Valid Statistical Analysis at John Deere and Use of the R Programming Language

Derek Hoffman

Nov-8-2012

A bit about your speaker…

• BS in Statistics andMaterial Science@ Winona State

University• Masters in Statistics

@ Iowa StateUniversity

• 5 Years @ John Deere

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

• 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.

Deere & Company – 3 parts

• Agriculture ~70%

• Turf~15%

• Construction~15%

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.

My group’s reach at John Deere

CEO, Presidents, Financials

Factory Shifts and

Production

Flexibility of Inventory

Next Month

New Markets, 10 Years Out

Forecasts

My group’s reach at John Deere

CEO, Presidents, Financials

Factory Shifts and

Production

Flexibility of Inventory

Next Month

New Markets, 10 Years Out

Forecasts

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)

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

Case Studies at John Deere

• Short Term Demand Forecasting• Crop Forecasting• Long Term Demand Forecasting• Parts Decision Tree (APO)• Order Line Up• Data Coordinator

Short Term Demand Forecasting

Composite Forecast

Estimate Group

Forecast

Factory Forecast

Marketing Forecast

Potential Good:•Multiple view points•Buy-in from all players•Disciplined in forecast creation

Potential Bad:•Group-think•Pressures other than accuracy•Poor information digestion

Bad Forecasting Philosophies

News, Experience

Experience + Feelings on that Day + Outside

pressures

“Forecasts” and directives and

goals

Executive OverrideNews,

Experience, Last YR’s #’s

Math Comparisons, Finical Forecasting,

Experience, Outside forecasts

Forecasts

Gut Feel / Art

History

?

Forecasts (NO estimates of

accuracy, NO interpretation)

Blackbox Forecasts

Forecasting Philosophies

Historical Data(known because is in the

past or current)

Data + Math/Statistics

as calculated by a trained statistician

Forecasts andMEANINGFUL

plus/minus intervals

(flexibility and bad forecast detection)

Statistical ModelsAssumptions

(user generated assumptions about the

future)

Data + Math/Statistics

as calculated by a trained statistician

Forecasts and Analysis of

Forecast Error Contributions by

Assumptions

Assumption ModelsData, Assumptions,

News, ???, Outside Forecasts

Data + Economics + ???

as created by a trained economist

Forecasts, Outside

Forecasts, Current Economic

News

Economic Models

Use of Data-Driven Analysis

Analysis done in my group using R and company data.

Case Studies at John Deere

• Short Term Demand Forecasting• Crop Forecasting• Long Term Demand Forecasting• Parts Decision Tree (APO)• Order Line Up• Data Coordinator

Crop Yields Forecasting

Relative Land Area and Use

Circle = Total Land

Acres in Major World Crops

Circle = Total Crop Land

Crop Yields Forecasting

Crop Yields Forecasting

History

1 Year OUT

2nd 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.

Changes in Crop Splits

Corn Yields

Case Studies at John Deere

• Short Term Demand Forecasting• Crop Forecasting• Long Term Demand Forecasting• Parts Decision Tree (APO)• Order Line Up• Data Coordinator

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).

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.)

Example of Calculations

The whole time, calculating the valid forecast error and influences.

A large computational task, heavily using programs written in R.

Case Studies at John Deere

• Short Term Demand Forecasting• Crop Forecasting• Long Term Demand Forecasting• Parts Decision Tree (APO)• Order Line Up• Data Coordinator

Parts Forecasting

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

Parts Forecasting – Trilingual?

Case Studies at John Deere

• Short Term Demand Forecasting• Crop Forecasting• Long Term Demand Forecasting• Parts Decision Tree (APO)• Order Line Up• Data Coordinator

Order Scheduling

Order Scheduling

Restraint on Feature A: At most 2 per 4 in a row.

We’re OK!

Order Scheduling

Restraint on Feature A: At most 2 per 4 in a row.

We’re OK!

Order Scheduling

Restraint on Feature B: At most 1 per 3 in a row.

We’re OK!

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!

Harvester Lineup – Random Guess

Harvester Lineup – Program Results

Order Scheduling – Time

Order Scheduling = $$$

• Old Process– Done manually by

hand– Weekly– Duration: 8 Hours– Not necessarily perfect

• Derek’s Process– Automates the process– Duration: 1.5-2 hours– Human time:15 mins

– Saves about 8 hours per week

– Saves ~$12K per year, per product implementation

Case Studies at John Deere

• Short Term Demand Forecasting• Crop Forecasting• Long Term Demand Forecasting• Parts Decision Tree (APO)• Order Line Up• Data Coordinator

Data Coordinator Uses

DB2

DB2

SQL

Oracle

Multiples Data

sources and Data types

Multiple ODBC

Connections

Single R source Code

DB2

Export Channels

Scheduled Tasks

Batch File execution

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

The cool word of the year – Dot-com

The cool word of the year - Radiation

The cool word of the year – Big Data

How can we grow responsibly as data scientists and statisticians?

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

Cautionary tale:

• Thousands spent on a weather “forecast”

• Ridiculous accuracy measures

• Business users don’t know the short falls till it’s too late

• 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.

Growing Need of Forecasting Professionals

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