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

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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 …

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.

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  • 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