Sales Forecasting for Manufacturers

Sales Forecasting for Manufacturers Prepared by: Chris Holling, Managing Executive Director Business Planning Solutions, Advisory Services Division Joyce Brinner Senior Principal, Business Planning Solutions Webcast 5 June 2007

Business-to-Business Sales Forecasting Prepared by: Chris Holling, Managing Executive Director Business Planning Solutions, Advisory Services Division 5 June 2007

Understand Your Business Drivers, Forecast Your Sales ROI from Forecasting: Increasing the accuracy of your forecasting or advance planning process can generate significant returns & Manage Business Cycle Risk. Copyright 2007 Global Insight, Inc. 3

What It Is All About: Decompose Your Sales Drivers Sales 2.6 Driver 1-1.9 Driver 2 0.2 Driver 3-3.2 Driver 4 0.9 Driver 5 1.8 Driver 6 2.4-4.0-3.0-2.0-1.0 0.0 1.0 2.0 3.0 Compound Annual Growth Rate, 2004-09 Copyright 2007 Global Insight, Inc. 4

From the Strategic to the Tactical (and Back) Insight on your business drivers for telling the story to Wall Street Copyright 2007 Global Insight, Inc. 5

Keys to Success in Sales Forecasting Be clear and explicit about how things work. Focus on causality, not correlation. Common sense always trumps econometrics and sophisticated statistical techniques. Process is king: developing internal alignment is more important than the model itself. Copyright 2007 Global Insight, Inc. 6

What Works Best The model is anchored by business unit management knowledge of the distribution of their sales across key end-markets B2B and consumer. The system clearly decomposes historical, current, and future sales into: Market volume growth, Price changes, and Share gains/losses due to new products or competitive initiatives. The model is simple and embodies common sense causal relationships. Copyright 2007 Global Insight, Inc. 7

What Does Not Work A forecasting process tied to black-box regressions, correlations, data mining, or pressure curves to explain the past or forecast the future. Models linked to overly macro concepts like total GDP, total industrial production and not to the specific, causal drivers that affect the company. Copyright 2007 Global Insight, Inc. 8

Keep Grounded in Common Sense THINK about your common sense expectations about how things work your a priori assumptions. LOOK at many time series charts of the dependent and possible independent variables. Develop a basic story about what is really going on. LEARN from how the equations or models from other projects were structured. DESIGN your initial model specification on paper first. Think about ways to normalize data to get at fundamental relationships and control for serial correlation. THINK BASIC focus on the core causal relationships between drivers and sales. Copyright 2007 Global Insight, Inc. 9

The Process Determines Success Copyright 2007 Global Insight, Inc. 10

Separate Drivers of New Demand vs. Replacement Demand New Demand: OEM production, construction, or retooling of customer manufacturing operations. Replacement Demand: product life span, technological change, wear and tear parts and products. New demand tends to be more cyclical than replacement demand. Copyright 2007 Global Insight, Inc. 11

Common B2B Industry Sales Drivers Positives: End-market industry production End-market industry investment Corporate profits Availability of financing Etc. Negatives: Relative prices Interest rates Price of complementary products/inputs (e.g electricity) Etc. Positive or Negative: Technology change New products Pace of model/category redevelopment Changes in regulations Copyright 2007 Global Insight, Inc. 12

Common Company Market Share Drivers Product positioning/ targeting that is different from the industry average Price Financing activity Promotion activity Product rationalization Sales force/distribution channel management Acquisitions/divestitures Copyright 2007 Global Insight, Inc. 13

Going Beyond the Trend Line Benchmark Relevant to What? Missed Market Growth from the Trend Line Approach a Custom Benchmark can Anticipate Market Trends. Identify factors driving demand and create multiple customized benchmarks based on these weights by: Geography, Business Unit, Product Line, etc. Copyright 2007 Global Insight, Inc. 14

Company Sales and Key End-market Drivers US$ thousands 14,000 13,000 12,000 11,000 10,000 9,000 8,000 7,000 6,000 5,000 3.00 2.50 2.00 1.50 1.00 0.50 4,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Index (1995:1=1.0) 0.00 Sales: product X (left scale) Commercial Bldg Investment (right scale) Public Utility Investment (right scale) Comm. Equip. Ind. Prod. (right scale) Metal Fab. Ind. Prod. (right scale) Copyright 2007 Global Insight, Inc. 15

Company Sales and the Weighted Market Index US$ thousands 14,000 13,000 12,000 11,000 10,000 9,000 8,000 7,000 6,000 5,000 3.0 2.5 2.0 1.5 1.0 0.5 4,000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Index (1995:1=1.0) 0.0 Sales: product X (left scale) Weighted Market Index Copyright 2007 Global Insight, Inc. 16

Product X Sales by End-market End-market 2006 Share Food 10% Textile 10% Chemicals 10% Metals 10% Electronics 10% Transportation 10% Rubber 9% Machinery 9% Paper 7% Industrial Gas 5% Utilities 3% Lumber/Woodw orking 2% Petroleum 2% Mining 2% Agriculture 1% Paper, 7% Machinery, 9% Industrial Gas, 5% Rubber, 9% Petroleum, 2% Utilities, 3% Lumber, 2% Agriculture, 1% Food, 10% Textile, 10% Chemicals, 10% Transportation, 10% Electronics, 10% Metals, 10% Copyright 2007 Global Insight, Inc. 17

Industrial Production Indices 1.8 Industrial Production Indices (2000 = 1.0) 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Chemicals Food Metals Textiles Electronics Transportation Rubber 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Copyright 2007 Global Insight, Inc. 18

End-market Production Index for Product X 1.2 End-market Production Index (2000 = 1.0) 1.1 1.0 0.9 0.8 0.7 0.6 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Copyright 2007 Global Insight, Inc. 19

Construction Indices Construction Indices (2000 = 100) 3.0 2.5 2.0 1.5 1.0 0.5 Chemicals Food Metals Textiles Electronics Transportation General Manufacturing 0.0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Copyright 2007 Global Insight, Inc. 20

End-market Construction Index for Product X 1.7 End-market Construction Index (2000 = 1.0) 1.5 1.3 1.1 0.9 0.7 0.5 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Copyright 2007 Global Insight, Inc. 21

Capacity Utilization by Industry 95% 90% 85% Capacity Utilization (%) 80% 75% 70% 65% 60% 55% 50% Chemicals Food Metals Textiles Electronics Transportation Rubber 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Note: Only the top seven end markets are shown on the graph above, but all of Company XYZ s end markets are included in the composite index. Copyright 2007 Global Insight, Inc. 22

End-market Capacity Utilization 100% 95% End-market Capacity Utilization (%) 90% 85% 80% 75% 70% 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Copyright 2007 Global Insight, Inc. 23

Case Study: Sales Driven by Construction Activity 3500 3000 Seasonally Adjusted Shipments Shipments (Seasonally Adjusted) 2500 2000 1500 1000 500 0 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 Copyright 2007 Global Insight, Inc. 24

Seasonal Adjustment Factors: Peak in the 4 th Quarter 104 Statistically Estimated Seasonal Factors 103 Seasonally Adjusted Shipments Seasonal Adjustment Factors 102 101 100 99 98 97 96 1992 1993 1994 1995 1996 1997 1998 1999 Copyright 2007 Global Insight, Inc. 25

Real Adjusted Shipments: Proxy for Volume Shipment Value Adjusted for Product Price Inflation 2596 Real Shipments 2096 1596 1096 596 96 1977 1980 1983 1986 1989 1992 1995 1998 Copyright 2007 Global Insight, Inc. 26

Market Index vs. Shipments (1977 = 1.0) 3.0 Market Index vs. Shipments (1977 = 1.0) 2.5 2.0 1.5 1.0 0.5 Market Index Real Shipments 0.0 1977 1980 1983 1986 1989 1992 1995 1998 Copyright 2007 Global Insight, Inc. 27

Sales Forecasting Models Put it all together in a forecasting model that takes into account the full range of demand dynamics. Copyright 2007 Global Insight, Inc. 28

Econometric Modeling Principles Some Technical Notes As you begin regression analysis, consider the following: Each time you run a regression, see if the coefficients match a priori expectations and look at how it forecasts. Remember the goal is not an equation with a high R-squared, it is an equation that forecasts well. Don t let a regression convince you that basic economic theory is wrong. A positive price elasticity or an income elasticity of 10 is not an exciting new discovery it is evidence of a mis-specified model. Always look at a time series chart of the residuals and think about how what you see may be a clue to a missing variable. Lagged dependent variable and Autoregressive (AR) corrections may happen only at the very end of a modeling process as a last resort to fine tune authorized by the Project Director. Copyright 2007 Global Insight, Inc. 29

Dashboard Reports Implement a sales benchmarking program for each business unit Formalize your market index in the form of a corporate dashboard Copyright 2007 Global Insight, Inc. 30 30

Global Consumer Buying Power: Segmentation and Forecasts Prepared by: Joyce Brinner Senior Principal, Business Planning Solutions

Sizing and Forecasting Markets Consider, quantify, and validate all of the potential drivers of demand for your products. Demographics SIZE & FORECAST YOUR TARGET MARKET Income Distribution Socio-Economics/ Culture Copyright 2007 Global Insight, Inc. 32

Developing a Comprehensive Methodology Database integration, statistical analysis, and econometric modeling capture the complex relationships between market characteristics and market size. Macroeconomic Analysis Household Demographics Consumer Segmentation Consumer Markets Demand/Sales Copyright 2007 Global Insight, Inc. 33

The Key to a Successful Model To be successful, a model must: Include the appropriate concepts Represent the true timing of reactions Possess coefficients with the appropriate magnitudes Earn the confidence of users Copyright 2007 Global Insight, Inc. 34

Start With the Demographics Each segment has a different target market, which is incorporated into the sales forecasting models. Target markets can differ across countries. Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 15-19 20-24 25-29 30-44 45-54 55-64 Copyright 2007 Global Insight, Inc. 35

Weighted Demographic Indices, United States and Germany The target age group for Product 1 is population aged 35 49 in both countries. The target market is shrinking in both countries in coming years. The decline is more prolonged in Germany. 1.10 0.04 Demographic Indexes (2000=1.00) 1.05 U.S. 1.00 0.95 0.90 0.85 Germany 0.80 1990 1995 2000 2005 2010 2015 2020 Demographic Indexes Annual Growth Rate 0.03 0.02 0.01 U.S. 0.00-0.01-0.02-0.03 Germany -0.04 1990 1995 2000 2005 2010 2015 2020 Copyright 2007 Global Insight, Inc. 36

Use Age-specific Weighting by Product 18% Body Product X 18% Product Face Y 15% 15% 12% 12% 9% 9% 6% 6% 3% 3% 0% 0% 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 21% 18% 15% 12% 9% 6% 3% Product Sun Z 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 0% 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 Copyright 2007 Global Insight, Inc. 37

Average Spending per U.S. Consumer Unit by Age 900 Furniture 900 Major Appliances 800 800 700 700 600 600 500 500 400 400 300 300 200 200 100 100 0 All < 25 25-34 35-44 45-54 55-64 65-74 75+ 0 All < 25 25-34 35-44 45-54 55-64 65-74 75+ 900 Men's Apparel 900 Women's Apparel 800 800 700 700 600 600 500 500 400 400 300 300 200 200 100 100 0 All < 25 25-34 35-44 45-54 55-64 65-74 75+ 0 All < 25 25-34 35-44 45-54 55-64 65-74 75+ Copyright 2007 Global Insight, Inc. Source: 2005 BLS Consumer Expenditure Survey 38

Variation in U.S. Population Growth by Age Bracket Age Brackets Total 75+ 70 to 74 65 to 69 60 to 64 55 to 59 50 to 54 45 to 49 40 to 44 35 to 39 30 to 34 25 to 29 20 to 24 15 to 19 10 to 14 5 to 9 0 to 4-1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% Compound Annual Growth Rate, 2007-2020 Copyright 2007 Global Insight, Inc. 39

Average Spending per U.S. Consumer Unit by Income 1,000 Furniture 1,000 Major Appliances 900 900 800 800 700 700 600 500 400 300 600 500 400 300 200 100 - All < $20K $20-30K $30-40K $40-50K $50-70K $70K+ 200 100 - All < $20K $20-30K $30-40K $40-50K $50-70K $70K+ 1,000 Men's Apparel 1,000 Women's Apparel 900 900 800 800 700 700 600 600 500 500 400 400 300 300 200 200 100 100 - All < $20K $20-30K $30-40K $40-50K $50-70K $70K+ - All < $20K $20-30K $30-40K $40-50K $50-70K $70K+ Copyright 2007 Global Insight, Inc. Source: 2005 BLS Consumer Expenditure Survey 40

Generate Multi-dimensional Product Forecasts Total Market Spend Total Age +++ ++ + Avg - Develop standardized reports to identify Hot Spots by: Products Sales Regions Income Bracket Age Bracket Total Income Copyright 2007 Global Insight, Inc. 41

Consider All the Drivers Starting Point: Trend Analysis Identify and validate economic and industry drivers Ending Point: Forecasting Model Put it all together in a forecasting model that takes into account the full range of demand dynamics. Copyright 2007 Global Insight, Inc. 42

Understand the Relative Importance of the Drivers U.K. refrigerator sales are projected to decline 1.2% in 2006. Total U.K. Refrigerator Sales Growth in Interest Rates Real Income Per Capita Growth Acceleration in Home Equity Values in Unemployment Rate Inflation-adjusted Refrigerator Price Growth Household Growth - + - + - + The The decline decline is is attributed attributed to to a a slowdown slowdown in in home home equity equity appreciation appreciation and and rising rising unemployment unemployment rates. rates. These These factors factors more more than than offset offset the the positive positive stimulus stimulus to to sales sales from from falling falling interest interest rates rates & & inflation-adjusted inflation-adjusted refrigerator refrigerator prices, prices, and and rising rising households households & & real real income income per per capita. capita. -3.0% -2.5% -2.0% -1.5% -1.0%-0.5% 0.0% 0.5% 1.0% 1.5% Growth Rate, 2005-06 Copyright 2007 Global Insight, Inc. 43

Analyze and Project New Product Adoption US$ per Minute (1999=1.00) 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 Real=$1.97/minute Nominal=$0.59/minute Cellular Service Costs Cellular Telecommunications Industry Association (CTIA) Bureau of Labor Statistics 8.0 7.0 6.0 5.0 4.0 3.0 2.0 Volume Weight Receiving Power Transmitting Power Composite Attributes Index 0.6 0.4 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 5 Real=$0.82/minute Nominal=$0.24/minute Technology Platforms CDMA GSM TDMA AMPS 1.0 0.0 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 100,000 Subscriber Penetration 4 3 2 TDMA AMPS GSM TDMA AMPS Subscribers (Thousands) 80,000 60,000 40,000 Technology AMPS 1 20,000 Price Existing Subscribers - 0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Copyright 2007 Global Insight, Inc. 44

Project Demand in Emerging Markets Per Capita Communications Spending and GDP Across 35 Countries and Time (1990 2007) Communications Spend per Capita (2000 US $) 900 800 Developed Economies 700 600 500 400 Emerging Markets 300 200 100 0 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 GDP per Capita (2000 US $) Copyright 2007 Global Insight, Inc. 45

UPDATE Target Specific Customers Top Markets by Absolute Change in Number of Middle Class Households: 2004 2020 Number of Households (millions) 50 40 30 20 10 0 14.1% 4.1% 7.5% 17.1% Labels Indicate the Compound Annual Growth Rate of the Middle Class: 2004 2020 3.6% 2.7% 1.5% 2.9% 9.4% 14.9% Mexico Russia South Korea China Brazil Poland Indonesia Argentina Hungary Pakistan Middle Class: Households with income $20k - $60k Sum of 95 countries. Income reported in 1997$. Copyright 2007 Global Insight, Inc. 46

Calculate Market Opportunity for Product X in Brazil Income Bracket Absolute Segment Size in 2003 Estimated Penetration Rate in Real US$ X = Potential Customers Less than $5K 19.5 million 9% 1.7 million Between $5K and $10K 13.5 million 15% 2.0 million Between $10K and $15K 6.1 million 41% 2.5 million Between $15K and $20K 2.9 million 86% 2.5 million Greater than $20K 9.2 million 97% 8.9 million Total Households 51.2 million Opportunity 17.6 million Copyright 2007 Global Insight, Inc. 47

Data are Dumb You Must be Smart Structural modeling most trustworthy Search for causation, not correlation. Be clear whether you are modeling demand or supply only one per equation. T-statistics are often misinterpreted Ts measure precision of estimate, not whether a factor is important. Multicolinearity must be dealt with through constraints, not exclusion of good factors. Copyright 2007 Global Insight, Inc. 48

Benchmark Results Against Relevant Experience Counter-intuitive elasticities are usually a sign of spurious correlation, data errors, or multicolinearity. Short-run income elasticities should be high for discretionary goods, particularly items that consumers can postpone purchasing. Price elasticities should be high when close substitutes are available. Demographic factors, often trendlike, are easily confused with penetration curves. Copyright 2007 Global Insight, Inc. 49

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For Additional Information: Chris Holling E-mail: chris.holling@globalinsight.com Joyce Brinner E-mail: joyce.brinner@globalinsight.com