The Art and Science of Forecasting

The Art & Science of Good Forecasting:
Combining Math & Analysis with Good Instincts and Sound Judgment

Michael Ryan, The Goodyear Tire & Rubber Company
Business Forecasting: Predictive Intelligence Summit, San Francisco, CA
March 6, 2013
Business Forecasting Summit | March 6, 2013
Copyright © 2013 The Goodyear Tire & Rubber Company Copyright © 2013 The Goodyear Tire & Rubber Company

Company

2012 Sales = $20.992 Billion1 Segmentation2
(% of 2012 Units of 164 million)

Replacement,
70%

Commercial,
20%
Consumer, 54%

Other, 13%

OE, 30%

Retail, 7%
Chemical, 6%

1 http://www.goodyear.com/investor/pdf/slides/4qtr12_slides.pdf
2 http://www.goodyear.com/investor/pdf/10k/2012_10K_w-exhibits.pdf
Business Forecasting Summit | March 6, 2013 2
Copyright © 2013 The Goodyear Tire & Rubber Company

Art & Science of Forecasting

History
Undersold In Range Oversold
Forecast vs. Statistical
Manage Organizational Knowledge Forecast of Forecast Forecast
Actual Forecast

Ext er nal Int er nal (Increases)

Forecast External 18 Month Forward Horizon
Execution Input
Consensus Agreed
Sales Plan Sales Plan Profit
Feedback
Loop Internal
Input (Decreases)
Scenario
Planning Discussion &
Consensus

Science Art Desired Outcome

What is the outcome you would like to achieve?

"Forecasting Isn't Rocket Science"

In Rocket Science, parameters are well understood

Laws of Physics:
• Immutable. Constant.

Within Control:
• Vehicle Mass
• Engine Thrust
• Launch Timing

Model Effects of:
• Wind
• Temperature
• Interaction of Systems

Processes in place to accurately model impact of variables
Photo: http://history.nasa.gov/ap11ann/kippsphotos/39961.jpg

More Variables Than Rocket Science

In Forecasting, variables can be both an art and a science

Law of Supply & Demand:
• Challenging to Fine Tune

Within Control:
• Price
• Supply

Model Effects of:
• Demand
• Supply
• Quantity Sold

In some regards, Rocket Science is easier than forecasting
Illustration: http://en.wikipedia.org/wiki/Supply_and_demand

Value of Forecast Accuracy

Profit generally improves as forecast accuracy increases

Undersold: Undersold In Range Oversold
• Cash Forecast of Forecast Forecast
• Inventory
• Capacity (Increases)
• Utilization

Oversold: Profit
• Lost Sales
• Backorders
• Overtime (Decreases)

• Expediting

Opportunity to translate the value of forecasting to your business

“Don't be afraid to give up the good to go
for the great.”

-John D. Rockefeller


What is a Process?

Beginning  Middle  End
Input  Transformation  Output
Start  And Then Some Magic Happens  Finish

Definable: Can draw a picture of it…

Predictable: Know what result will be…
+ = + =

Repeatable: Outcome is assured & repeated…

Having a process to begin with helps focus improvements

Forecasting Cycle

To optimize effectiveness, make forecasting a closed loop

History

 Create the plan
Forecast vs. Statistical
Actual Forecast

Forecast External
Execution Input
 Measure performance
Feedback
Loop Internal
Scenario
Planning Discussion &
Consensus
Input
 Incorporate feedback

Good forecasting incorporates a thorough gap analysis

Art vs. Science

Art: Subjective
• How to tell “Good” from “Bad”
• What information is useful
• Random many
• Qualitative

2,000,000
1,900,000
1,800,000
1,700,000 Science: Objective
• Measurable
1,600,000
1,500,000
1,400,000
1,300,000
1,200,000
• Cause and effect
1,100,000
1,000,000 • Selective few
• Quantitative
900,000
800,000
700,000
600,000
500,000
Jul-10

Jul-11
Feb-10

Sep-10

Feb-11

Sep-11

Feb-12
Jan-10

Jan-11

Jan-12
Dec-10

Dec-11
Jun-10

Jun-11
Aug-10

Aug-11
May-10

May-11
Oct-10

Oct-11
Nov-10

Nov-11
Apr-10

Apr-11

Apr-12
Mar-10

Mar-11

Mar-12

Part of the science is in understanding what is art

Manage Organizational Knowledge

Integrating multiple sources of information

Ext er nal Int er nal

18 Month Forward Horizon

Consensus Agreed
Sales Plan Sales Plan

External inputs help shape long term forecast

“Good judgment comes from
experience, and a lot of that comes from
bad judgment”

-Will Rogers


Qualitative vs. Quantitative

Different ways to describe a simple cup of coffee

Qualitative:
Descriptions
• Big Cup
• Rich Color
• Strong Brew
• Very Hot

Quantitative:
Measurements
• 20 oz. Serving
• Index of Refraction
• X mg Caffeine / Y oz.
• 150º F

Beware of qualitative masquerading as quantitative

Forecasting as a Collaborative Process

5/29/2012 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Q1 Q2 Q3 Q4 Full Year
Production
Plant A 686,533 654,888 809,606 741,601 801,591 945,080 813,117 919,690 925,298 853,779 775,512 721,038 2,151,026 2,488,272 2,658,105 2,350,329 9,647,733
Plant B 1,040,201 992,254 1,226,676 1,123,639 1,214,532 1,431,939 1,231,995 1,393,470 1,401,967 1,293,604 1,175,019 1,092,482 3,259,131 3,770,110 4,027,432 3,561,105 14,617,778
Plant C 416,080 396,902 490,670 449,455 485,813 572,776 492,798 557,388 560,787 517,442 470,007 436,993 1,303,652 1,508,044 1,610,973 1,424,442 5,847,111
Domestic Production 2,142,814 2,044,044 2,526,952 2,314,695 2,501,936 2,949,794 2,537,909 2,870,549 2,888,053 2,664,825 2,420,538 2,250,512 6,713,809 7,766,426 8,296,511 7,335,876 30,112,622
Imports 142,250 135,693 167,751 153,660 166,090 195,821 168,478 190,560 191,722 176,903 160,686 149,399 445,693 515,571 550,760 486,989 1,999,012
Total Production 2,285,064 2,179,737 2,694,702 2,468,355 2,668,026 3,145,615 2,706,387 3,061,109 3,079,775 2,841,728 2,581,225 2,399,911 7,159,503 8,281,996 8,847,271 7,822,864 32,111,634
Shipments
Region A 555,663 530,050 655,275 600,234 648,788 764,925 658,117 744,375 748,914 691,028 627,681 583,591 1,740,989 2,013,947 2,151,406 1,902,300 7,808,642
Region B 333,398 318,030 393,165 360,141 389,273 458,955 394,870 446,625 449,349 414,617 376,609 350,154 1,044,593 1,208,368 1,290,844 1,141,380 4,685,185
Region C 689,022 657,262 812,542Sales & Demand Plan Flow
744,291 804,498 948,507 816,065 923,025 928,654 856,875 778,324 723,652 2,158,826 2,497,295 2,667,744 2,358,851 9,682,716
Region D 466,757 445,242 550,431 504,197 544,982 642,537 552,818 625,275 629,088 580,464 527,252 490,216 1,462,430 1,691,716 1,807,181 1,597,932 6,559,259
Domestic Shipments 2,044,839 1,950,585 2,411,414 2,208,862 2,387,541 2,814,923 2,421,870 2,739,300 2,756,004 2,542,983 2,309,866 2,147,614 6,406,838 7,411,326 7,917,175 7,000,462 28,735,802
Export 177,812 169,616 209,688 192,075 207,612 244,776 210,597 238,200 239,653 221,129 200,858 186,749 557,116 644,463 688,450 608,736 2,498,765
Total Shipments 2,222,651 2,120,201 2,621,102 2,400,937 2,595,154 3,059,699 2,632,468 2,977,500 2,995,657 2,764,112 2,510,724 2,334,363 6,963,955 8,055,790 8,605,625 7,609,198 31,234,567
External Views
Inventory Inv End Q1 Inv End Q2 Inv End Q3 Inv End Q4
Supply Y/E Inv
Warehouse 1
Warehouse 2
566,776
700,135
576,897
712,638
589,409
728,094
600,870
742,252
613,259 627,864
757,555 775,597
640,431
791,120
654,644 668,944 682,139
808,678 826,343 842,642
694,124 705,267 589,409
857,447 871,213 728,094
Planning 705,267(Plan)
627,864
775,597
668,944
826,343 871,213
705,267
871,213
Warehouse 3 533,436 542,962 554,738 565,525 577,185 590,931 602,758 616,136 629,594 642,013 653,293 663,781 554,738 590,931 629,594 663,781 663,781
Warehouse 4 733,475 746,573 762,765 777,597
Parallel
793,629 812,530 828,793 Consensus
847,186 865,692 882,768 SAP/DP
898,278 912,699 762,765 812,530 865,692 912,699
Control
912,699
Warehouse 5 333,398 339,351 346,711 353,453 360,740 Views
369,332 376,724 Process
385,085 393,497 401,258 408,308 414,863 346,711 369,332 393,497 414,863 Document
414,863
Warehouse 6 466,757 475,092 485,396 494,834 505,036 517,065 527,413 539,119 550,895 561,761 571,632 580,808 485,396 517,065 550,895 580,808 580,808
Total Inventory 3,333,977 3,393,512 3,467,113 3,534,531 3,607,403 3,693,320 3,767,239 3,850,847 3,934,966 4,012,582 4,083,083 4,148,632 3,467,113 3,693,320 3,934,966 4,148,632 (Actual)
Finance 4,148,632

Internal Views

All working to - and collaborating on - the same plan

Performance Measurement

If it can’t be measured, it can’t be (proven) fixed

Direct Measurements:
• Accuracy 10 80%

• Bias 9

8
70%

60%

• MAPE (Mean Average Percent Error) 7 50%

6 40%

Bias (%)
Volume
5 30%

Indirect Measurements: 4 20%

• Customer Service
3 10%

2 0%

• Inventory Levels 1

0
-10%

-20%

• Supply Chain Costs Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12

Forecast Bias (%) Forecast Actual

Make Measurements Matter:
• Tie Accuracy to Compensation
• Pay for Performance

Incorporate measurement as feedback to improve the forecast

Advancing Forecasting Practices

Use SMART goals to drive improvement

Measuring performance highlights collaboration opportunities

“The measure of success is not whether
you have a tough problem to deal with, but
whether it is the same problem you had last
year.”

-John Foster Dulles


Results

Strong process facilitates the path to profitability

S&OP Helps Drive: NAT Segment Operating Income1
($ in Millions) $514
Risk Mitigation $450

$276

Forecast Accuracy
$18

Inventory Turns
($305)
Customer Satisfaction 2009 2010 2011 2012 Prior Target

North American Tire exceeded its 2013 SOI target
one year ahead of plan
1 http://www.goodyear.com/investor/pdf/slides/4qtr12_slides.pdf


For More Information

Michael_Ryan@Goodyear.com

www.Linkedin.com/in/isolveproblems


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