rethink development models€¦ · SOURCE: McKinsey Digital Quotient analysis of 46 publicly traded...

WORKING DRAFTLast Modified 06.05.2019 18:02 W. Europe Standard TimePrinted 4/19/2019 12:02 AM India Standard Time

Vienna | June, 2019

The 4th industrial revolution – and the need to rethink development models

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DR. JAN MISCHKE, MCKINSEY GLOBAL INSTITUTE

Last Modified 06.05.2019 18:02 W

. Europe Standard Time

Printed 4/19/2019 12:02 AM India Standard Tim

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Contents

Digitisation is spreading rapidly

There is significant economic and business upside at stake

Businesses and governments will need to consider key disruptions

▪A rising superstar effect

▪New business strategies

▪Large skill shifts and the future of work

▪More digital flows, less labor cost arbitrage




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The digital ecosystem and the 4th industrial revolution

Industry 4.0

Fundamental technological changes ahead

Digitisation

Artificial intelligence

Automation

Technological convergence




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Starting from a low base, developing economies are beginning to close the gap through rapid adoption of new technologies

9990

45444443

363535

31303030

27252524

Sweden

IndiaIndonesia

ItalyJapan

ChinaRussia

Brazil

Germany

South AfricaFranceSouth KoreaUnited Kingdom

United States

CanadaAustraliaSingapore

7573

67676666656464

615857

5047

4040

32

SwedenSouth Korea

Canada

United KingdomSingaporeUnited StatesAustralia

RussiaJapanGermanyFranceItalyBrazilChinaIndonesiaSouth AfricaIndia

SOURCE: McKinsey Global Institute, Digital India: Technology to transform a connected nation, March 2019, McKinsey Global Institute analysis

Country Digital Adoption Index, Score (0-100), 2017

Growth in Country Digital Adoption Index, % growth, 2014-17

Advanced Emerging




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In many developing countries, the price of data is no longer a constraint on digital adoption

SOURCE: Analysis Mason, January 9, 2019; UN Database; Digital India; McKinsey Global Institute analysis

16

60

2010 1511 12 13 14 20170

10

20

30

40

50 Brazil

India

China

United states

Data pricePer GB of data (% of monthly GDP per capita)

100 240 325800

20 20 30 100 175 200 260700

50 30 30 100 160 260700 750

15 20 90 100397

0

3,000

1,000

2,000

4,000

1211

1,000

2010 13 14 15 160

20170

3,0003,250

3,750

1,622

Data quantityPer connection, per month (MB)

4X




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The same sectors tend to be at the frontier – and lagging behind – in the United States, Europe, and China

1 Index is based only on asset and labour components and thus may not align with heat maps displayed elsewhere 2 Due to accounting differences between the United States and Europe, not all sectors can be fairly compared

SOURCE: McKinsey Global Institute analysis

Relatively lowdigitisation

Relatively highdigitisationMGI Industry Digitisation Index1, Select sectors2

SectorICT

Professional services

Media

Finance and insurance

Wholesale trade

Advanced manufacturing

Real estate

Government

Retail trade

Basic goods manufacturing

Health care

Construction

United States

United Kingdom Germany France

Nether-lands Italy Sweden China




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8McKinsey & CompanySOURCE: The Conference Board Total Economy database; United Nations Population Division; McKinsey Global Institute analysis

GDP growth for G19 and Nigeria

1.70.1 0.1 0.1 0.1

1.8

0.12.8

1.4 0.8

Required to maintain current GDP per capita

Historical

3.5

Early scenarioRequired to achieve projected GDP per capita

Late scenario

Productivitygrowth

Employmentgrowth 0.2

2.9

1.50.9

Full-time equivalent gap1

Billion 0.1 6.7

GDP per capita growthCompound annual growth rate%

2.1 0 2.5 0.5–1.1

1 Additional full-time equivalents (FTEs) needed to achieve growth target.NOTE: Numbers may not sum due to rounding.

Potential impact of automation

Globally, automation could become a significant economic growth engine as employment growth wanes

Historical1964–2014

Future2015–65

Compound annual growth rate%




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18

10

4

Digital leaders

Emerging leaders

Digital laggards

7.8

-1.7

0.1

4.3

-1.4

0.3

Partial adopters of AI

Proactive adopters of AI

Non-adopters of AI

Firms that lead on digitisation perform better according to key financial metrics

Automotive and assembly Consumer packaged goods

5-year revenue growth1

Compound annual growth rate, %Profit margin2

Percentage points difference from industry average

SOURCE: McKinsey Digital Quotient analysis of 46 publicly traded companies; McKinsey Global Institute analysis, McKinsey Global Institute, Digital Europe: Pushing the frontier, capturing the benefits, June 2016; McKinsey Global Institute, AI: The next digital frontier? June 2017

1 Digital leaders, emerging leaders, and digital laggards defined based on Digital QuotientTM material. Here, digital laggards are companies with DQTM scores <30 (<25 in DQTM materials), emerging leaders have DQTM scores of 30-55 (25-50 in DQTM materials), and digital leaders have DQTM scores of >55 (>50 in DQTM materials) 2 Operating profit margin for selected sectors as a share of turnover, for continuing operations and before exceptional items




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AI creates more value compared to traditional analytics techniques, with knock-on implications for the bottom line

NOTE: Numbers may not sum due to rounding.

SOURCE: McKinsey Global Institute, Notes from the AI Frontier, April 2018, McKinsey Global Institute analysis

12889

878585

7967

57565555

50444444

3938

3630

TravelTransport and logistics

High tech

Retail

Oil and gas

Basic materials

Automotive and assembly

Chemicals

Public and social sector

Media and entertainment

Consumer packaged goodsAgricultureBankingHealthcare systems and services

TelecommunicationsPharmaceuticals and medical productsInsuranceAdvanced electronics/semiconductorsAerospace and defense

Average = 62

Potential incremental value from AI over other analytics techniques, %Highest potential impact for AI across manufacturing business problem areas, $ billion

100

100

Logistics network and warehouse optimisation

Yield optimisation

Predictive maintenance

Procurement and spend analytics

300-600

Inventory and parts optimisation

Sales and demand forecast

500-700

100-200

100-200

Manufacturing




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Industry 4.0, specifically, can enhance performance across the manufacturing value chain

SOURCE: Industry 4.0: How to navigate ditigisation of the manufacturing sector, McKinsey Digital, 2015.

1 Cf. McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity 2 McKinsey analysis 3 McKinsey analysis 4 Cf. McKinsey Global Institute: A future that works: Automation, employment, and productivity, January 2017 5 See, for example, ABB case study6 Cf. Bauernhansl, Thomas, ten Hompel, Michael, Vogel-Heuser, Birgit (Hrsg.): Industrie 4.0 in Produktion/Automatisierung/Logistik (2014)

Resource/process

Service/aftersales

Asset utiliza-

tion

Supply/demand

match

Time to

market

Labour

Value Drivers

InventoriesQuality

Productivity increase by 3 - 5%5

64% automation potential for global manufacturing

10 - 40% reduction of maintenance costs1

Forecasting accuracy increased to 85+%3

Costs for inventory holding decreased by 20 - 50%3

20 - 50% reduction in time to market1

30 - 50% decrease in machine downtime2

Costs for quality reduced by 10-20%6




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A rising superstar effect: Economic profits are more concentrated today than they were 20 years ago

SOURCE: McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis

1 Nonfinancial firms only. EBITA stands for earnings before interest, tax, and amortization. NOPLAT stands for net operating profit less adjusted taxes.

Top 10% of economic profit

27.8

41.7

42.8

52.5

30.1

44.8

45.6

53.1Net incomeshare

Revenue share

EBITA share

NOPLAT share

+2.3

+3.1

+2.7

+0.7

Share of global revenue and profit accruing to superstar firms, 1990s vs today

n = 245 firms in 1995–97 and 575 firms in 2014–16%

Top 1% of economic profit

8.2

14.1

14.5

17.8

8.6

17.3

17.6

23.5

+0.4

+3.2

+3.1

+5.7

Average, 1995–97

Average, 2014–16




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Distribution of trajectory for top quintile economic profit generators over 10 years1

% (N = 48 countries and 2,284 total companies2,3)

1 Quintiles based on rankings within archetype by economic profit generation between 2001–05 and 2011–15. Economic profit defined as net operating profit less adjusted taxes (NOPLAT) – [invested capital x weighted average cost of capital].2 Outperformers include China, India, Indonesia, Malaysia, Thailand, Hong Kong, Singapore, and South Korea; high-income countries include Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Israel, Italy, Japan, Netherlands,

Norway, Saudi Arabia, Spain, Switzerland, United Arab Emirates, the United Kingdom, and the United States; non-outperformer emerging economies include Argentina, Brazil, Chile, Colombia, Czech Republic, Egypt, Greece, Hungary, Mexico, Morocco, Nigeria, Pakistan, Peru, Philippines, Poland, Portugal, Russia, Slovak Republic, South Africa, and Turkey.

3 Publicly listed companies with more than $500 million in revenue in 2016, of which 457 were top quintile.

SOURCE: McKinsey Strategy Practice (Beating the Odds model v20.0); McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis

Emerging economies exhibit greater contested leadership among top firms

15

23

62

Drop tothe bottomquintile

Remain inthe topquintile

High income

Drop tothe middle 3quintiles

23

32

45

Outperformers

36

50

58

62

63

63

68

76

Germany

Canada

Australia

United Kingdom

Japan

Switzerland

France

United States

Country Country

20

34

43

60

China andHong Kong

South Korea

Malaysia

India

Firms remaining in top quintile%

Firms remaining in top quintile%




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Comparison of self-reported performance and practices for top-performing firms across archetypes1,2

Absolute difference compared to top-performing firms from high-income economiesN = 7 countries, 2,172 companies3

1 Top-performing defined as top quartile of self-reported revenue growth (over past 3 years) adjusted for country and industry. 2 All reported statistics are calculated as weighted averages across countries within archetype.3 Outperformers include China, India, and Indonesia; non-outperformer emerging economies include Brazil and South Africa; high income includes Germany and the United States.4 Score marks number of dimensions for which respondent answered either “Strongly agree” or “Agree” among 10 dimensions that describe the company’s current innovation capabilities and practices.5 Proactiveness measured as answering either "We have changed our longer-term corporate strategy to address the disruption” or "We initiated the disruption(s)” to question “Which of the following statements best describes your company’s approach

to addressing the technological and digital disruptions that have affected your industry in the past three years?" 6 Score marks number of “changes [made] to the strategy of individual business units…in response to technological and digital disruptions that have affected your industry in the past three years.”7 Based on financial data for large publicly listed companies with more than $500 million in annual revenue; top performing defined as top quartile in terms of total return to shareholders adjusted by industry.NOTE: Not to scale.

SOURCE: McKinsey 2017 Firm Survey; McKinsey Corporate Performance Analytics; McKinsey Global Institute analysis

Top firms in outperformer emerging economies are bolder, quicker, and more forceful than their peers

+8 1.3

0.8

+33

-6

+1.1

+0.5

+1.1

+0.3

-6.0

-7.9

+27

+13

Outperformersvs high income

Non-outperformers vs high income

High income 48 6.5 25 3.2 1.5 18.7 47

Innovation practices4

Score (1 to 10)

Disruption proactive-ness5

Percentage points

Investment levels7

Capex to depreciation ratio

Investment speedNumber of weeks

Expansion priorityMarkets outside home (percentage points)

Innovation assessmentSales from new products (percentage points)

Digital disruption response6

Score (1 to 8)

Innovation and digital disruption InvestmentGeographic expansion




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Three types of ecosystems emerging as sources of value creation

SOURCE: McKinsey team analysis

Horizontal platforms Any-to-any ecosystems

Built around linear series of value-adding steps with the goal of producing specific end products (e.g., Ford)

Built around value-adding software and technology stacks that cut across value chains (e.g., Google, Facebook)

Built around value-creating interactions that quickly adapt to new needs and ideas (e.g., Airbnb and Uber)

Linear value chains

& &




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IKEA’s augmentedreality catalogue on iPad visualizes new furniture inside the consumer’s home

Adidas’s robot-operated “SpeedFactories” create consumer-designed individualized sneakers

Doob Group allows consumers to scan their bodies and provides unique 3D-printed selfie figurines

Spotify creates tailored music recommendations based on playlists generated by other users

Robotic manufacturing Augmented reality 3D printing Big data & analytics

Businesses are utilising new technologies to create “Markets of One”




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Nearly 50% of tasks could be automated by 2030, affecting 760 million workers in emerging economies

SOURCE: McKinsey Global Institute, Harnessing Automation for a Future that Works, 2017, McKinsey Global Institute analysis

Automation potential, % Employees, millions

395.3

235.1

26.1

25.5

21.8

21.0

12.5

6.9

6.7

5.6

2.2

1.1

Thailand

Morocco

China

India

Ethiopia

Nigeria

Egypt

Mexico

South Africa

Peru

Senegal

Costa Rica

51

52

46

52

50

55

49

53

41

51

54

52

Mexico

China

Senegal

South Africa

India

Ethiopia

Thailand

Nigeria

Peru

Egypt

Morocco

Costa Rica




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9 11 16

17 1821

21 2222

20 1815

33 31 26

Basic cognitive skills

Physical and manualskills

Higher cognitive skills

Social and emotionalskills

Technological skills

20021 2016 2030Skill categories

2002–16

3

1

9

13

27

2016–30

11

14

9

26

60

1 Calculated using the 2004 to 2016 CAGR extrapolated to a 14-year period.NOTE: Based on difference between hours worked per skill in 2016 and modeled hours worked in 2030. Numbers may not sum due to rounding.

SOURCE: U.S. Bureau of Labor statistics; McKinsey Global Institute workforce skills model; McKinsey Global Institute analysis

Based on McKinsey Global Institute workforce skills modelUnited States, all sectors, 2002–30Evolution in skill categories% of time

Change in hours worked% difference

Demand for skills will shift due to automation and AI




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$ trillion, 2017

1 Higher-end estimate.2 In value-added terms. The value of services embedded in goods trade and the value of goods embedded in services trade have been removed.NOTE: Services embedded in goods trade defined as services value added in goods trade. Estimate of intangibles provided to foreign affiliates based on company-level data on foreign

affiliate economic profit and expenses, adjusted for the share of revenue associated with intangibles produced by headquarters country. Estimate of free cross-border digital services based on the number of foreign users of global websites and the implied value of digital services (such as social media and messaging services).

5.1

13.4 13.0

4.3

0.8

3.2

Intangiblesprovided to

foreign affiliates1

Gross servicestrade

Servicesembedded in goods trade

Free cross-border digital services1

Adjusted valueof services trade in value added2

Goods trade invalue-added

terms2

+3%

Taking into account the undermeasured aspects of service flows, services already account for more than half of value added in overall trade

SOURCE: Capital IQ; WTO; IMF; World Input-Output Database; Alexa Web Information Service; McKinsey Global Institute analysis




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The share of global trade based on labor-cost arbitrage is less than 20 percent %

1 Excluding energy, mining, and agriculture.2 Australia, Hong Kong, Japan, New Zealand, Singapore, and South Korea. NOTE: Labor arbitrage defined as exports from a country whose GDP per capita is one-fifth or less than that of the importing country. Figures may not sum to 100% because of rounding.

SOURCE: IMF; WTO; OECD; UNCTAD; McKinsey Global Institute analysis

19

55

16

16

20

18

43

17

14

16Resource-intensive goods1

All globalvalue chains1

Regionalprocessing

Labor-intensivegoods

Globalinnovations

53

33

15

20001995 05 10 15 20200

10

20

30

40

50

60

AdvancedAsia2

UnitedStates

Europe

China

+8

-8

-3

+1

2005 2017

Share of global goods trade based on labor arbitrage by type of global value chain

Share of imports based on labor arbitrage by country/region

Change, 2005–17Percentage points

rethink development models€¦ · SOURCE: McKinsey Digital Quotient analysis of 46 publicly traded...

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