the year – Dot-com

45. The cool word of the year - Radiation

46. The cool word of the year – Big Data
How can we grow responsibly as data
scientists and statisticians?

47. Signs you are in the hype
• Everyone claims it will change the world
• It’s taught in business schools
• Features on covers of general magazines
• TONS of snake-oil salesmen
• Legitimate ease in access to the new thing

48. Cautionary tale:
• Thousands spent on a
weather “forecast”
• Ridiculous accuracy
measures
• Business users don’t
know the short falls till
it’s too late

49. Growing Need of Forecasting Professionals
• A need for educated gate keepers to weed
bad analysis from good.
• More people are needed to practice
forecasting as a profession – or the whole
industry will suffer.
• More data, more ease, more computing
needed, with greater need for responsible
use.

50. Statistics and R at John Deere
• John Deere is among the best in large
manufactures in implementing good
forecasting methods to demand planning
• There are still huge areas to grow – no
where near the data usage of companies like
Amazon or Wal-Mart
• The challenge is to increase usage and
access while maintaining a good internal and
external reputation