Leadership Challenges in Digital Transformation

Globelics Academy, Tampere 16.8.2019Matti Sommarberg

• Perspectives on discontinuities• Role of the technology• Empiric findings• Conclusions

Evolution vs. revolution

Foster, Richard N. (1986). Innovation: The Attacker’s Advantage, Summit Books, New York, USA, p.102

Perfo

rman

ce

Effort

Discontinuity

Utterback & Abernathy (1975). A Dynamic Model of Process and Product Innovation, Omega, Vol. 3, No.6, pp. 639-656

Discontinuity is often the target of innovation

It can also disrupt existing businesses or industries

Christensen, Clayton M. (2011 4th ed.). The Innovators Dilemma, HarperCollins, New York, USA, pp. xix

Time

Prod

uct P

erfo

rman

ce

Performance demanded at the low end of the market or in a new emerging segment

Performance demanded at the high end of the market

Progress due to disruptive

technologies

Progress due to sustaining

technologies

Which is not a feature from digital era

Utterback & Suarez (1993). Innovation, Competition and Industry Structure, Research Policy, Vol. 22, No.1, pp. 1-21

Some systemic innovation impact all industries

1780-1830 1830-1880 1880-1930 1930-1970 1970-2010

Nikolai Kondratiev 1892-1938

Dynamics has traditionally been driven by the value chain,

Michael E. Porter (1979), How Competitive Forces Shape Strategy, Harvard Business Review, Vol. 57, Issue 2, pp. 137-145

Rivalry among existing

competitors

Bargaining power of suppliers

Bargaining power of customers

Threat of new entrants

Threat of substitute products or services

Firm / Industry

Economic

Social

Technological

Ecological

Legal

Political

Modified from Aguilar, Francis Joseph (1967). Scanning the Business Environment, Collier-Macmillan, Toronto, Canada, pp. 1-17

as well business environment at large

or by the stakeholders.

Firm

Local Community

Owners

Consumer Advocates

Customers

Competitors

MediaEmployees

SIG

Environ mentalists

Suppliers

Governnents

Modified from Freeman, Edward, R. (2015). Strategic Management: A Stakeholder Approach, Cambridge University Press, Cambridge, UK, p. 25

Digital technologies drive industry convergence

Convergence: “the blurring of industry boundaries that creates competition among firms, which did not compete with another previously.”*

• ICT and media• Functional food• Consumer / industrial• Service into products• Software into everything

McKinsey Quarterly October 2017

* Prescott, J.E.; Karim S; Hsu S. (2014). The Strategic Process and Competitive Dynamics of Industry Convergence, Strategic Management Society, 34th Annual Conference, Madrid

and disruption cases are often out-of-industry (and they also shadow the actual phenomena)

Share economy Platform

Open Innovation Blockchain

13

Platforms are not explained by the value chain

Network effects

End-user knowledge

Open platforms

e.g. Alibaba

Innovation platforms

e.g. Apple Store

Trading platforms

e.g. Uber, AirBnB

Integration platforms

e.g. Santander “All in One Service”

Ailisto et al. (2016), Onko Suomi jäämässä alustatalouden junasta?, VNK 19/2016, p.18

Market pull(free competition from

customers)

Technology push(customer monopoly)

Two-way markets Multidirectional markets

Impact of digital technologies is difficult to detect as

Most digital technologies are generic

• Multiple applications• Across industries

Technologies are systemic and interrelated

Magnitude is also in our head

Availability HeuristicsKahneman, Daniel (2003). Maps of Bounded Rationality: Psychology of Behavioral Economics, American Economic Review, Vol. 93, Issue 5, pp. 1449-1475

Bounded RationalitySimon, Herbert A. (1979). Rational Decision Making in Business Organizations, American Economic Review, Vol. 69, Issue 4, pp. 493-513

Industry RecipeSpender J.-C. (1989), Industry Recipes, An Enquiry into Nature and Sources of Managerial Judgement, http://jcspender.com/uploads

Halo EffectRosenzweig, Phil (2007). The Halo Effect, Free Press, New York,

Absorptive CapacityCohen, Wesley M.; Levinthal, Daniel A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation, Administrative Science Quarterly, Vol. 35, Issue 1, pp. 128-152

Dominant LogicPrahalad, C.K.; Bettis, Richard A. (1986). The Dominant Logic: a New Linkage Between Diversity and Performance, Strategic Management Journal, Vol. 7, Issue 6, pp. 485-501

Status Quo BiasDaniel Kahneman; Jack, L. Knetsch; Richard H. Thaler (1991). Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias, The Journal of Economic Perspectives, Vol. 5, Issue 1, pp. 193-206

AM / 3D printing

https://3dprinting.com/news/royal-navy-reveals-nautilus-100-3d-printed-submarine-concept/

• Decoupling physical value chain• New Features • Hybrid opportunities

• Material coverage ?• Cost development ?• New business models ?

https://www.worldmaritimenews.com

Big Data / Artificial Intelligence

Hans Morawec (2003), Robots, after all, Communications for ACM, Vol. 46, Issue 10, pp. 90-97

Moore

Cloud Computing

• Cost?• Capacity?• Security?• New business models?

• New normal already?

Internet of Things

0

5000

10000

15000

20000

25000

2016 2017 2018 2020

IoT installed base (millions of units)

Consumer Business (Cross-Industry)Business (Vertical)

8.4 Billion Connected "Things" Will Be in Use in 2017, Up 31 Percent From 2016, Gartner press release from February 7, 2017

• The development is inevitable• 5G?• Architecture?• Security?• New business models?

Model Based System Engineering

• Modelling capabilities• Multidisciplinary• Life time• Advanced design tools• Interfaces like AR, VR• Digital twin concept• Enabling co-creation

https://community.plm.automation.siemens.com/t5/Digital-Transformations/The-value-of-the-digital-twin/ba-p/385812?lightbox-message-images-385812=31800iB94D19235331E799

Robotics and/or autonomic systems

www.kalmarglobal.com

http://www.tut.fi/fi/tietoa-yliopistosta/uutiset-ja-tapahtumat/ robottibussit-liikenteeseen-hervannassa-x171216c3

https://waymo.com/

https://www.amazon.com

https://sanbot.com

https://www.rolls-royce.com/products-and-services/marine/ship-intelligence.aspx

What factors, enabled by digitalization, drive disruption in machine building industry serving the global container handling?

Does the view differ based on position in the value chain?

Continuous improvement Quantum leap Disruption

Products

Services

Operations

Business models

Nature of the potential impact

Staying where you are- This is housekeeping

Change of positions- Innovation gets you here

Change of rules- Transformation gets you here

Enabler Actor User

Decision-maker *

Synthesizer *

Scientist *

* Kuusi, Osmo (1999). Expertise In the Future Use of Generic Technologies, Epistemic and Methodological Considerations Concerning Delphi Studies, VATT-Research reports 59, Helsinki, pp. 36

• 246 research articles, books or reports

• 305 survey answers from 23 countries

• 60 interviews in 11 countries

• 3 industry cases

• Current business and ongoing experiments

https://tutcris.tut.fi/portal/en/publications/digitalization-as-a-paradigm-changer-in-machinebuilding-industry(9aecb1d2-dc64-4336-9c7e-44d7f27c3127).html

Digitalization impact

Survey

Drivers

Technology # 1 / 36 Mean

Big Data / AI 18 1.9Internet of Things 9 2.4Model Based System Engineering

5 3.9

Robotization 2 3.7AM / 3D 2 5.2Cloud computing 0 4.9

Economic Mean

Networks, crowds, platforms

13 2.2

Industry convergence 9 3.8Radical innovation 7 3.8Digital business models 4 2.7Firm, strategy and management

2 4.1

Industry forces (six) 1 5.2Risk capital 0 6.4

Delphi interviews

Big data, Artificial intelligence

18 / 361.9 / 1,3

- +

Delphi interviews

0 10 20 30 40 50

Prescriptive potential

Knowledge scalability

Users open their data

Advancement (e.g.cognitive computing)

Reduce of human errors

Emergence of platformtools

0 10 20 30 40 50

Lack of competences

Management beliefs

Data ownership conflictof interest

Data security risks

Data ownershipconfusion

Industry beliefs

13 /362,2 / 1,4

- +

Delphi interviews

0 10 20 30 40

Platform (industry)

Crowd intelligence effect

Platform (disipline)

Self-fulfillment motives ofknowledge workers

Unrestricted capacity

Fixed cost light

0 20 40 60

Management beliefs

Lack of capabilities(orchestration)

Legacy systems andprocesses

Lack of systems andprocesses

Conflict in IPR

Networks, crowds, platforms

Conclusions

Near-term

Long-term

1.Operational Efficiency• Asset utilization• Operational cost reduction• Worker productivity

2. New Products & Services• Pay-per-use• Software-based services• Data monetization

3. Outcome Economy• Pay-per-outcome• New connected ecosystems• Platform-enabled marketplace

4. Autonomous Pull Economy• Continuous demand-sensing• End-to-end automation• Resource optimization & waste reduction

World Economic Forum (2015). Industrial Internet of Things: Unleashing the Potential of Connected Products and Services

This appears still to make sense

Porter, Michael E.; Heppelman, James, E. (2014). How Smart, Connected Products Are Transforming Competition, Harvard Business Review, Vol. 92, Issue 11, pp. 64-88

Farm Equipment

System

Irrigation System

Seed Optimization

System

Weather Data

System

Farm Management

System

Weather mapsWeather forecasts

Weather data applicationRain, humidity, temperature sensors

Farm performance database

Seed database

Seed optimization application

Irrigation applicationField

sensorsIrrigation nodes

This already exists

CompetitorsCustomers

X

You You

Platforms come – like it or not

Suppliers

Anyone

Systemic productivity that has an impact to physical assets

Growth of underlying business activity

Is hardware growth sustainable?

M. Sommarberg, April 2014

Rogers, Everett M. (2003, 5th ed.) The Diffusion of Innovation, Free Press, New York, USA, p. 281

Early Adopters13.5 %

Early Majority34 %

Late Majority34 %

Laggards16 %

Innovators2.5 %

Diffusion of innovation still likely to matter

Degree of BigData AI Utilization

Datacompetencies

+

Efficiency ofoperations

Managementbeliefs

Industrybeliefs

Dominantlogic

Pressure toinvest

CapabilityInvestments

Amount ofdata

+Amount of IoT

sensors

Public data

+

Industryconvergence

Amount of relevanthistory data

+

Assets+

Advancement incognitive computing

Use cases

+

+Performingintelligence

New productfeatures

Advancedservices

New bizmodels

Domain intelligenceacquistion

Utilization ofplatforms

Orchestrationcapability

Disciplineintelligenceacquisition

Users opentheir data

Industryactivity

<Managementbeliefs>

Platformcapasity

Companyinteligence

Legacy

Platformsupply

Contributorsupply

Entrepreneurialknowledge workers

The impact is systemic

36

Public opinion (=Google trend)

Digitalization

Machine learningBig dataData analytics

37

Finnish experts and temporality

0,01,02,03,04,05,06,07,08,09,0

Products Service Operations Business models

Impact

2014 mean 2017 mean

0123456789

3D prin

ting

AI / Big da

taClou

d IoT

Industr

ial Inter

net

MBSE

Open In

novatio

n

Roboti

cs

Drivers

2014 mean 2017 mean

M. Baghai, S. Coley and D. White, The Alchemy of Growth, Texere Publishers, 2000. p. 5

Profit

Years

Horizon 1 – extend and defend core business

Horizon 3 – create viable options

Horizon 2 – build emerging businesses

Three strategic horizons

Temporality / Learning

Minzberg, Henry; Ahlstrand, Bruce; Lampel, Joseph (1998). Strategic Safari, The Free Press, New York, USA, p. 369

AM AI IoT PE OE OIProductsServicesOperations

Critical capabilities mapped, example

Spare part stock

becomes virtual

OEM decides technical

specification->

Higher standardization

AI can take care of 90 %

of the help desk

->Offshoring?

Are capability needs related to strategic ambition level (firm, industry, generic)?

• Business becomes data and user centric• Platforms decouple old value chains that transforms into networks• Speed of disruption is not slowed down by technology• Service of everything, outcome economy, systems of systems

disrupt current business models• View of impact is a strategic choice (strong polarization) • Leadership is vital (if transformation needed)• Capabilities and understanding -> Experiments -> Learning• Fundaments do not disappear (real world is still going be

analogical, domain knowledge)

• Co-Creation, between disciplines, systemic eco-systems, experiments -> long term PPP (including users when possible)

Conclusions

“Those who cannot change their minds cannot change anything”

- George Bernard Shaw