forecasting technological change (2)

04/11/23 1

Forecasting Technological ChangeForecasting Technological Change

Session 2. Trend AnalysisSession 2. Trend AnalysisPaul A. Schumann, Jr.Paul A. Schumann, Jr.

Glocal Vantage, Inc.Glocal Vantage, Inc.

04/11/23 2

SessionsSessions

• IntroductionIntroduction

• Trend Analysis TechniquesTrend Analysis Techniques

• Expert Opinion TechniquesExpert Opinion Techniques

• Integrative TechniquesIntegrative Techniques

• ClosingClosing

04/11/23 3

SessionsSessions

• IntroductionIntroduction

• Trend Analysis TechniquesTrend Analysis Techniques

• Expert Opinion TechniquesExpert Opinion Techniques

• Integrative TechniquesIntegrative Techniques

• ClosingClosing

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2. Trend Analysis Techniques2. Trend Analysis Techniques

• Shape of S-CurveShape of S-Curve• Dependent on utility functionDependent on utility function• Based on historical data Based on historical data

(time or experience)(time or experience)• Not causalNot causal• ReproducibleReproducible• Requires analysis of driving Requires analysis of driving

forcesforces

If you don’t know where you If you don’t know where you are, and how you got there, are, and how you got there,

how can you possibly how can you possibly know where you are going?know where you are going?

Abe LincolnAbe Lincoln

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TechniquesTechniques

• AnalogyAnalogy

• Precursor DevelopmentsPrecursor Developments

• Trend ExtrapolationTrend Extrapolation

• Limit CurveLimit Curve

• Learning CurveLearning Curve

• Substitution AnalysisSubstitution Analysis

• Multiple Substitution AnalysisMultiple Substitution Analysis

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• AnalogyAnalogy







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Economic Maturation of TechnologyEconomic Maturation of Technology

• Nonproductive (25 years)Nonproductive (25 years)

• Counter Productive (25 years)Counter Productive (25 years)

• Hyperproductive and Transformational Hyperproductive and Transformational (25 years)(25 years)

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Future ForecastFuture Forecast

• First Industrial Revolution (1760 - 1860)First Industrial Revolution (1760 - 1860)

• Second Industrial Revolution (1860 - Second Industrial Revolution (1860 - 1950)1950)

• Third Industrial Revolution (1950 - Third Industrial Revolution (1950 - 2020)2020)

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Nature of WorkNature of Work

Source: Snyder, et al, “The Strategic Context of Educationin America 2000 - 2020

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Information TechnologyInformation Technology

Source: Jeremy Greenwood, The Third Industrial Revolution

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Third IR TechnologiesThird IR Technologies

0 20 40 60 80 100 120 140

Telephone

TV

Transistor

Electronic Computer

Programming

Fiber Optics

IC

Sattelite Communications

Internet

PC

Cell Phones

Tec

hn

olo

gy

Age (years)

195019752000

NPCPHP

2025

DP

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• AnalogyAnalogy







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PrecursorPrecursor

Time (Effort, Experience)

Util

ityF

unct

ion

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Precursor ExamplePrecursor Example

Source: Ralph Lenz,TFI

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Precursor AnalysisPrecursor Analysis

• Identify lead-lag relationshipIdentify lead-lag relationship

• Obtain lead-lag dataObtain lead-lag data

• Decide whether lead-lag relationship will Decide whether lead-lag relationship will continuecontinue

• Causal connections are bestCausal connections are best

• Similar control factors are next bestSimilar control factors are next best

• Accidental relationships are misleadingAccidental relationships are misleading

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• AnalogyAnalogy







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Trend ExtrapolationTrend Extrapolation• IfIf

– Technical parameter has utilityTechnical parameter has utility– Market continues to value utilityMarket continues to value utility– Driving forces remain sameDriving forces remain same– Not approaching limitNot approaching limit

• ThenThen– Most technologies follow a pattern of constant Most technologies follow a pattern of constant

percentage increasepercentage increase

– y = yy = y00eemt mt i.ei.e..(log y = log y(log y = log y00 + m t+ m t ) (natural log)) (natural log)

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Trend AnalysisTrend Analysis

Source: Ralph Lenz, TFI

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First Technology Trend ForecastFirst Technology Trend Forecast

0

10

20

30

40

50

60

70

80

90

100

1940 1945 1950 1955 1960 1965

Year

Nu

mb

er o

f E

lect

ron

ic C

om

po

ne

nt

Par

ts

(Th

ou

san

ds

)

B-58

B-52

B-47

B-50B-29B-17 Glocal Vantage, Inc.

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Internet UsersInternet Users

0

100

200

300

400

500

600

700

800

900

1980 1985 1990 1995 2000 2005

Year

Inte

rne

t U

se

rs (

WW

, M

illi

on

s)

eTForecasts(2000)

ComputerIndustryAlmanac(2000)

ComputerIndustryAlmanac(1998)

Tapscott(1996)

USDOC(1999)

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0.001

0.01

0.1

1

10

100

1000

10000

100000

Year

Inte

rne

t U

ser

s W

orl

dw

ide

(M

illio

ns)

eTForecasts (2000)

Computer Industry Almanac(2000)

Computer Industry Almanac(1999)

Tapscott (1996)

US DOC (1999)

Trend Analysis (2001)

World's Population

World's Population

Glocal Vantage, Inc.

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Internet Users AnalysisInternet Users Analysis

• GrowthGrowth– 114% increase per year114% increase per year– doubling time = 11 monthsdoubling time = 11 months

• Surpass world’s population in 2005Surpass world’s population in 2005

• Is it a utility function?Is it a utility function?

• Limits to growth?Limits to growth?

• What’s a user? What’s a user?

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IC ExampleIC Example

www. intel.com/research/silicon/mooreslaw.htm

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Microprocessor SpeedMicroprocessor Speed

Source: TFI (1990)

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Evolution of Computer PowerEvolution of Computer Power

Source: Hans Moravec

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Information Revolution?Information Revolution?

Source: Schumann (1982)

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Genetic SequencingGenetic Sequencing

Source:www.ncbi.nih.gov/Genbank/genbankstats.html

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Genetic SequencesGenetic Sequences

100

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

1982 1987 1992 1997 2002

Year

Nu

mb

er o

f S

equ

ence

s (m

illio

ns)

Doubling every 17 monthsSource: Enriquez

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Chromosome MappingChromosome Mapping

Source: www.ornl.gov/hgmis/posters/chromosome

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Trend ExtrapolationTrend Extrapolation• Specify assumptionsSpecify assumptions• Include quantitative evidenceInclude quantitative evidence• Follow logical approachFollow logical approach• Use prior rates of improvement unlessUse prior rates of improvement unless

– There is a limitThere is a limit– Utility is changingUtility is changing– Approach is changingApproach is changing– Driving forces are changingDriving forces are changing

• Document reasonsDocument reasons• Useful to develop alternative projectionsUseful to develop alternative projections• Provide a basis for rational discussionProvide a basis for rational discussion

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• AnalogyAnalogy







04/11/23 32

Limit CurveLimit Curve

• Organic growth modelOrganic growth model• Often used when technology approaches a Often used when technology approaches a

natural limitnatural limit• Limit may not be realLimit may not be real• Use limit and make forecastUse limit and make forecast• Review limit - real?Review limit - real?• Shifts usually occur as limit is approached if Shifts usually occur as limit is approached if

utility still existsutility still exists

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Growth CurveGrowth Curve

Source: Alan Porter

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100

150

200

250

300

350

400

1930 1940 1950 1960 1970 1980 1990 2000

Year

Eg

gs/

Ch

icke

n Y

ear

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100

150

200

250

300

350

400

1930 1940 1950 1960 1970 1980 1990 2000

Year

Eg

gs/

Ch

icke

n Y

ear

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100

150

200

250

300

350

400

1930 1940 1950 1960 1970 1980 1990 2000

Year

Eg

gs/

Ch

icke

n Y

ear

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100

150

200

250

300

350

400

1930 1940 1950 1960 1970 1980 1990 2000

Year

Eg

gs/

Ch

icke

n Y

ear

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• Pearl curvePearl curve

• y = L / (1+a e y = L / (1+a e -bt-bt))

• L = 350L = 350

• a = 1.71a = 1.71

• b = .038b = .038

• t = 0 in 1937t = 0 in 1937

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0.001

0.01

0.1

1

10

100

1000

10000

1980 1985 1990 1995 2000 2005 2010 2015

Year

Nu

mb

er (

mill

ion

s)

Data

Forecast

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Limit CurvesLimit Curves

• PearlPearl

• Fisher-PryFisher-Pry

• GompertzGompertz

• BertalanffyBertalanffy

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• PearlPearl




y = L / (1 + a exp (- b t))y = L / (1 + 10(A - B t))

L = limita = controls locationb = controls shape

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• PearlPearl




f = 1/2 (1 + tanh a (t - t0))

f = fraction of applications in which the new technology has been substituted for the oldt0 = time for 50% substitutiona = controls shape

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• PearlPearl




Y = L exp (- b exp ( - k t))

Non symmetrical

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• PearlPearl




y = L + (y0 - L) exp (- b t)

L = limity0 = initial valueb = controls shape of curve

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Envelope CurveEnvelope Curve

Source: Ralph Lenz, TFI

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ScanningScanning

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Envelope CurveEnvelope Curve

Source: TFI 1983

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• AnalogyAnalogy







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Learning CurveLearning Curve

• Some improvements can be forecast Some improvements can be forecast better based on experience rather than better based on experience rather than time.time.

• Frequently production costs follow this Frequently production costs follow this type of relationshiptype of relationship

• y = a xy = a x- b- b

• log y = log a - b log xlog y = log a - b log x

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Learning CurveLearning Curve

• Frequently experience is equivalent to Frequently experience is equivalent to cumulative production quantity cumulative production quantity

• Usually expressed as percent Usually expressed as percent improvement for each doubling of improvement for each doubling of experienceexperience– 5% cost reduction in 2x5% cost reduction in 2x– 95% learning curve95% learning curve

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Learning Curve ExampleLearning Curve Example

Source: Porter

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Residential Electric Power Learning CurveResidential Electric Power Learning Curve

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500 4.000

Log (cumulative residential consumption in terawatt hours)

Lo

g (

cen

ts p

er k

ilo

wat

t h

ou

r in

196

7 d

oll

ars

)

Data

1915

1920

1925

1930

1935

19401945

19501955

19601965

1970

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0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 4.500


Lo

g (

cen

ts p

er k

ilo

wat

t h

ou

r in

196

7 d

oll

ars

)

Data

Forecast

1915

1920

1925

1930

1935

1940

1945

1950

1955

1960

1965

19701975

1980

1985

66% Learning Curve

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0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 4.500


Lo

g (

cen

ts p

er k

ilo

wat

t h

ou

r in

196

7 d

oll

ars

)

Data

Forecast

1915

1920

1925

1930

1935

1940

1945

1950

1955

1960

1965

19701975

1980

1985

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• AnalogyAnalogy







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Fisher-Pry Substitution AnalysisFisher-Pry Substitution Analysis

• f = 1/2 (1 + tanh a (t - tf = 1/2 (1 + tanh a (t - t00))))

• f = fraction of applications in which f = fraction of applications in which the new technology has been the new technology has been substituted for the oldsubstituted for the old

• tt00 = time for 50% substitution = time for 50% substitution

• a = controls shapea = controls shape

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Shape of Fisher-Pry CurvesShape of Fisher-Pry Curves

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8

Time Units

Rat

io (

new

/ o

ld)

t0 = 3

a = 0.5

a = 1

a = 1.5

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Substitution of Steam for SailSubstitution of Steam for Sail

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1780 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980

Year

Ste

am

Gro

ss

To

nn

ag

e

Steam

Sail

04/11/23 59

Steam/Sail Fisher-Pry SubstitutionSteam/Sail Fisher-Pry Substitution

0.01

0.1

1

10

100

Year

Ste

am

/Sa

il

Data

Fisher-Pry Model

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CATV Substitution in USCATV Substitution in US

0.001

0.01

0.1

1

10

1950 1960 1970 1980 1990 2000

Year

Ho

me

s w

ith

CA

TV

/Ho

me

s w

ith

ou

t C

AT

V (

US

)

Data

Forecast

Source: Martino, 1983

04/11/23 61

CATV Substitution (US)CATV Substitution (US)

0.001

0.01

0.1

1

10

1950 1960 1970 1980 1990 2000

Year

Ho

mes

wit

h C

AT

V/H

om

es w

ith

ou

t C

AT

V (

US

)

Data (1994, 1996)

Forecast

Data (1980)

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Telecommunication Technology SubstitutionTelecommunication Technology Substitution

0.01

0.1

1

10

100

1000

Year

Sto

red

Pro

gra

m/E

ctr

om

ech

an

ica

l

Data

Forecast

Source: Lenz & Vanston, TFI 1986

04/11/23 63

Telecommunications Substitution AnalysisTelecommunications Substitution Analysis

0

20

40

60

80

100

120

140

0% 20% 40% 60% 80%

Stored Program % of Total Market

Ele

ctro

me

chan

ica

l Sys

tem

s (

mill

ion

s o

f su

bsc

rib

ers

)

ElectromechanicalMarket

Total MarketGrowth Rate

100%

80%

60%

40%

20%

0%

Total Market Annual Growth Rate

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• AnalogyAnalogy







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Multiple Substitution AnalysisMultiple Substitution Analysis

• Requires a more generalized Requires a more generalized substitution modelsubstitution model

• Lotka-Volterra equationLotka-Volterra equation– dXdXn n / dt = X/ dt = Xnn M Mnn(X(Xnn) = growth in technology) = growth in technology

– X = existing levels of technologyX = existing levels of technology– M(X) = market potentialM(X) = market potential

= a= an n - b- bnn X - c X - cnn Y Y

04/11/23 66

Multiple Substitution ExampleMultiple Substitution Example

Source: Porter

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Comparison of ModelsComparison of ModelsModel Equation Growth Conditions

Linear X = A t + B dX/dt = A Slow growth, shorttime periods

Exponential X = exp(A t) dX/dt = A X Constantpercentage growth

Pearl X = A / (1+B exp(-C t)) dX/dt = X (-A - C X) Modeltechnologies thathave similargrowth rates

Gompertz X = A exp(-C exp(-D t)) dX/dt = C X exp(-A t) Models oldertechnologygrowing obsolete

Source: Porter

04/11/23 68

SummarySummary

• Trend analysis has strong historical Trend analysis has strong historical validationvalidation

• Models have been accepted but not Models have been accepted but not explainedexplained

• QuantitativeQuantitative

• Does not explain how change will occurDoes not explain how change will occur

• Often misused and misunderstoodOften misused and misunderstood

04/11/23 69

Summary (cont.)Summary (cont.)

• First noticed by Henry Adams in 1918First noticed by Henry Adams in 1918

• ““Knowledge begets knowledge as Knowledge begets knowledge as money begets interest.” - Arthur Conan money begets interest.” - Arthur Conan DoyleDoyle

04/11/23 70

IC Line WidthIC Line Width

0.01

0.1

1

10

100

1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Ine

Wid

th (

mic

rom

ete

rs)

Data

Forecast

04/11/23 71

Trend AnalysisTrend Analysis

• AnalogyAnalogy• Precursor Precursor

DevelopmentsDevelopments• Trend ExtrapolationTrend Extrapolation• Limit CurveLimit Curve• Learning CurveLearning Curve• Substitution AnalysisSubstitution Analysis• Multiple Substitution Multiple Substitution

AnalysisAnalysis

Data, InsightData, InsightSurveillance Material

ChangeChangeTrend Analysis

Formal

72

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• PO Box 161475PO Box 161475

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• (512) 632-6586(512) 632-6586

• [email protected]@glocalvantage.com

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• Twitter: innovant2003Twitter: innovant200304/11/23

Paul SchumannPaul Schumann

• Futurist and innovation consultantFuturist and innovation consultant• Application of web 2.0 to market & strategic Application of web 2.0 to market & strategic

intelligence systemsintelligence systems• Web 2.0 tools & technologiesWeb 2.0 tools & technologies• Application of web 2.0 to democratic processesApplication of web 2.0 to democratic processes• Broad perspectives on the futureBroad perspectives on the future• ServicesServices

– Strategic market research & technology forecastingStrategic market research & technology forecasting– Intelligence systems consultingIntelligence systems consulting– Seminars, webinars & presentationsSeminars, webinars & presentations

7304/11/23

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04/11/23 74