Sales Forecasting for ManufacturersPrepared by:
Chris Holling, Managing Executive DirectorBusiness Planning Solutions, Advisory Services Division
Joyce BrinnerSenior Principal, Business Planning Solutions
Webcast5 June 2007
Business-to-Business Sales Forecasting
Prepared by:Chris Holling, Managing Executive Director
Business Planning Solutions, Advisory Services Division
5 June 2007
Copyright © 2007 Global Insight, Inc. 3
Understand Your Business Drivers, Forecast Your Sales…
…& Manage Business Cycle Risk.ROI from Forecasting:
Increasing the accuracy of your forecasting or
advance planning process can generate
significant returns
Copyright © 2007 Global Insight, Inc. 3
…& Manage Business Cycle Risk.
Copyright © 2007 Global Insight, Inc. 4
2.4
1.8
0.9
-3.2
0.2
-1.9
2.6
-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0
Driver 3
Driver 2
Driver 1
Driver 4
Driver 5
Driver 6
Compound Annual Growth Rate, 2004-09
Sales
What It Is All About: Decompose Your Sales Drivers
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From the Strategic to the Tactical (and Back)…
Insight on your business drivers–for telling the story to Wall Street
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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.
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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. 8
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.
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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.
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The Process Determines Success
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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.
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Common B2B Industry Sales Drivers
Positives:End-market industry productionEnd-market industry investmentCorporate profitsAvailability of financingEtc.
Negatives:Relative pricesInterest ratesPrice of complementary products/inputs (e.g electricity)
Etc.
Positive or Negative:Technology changeNew productsPace of model/category redevelopmentChanges in regulations
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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
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Going Beyond the Trend Line
Identify factors driving demand and create multiple customized benchmarks based on these weights by:
Geography, Business Unit, Product Line, etc.
…a Custom Benchmark can Anticipate Market Trends.
Missed Market Growth from the Trend Line Approach…
Benchmark – Relevant to What?Benchmark – Relevant to What?
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Company Sales and Key End-market Drivers
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
14,00019
95
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
US$
thou
sand
s
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Inde
x (1
995:
1=1.
0)
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. 16
Company Sales and the Weighted Market Index
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
14,00019
95
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
US$
thou
sand
s
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Inde
x (1
995:
1=1.
0)
Sales: product X (left scale) Weighted Market Index
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Product X Sales by End-market
Metals , 10%
Textile , 10%
Chemicals , 10%
Petroleum , 2%
Paper , 7%
Machinery , 9%
Utilities , 3% Industrial Gas ,
5%
Lumber, 2%
Agriculture , 1%
Electronics , 10%
Transportation , 10%
Rubber , 9%
Food , 10%
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%
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Industrial Production Indices
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Indu
stria
l Pro
duct
ion
Indi
ces
(200
0 =
1.0)
ChemicalsFoodMetalsTextilesElectronicsTransportationRubber
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End-market Production Index for Product X
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
End-
mar
ket P
rodu
ctio
n In
dex
(200
0 =
1.0)
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Construction Indices
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Con
stru
ctio
n In
dice
s (2
000
= 10
0)
ChemicalsFoodMetalsTextilesElectronicsTransportationGeneral Manufacturing
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End-market Construction Index for Product X
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
End-
mar
ket C
onst
ruct
ion
Inde
x (2
000
= 1.
0)
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Capacity Utilization by Industry
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Cap
acity
Util
izat
ion
(%)
ChemicalsFoodMetalsTextilesElectronicsTransportationRubber
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.
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End-market Capacity Utilization
70%
75%
80%
85%
90%
95%
100%
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
End-
mar
ket C
apac
ity U
tiliz
atio
n (%
)
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Case Study: Sales Driven by Construction Activity
0
500
1000
1500
2000
2500
3000
3500
1971 1974 1977 1980 1983 1986 1989 1992 1995 1998
Ship
men
ts (
Seas
onal
ly A
djus
ted)
Seasonally Adjusted Shipments
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96
97
98
99
100
101
102
103
104
1992 1993 1994 1995 1996 1997 1998 1999
Seas
onal
Adj
ustm
ent F
acto
rs
Seasonally Adjusted Shipments
Seasonal Adjustment Factors: Peak in the 4th Quarter
Statistically Estimated Seasonal Factors
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“Real” Adjusted Shipments: Proxy for Volume
96
596
1096
1596
2096
2596
1977 1980 1983 1986 1989 1992 1995 1998Ship
men
t Val
ue A
djus
ted
for P
rodu
ct P
rice
Infla
tion
Real Shipments
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Market Index vs. Shipments (1977 = 1.0)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1977 1980 1983 1986 1989 1992 1995 1998
Mar
ket I
ndex
vs.
Shi
pmen
ts
(197
7 =
1.0)
Market Index
Real Shipments
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Sales Forecasting Models
Put it all together in a forecasting model that takes into account the full range of demand dynamics.
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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. 30
Dashboard Reports
30
Implement a sales benchmarking program for each business unitFormalize your market index in the form of a corporate dashboard
Global Consumer Buying Power: Segmentation and Forecasts
Prepared by:Joyce Brinner
Senior Principal, Business Planning Solutions
Copyright © 2007 Global Insight, Inc. 32
Sizing and Forecasting Markets
Income Distribution
Socio-Economics/Culture
Demographics
SIZE & FORECAST SIZE & FORECAST YOUR TARGET MARKETYOUR TARGET MARKET
Consider, quantify, and
validate all of the potential drivers
of demand for your products.
Copyright © 2007 Global Insight, Inc. 33
Developing a Comprehensive Methodology
Database integration, statistical analysis, and econometric modeling capture the complex relationships
between market characteristics and market size.
HouseholdDemographics
HouseholdDemographics
Macroeconomic Analysis
Macroeconomic Analysis
ConsumerSegmentation
ConsumerSegmentation
Consumer MarketsDemand/Sales
Consumer MarketsDemand/Sales
Copyright © 2007 Global Insight, Inc. 34
The Key to a Successful Model
• Include the appropriate concepts
• Represent the true timing of reactions
• Possess coefficients with the appropriate magnitudes
• Earn the confidence of users
To be successful, a model must:
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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
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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.
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1990 1995 2000 2005 2010 2015 2020
Dem
ogra
phic
Inde
xes
(200
0=1.
00)
U.S.
Germany-0.04
-0.03
-0.02
-0.01
0.00
0.01
0.02
0.03
0.04
1990 1995 2000 2005 2010 2015 2020
Dem
ogra
phic
Inde
xes
Ann
ual G
row
th R
ate
U.S.
Germany
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Use Age-specific Weighting by Product
Body
0%
3%
6%
9%
12%
15%
18%
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59
Face
0%
3%
6%
9%
12%
15%
18%
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59
Sun
0%
3%
6%
9%
12%
15%
18%
21%
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59
Product X Product Y
Product Z
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Major Appliances
0
100
200
300
400
500
600
700
800
900
All < 25 25-34 35-44 45-54 55-64 65-74 75+
Women's Apparel
0
100
200
300
400
500
600
700
800
900
All < 25 25-34 35-44 45-54 55-64 65-74 75+
Men's Apparel
0
100
200
300
400
500
600
700
800
900
All < 25 25-34 35-44 45-54 55-64 65-74 75+
Furniture
0
100
200
300
400
500
600
700
800
900
All < 25 25-34 35-44 45-54 55-64 65-74 75+
Average Spending per U.S. Consumer Unit by Age
Source: 2005 BLS Consumer Expenditure Survey
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Variation in U.S. Population Growth by Age Bracket
-1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0%
0 to 45 to 9
10 to 1415 to 1920 to 2425 to 2930 to 3435 to 3940 to 4445 to 4950 to 5455 to 5960 to 6465 to 6970 to 74
75+Total
Age
Bra
cket
s
Compound Annual Growth Rate, 2007-2020
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Major Appliances
-
100
200
300
400
500
600
700
800
900
1,000
All < $20K $20-30K $30-40K $40-50K $50-70K $70K+
Women's Apparel
-
100
200
300
400
500600
700
800
900
1,000
All < $20K $20-30K $30-40K $40-50K $50-70K $70K+
Men's Apparel
-
100
200300
400
500
600
700800
900
1,000
All < $20K $20-30K $30-40K $40-50K $50-70K $70K+
Furniture
-
100200
300
400
500600
700
800900
1,000
All < $20K $20-30K $30-40K $40-50K $50-70K $70K+
Average Spending per U.S. Consumer Unit by Income
Source: 2005 BLS Consumer Expenditure Survey
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Generate Multi-dimensional Product Forecasts
Total Market Spend
TotalIncome
TotalAge
+++ ++ + Avg -
Develop standardized reports to identify “Hot Spots” by:
• Products• Sales Regions• Income Bracket• Age Bracket
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Put it all together in a forecasting model that takes into account the full range of demand dynamics.
Starting Point: Trend Analysis Identify and validate economic and industry drivers
Ending Point: Forecasting Model
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Consider All the Drivers
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Understand the Relative Importance of the Drivers
Household Growth
Inflation-adjusted Refrigerator Price Growth
∆ in Unemployment Rate
Acceleration in Home Equity Values
Real Income Per Capita Growth
∆ in Interest Rates
Total U.K. Refrigerator Sales Growth
-3.0% -2.5% -2.0% -1.5% -1.0%-0.5% 0.0% 0.5% 1.0% 1.5%
Growth Rate, 2005-06
--
--
--
++
++
++
U.K. refrigerator sales are projected to decline 1.2% in 2006.
The decline is attributed to a slowdown in home equity
appreciation and rising unemployment rates.
The decline is attributed to a slowdown in home equity
appreciation and rising unemployment rates.
These factors more than offset the positive stimulus to sales from falling interest rates & inflation-adjusted refrigerator prices, and rising households & real income per capita.
These factors more than offset the positive stimulus to sales from falling interest rates & inflation-adjusted refrigerator prices, and rising households & real income per capita.
Copyright © 2007 Global Insight, Inc. 44
0
1
2
3
4
5
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
AMPS
TDMAAMPS
GSMTDMAAMPS
CDMA GSMTDMA AMPS
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Bureau of Labor Statistics
Cellular Telecommunications Industry Association (CTIA)
US$ per Minute (1999=1.00)Real=$1.97/minuteNominal=$0.59/minute
Real=$0.82/minuteNominal=$0.24/minute
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
Analyze and Project New Product Adoption
Subscriber Penetration
Cellular Service Costs Composite Attributes Index
Technology Platforms
VolumeWeightReceiving PowerTransmitting Power
-
20,000
40,000
60,000
80,000
100,000
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Subs
crib
ers
(Tho
usan
ds)
Existing Subscribers
Price
Technology
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0
100
200
300
400
500
600
700
800
900
0 5000 10000 15000 20000 25000 30000 35000 40000 45000GDP per Capita (2000 US $)
Com
mun
icat
ions
Spe
nd p
er C
apita
(200
0 U
S $
)Project Demand in Emerging MarketsPer Capita Communications Spending and GDP Across 35 Countries and Time (1990–2007)
Emerging Markets
Developed Economies
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Target Specific Customers
Middle Class: Households with income $20k - $60kSum of 95 countries. Income reported in 1997$.
3.6% 2.7% 2.9% 14.9%9.4%1.5%17.1%7.5%4.1%
14.1%
0
10
20
30
40
50
China Brazil Poland Indo-nesia
Mexico Russia SouthKorea
Argen-tina
HungaryPakistan
Num
ber o
f Hou
seho
lds
(mill
ions
) Labels Indicate the Compound Annual Growth Rate of the Middle Class: 2004–2020
Top Markets by Absolute Change in Number of Middle Class Households: 2004–2020
UPDATE
Copyright © 2007 Global Insight, Inc. 47
Calculate Market Opportunity for Product X in Brazil
8.9 million97%9.2 millionGreater than $20K
2.5 million86%2.9 millionBetween $15K and $20K
2.5 million41%6.1 millionBetween $10K and $15K
2.0 million15%13.5 millionBetween $5K and $10K
1.7 million9%19.5 millionLess than $5K
Potential Customers
Estimated Penetration Rate
Absolute Segment Size
in 2003
Income Bracketin Real US$ X =
17.6 millionOpportunity51.2 millionTotal Households
Copyright © 2007 Global Insight, Inc. 48
Data are Dumb…You Must be Smart
Structural modeling — most trustworthySearch for causation, not correlation.Be clear whether you are modeling demand or supply – only one per equation.
T-statistics are often misinterpretedTs 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
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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 trend-like, are easily confused with penetration curves.
Questions & Answers:To ask a question, please click on the ‘Questions &
Answers’ button within the Webcast system. Please type your question in the pop-up box and click ‘Ask’. (After you click ‘Ask’
you will need to close out of that window.)
Thank you.
For Additional Information:
Chris HollingE-mail: [email protected]
Joyce BrinnerE-mail: [email protected]
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