Index

Real Time Strategy: When Strategic Foresight Meets Artificial Intelligence

ISBN: 978-1-78756-812-9, eISBN: 978-1-78756-811-2

Publication date: 29 April 2020

This content is currently only available as a PDF

Citation

Schühly, A., Becker, F. and Klein, F. (2020), "Index", Real Time Strategy: When Strategic Foresight Meets Artificial Intelligence, Emerald Publishing Limited, Leeds, pp. 195-198. https://doi.org/10.1108/978-1-78756-811-220201010

Publisher

:

Emerald Publishing Limited

Copyright © 2020 Emerald Publishing Limited


INDEX

Adaptiveness (see also Complexity)
, 133–140, 143–145, 161, 163

wall
, 133

Agents
, 6

Aging
, 7

Agrochemicals in Asia
, 83

Airbus A400M
, 38

Amazon
, 92, 105

Apple
, 90, 92–93, 99–100, 121–122

1973 Arab–Israeli War (see Yom Kippur War)

Artificial intelligence (AI)
, 2, 18, 28, 74, 114, 145, 153, 165

advantages
, 148

AI-generated insights
, 103–105

augmenting modelling stage
, 156–159

augmenting monitoring stage
, 159–163

augmenting research stage
, 154–156

excuse
, 145

general
, 148

scenario planning in combination with
, 144–145

AT&T
, 34–35, 44

Automation
, 18

‘Automotive’
, 155–156

Autonomous driving
, 145

Availability bias
, 49

Battery technologies
, 9

BCG Matrix
, 28

Bias

availability
, 49

cognitive
, 10–11

Big bang problems
, 47–48

Big Data
, 2, 105, 170, 172

‘Black box’ of Game Theory
, 144

Black swans
, 79–80

Blockbuster
, 7–8, 14

Brainstorm
, 144, 154

Building Narratives
, 89

California Institute of Technology
, 39

Captive banks
, 8–9

real competition for
, 9

Carmakers
, 10

Characters
, 89–90

Chernobyl nuclear disaster
, 79

Chicago Times
, 35

Choice Cascade
, 95–96

Clarity (see also Relevance; Validity)
, 13–17

Classical agent-principal dilemma
, 6

Classical crowdsourcing
, 104

Climate change
, 2, 4, 73, 132, 175

Cognitive bias
, 10–11

Complex adaptive systems
, 147–148, 154, 159, 180–182

Complex quantitative models
, 33

Complex simulation models
, 36

Complexity (see also Adaptiveness)
, 124–133, 138, 140–141, 143–145, 161, 163

management
, 17–19

wall
, 126

Complicatedness
, 127–128, 136

Conflict
, 90

Consultants
, 12

Corporations
, 11

Creativity
, 19–20, 31, 48–49, 59, 77, 120

Crisis-response-scenario
, 91

Critical uncertainties
, 59, 76–83, 85, 156–158

Crowdsourcing
, 103–105

Cutting-edge technology
, 97–100

Decision analysis
, 32

Decision makers
, 2–3, 6–7, 11–12, 16–17, 20, 23, 77–78

ideal
, 13

liability of
, 12

Decision making

processes
, 6, 11

quality
, 13

Deep Blue computer
, 146

DeepL
, 146

Desirable future
, 37

Dieselgate scandal
, 9–10

Differentiator
, 117, 119, 161

Digital dementia
, 79

Digital media
, 106–107

Digital photography
, 77

Digital Strategy
, 113

Digitalisation
, 50, 107, 135, 147

Digitalising
, 7

Driving forces
, 72–76, 154–156

Dynamic scenario modelling
, 142–143

Dynamic strategic thinking
, 7–12

Dynamic strategy, dimensions of
, 12–13

clarity
, 13–17

relevance
, 19–21

validity
, 17–19

Economic drivers
, 73

Enemies

adaptiveness
, 133–140

complexity
, 124–133

Environmental drivers
, 73

Environmental onion
, 54

Epistemology
, 4

European Bank
, 71

Evolutionary change
, 92

External circle
, 55

Facebook
, 92

Fast-moving Consumer Goods (FMCG) Company
, 72

Fiction to science to strategy
, 145–148

Financial engineering
, 116

Flight of Flamingos scenario
, 51

Focal question
, 68–72, 154, 156–158, 161–162

Forecast methods
, 32

Forecasting future
, 31–36

Future

desirable
, 37

predicting or forecasting
, 31–36

‘Future-now’ thinking
, 38

Futurism
, 31

Game Theory
, 139–140, 143–144, 154

Garbage in, garbage out issue
, 76

General Electric (GE)
, 42, 116

German Mittelstand
, 53

Global Business Network (GBN)
, 26, 37, 43–44, 58–59, 67–68

Globalisation
, 1–2

Gnoseology
, 4

Go (Japanese board game)
, 146

Gold standard of corporate scenario generation
, 67

Google
, 92

Google Translate
, 146

Group Planning department
, 41

Gulf Stream
, 88

Healthcare Company
, 72

HMD
, 100

Holistic view
, 56

Horizon Planning initiative
, 40

Horizon year
, 70

Human intuition
, 57–58

Humanity
, 3

Humankind
, 1–4

Hyper-connectivity
, 1–2

IBM
, 39, 44, 93, 146

Icarus scenario
, 51

‘Imitation Game, The’
, 146

Impact-uncertainty grid
, 81

Indiana Jones
, 12–13

Industry scenarios
, 71, 85

Infinite-possibility archetype
, 92–93

Innovation
, 36, 49–50, 88, 92, 94, 179

iPhone (Apple)
, 99–100, 122

iPod
, 27

iStockPhoto
, 104

Kodak
, 7–8, 14, 77, 98, 135

KPIs
, 73, 111–112, 139

Lame Duck scenario
, 51

Leaders
, 8

Logic
, 83

Long view
, 13–17, 53

Longitude Prize
, 104

Machine learning
, 32, 146–147, 172

Machine objectivity
, 57–58

Macro environment drivers
, 73

Managers
, 6

Markets
, 7, 10

Med-Tech
, 74, 84, 86

Media
, 123

Media company
, 82–83

Medium circle
, 55

Mental model
, 49, 127–129, 132–133, 135–137, 154, 159, 177, 179–180

‘Middle of the road scenario’
, 59

Modern scenario planning
, 37

Monitor Group
, 62n7

Moore’s law
, 32, 62n18

Motivation
, 48, 117, 120, 133, 140–141

Natural Language Processing
, 104

Netflix
, 8, 24, 27

Neural networks
, 147

Nokia
, 7–8, 99–100

Non-scenario-related forecasting techniques
, 36

Non-strategic goals
, 117

October War (see Yom Kippur War)

‘Official’ future
, 39

Oracle of Delphi
, 31

Organization of the Petroleum Exporting Countries (OPEC)
, 41

Ostrich scenario
, 50

Outside-in thinking
, 28, 44, 54–55, 69, 73

Paralysis
, 94

issue
, 78–79

Pendulum
, 65n57

Perpetual transition
, 93

Personas
, 89–90

Plausibility
, 27–28, 55–56, 108

Plausible futures
, 30, 33, 49, 60

Playing field
, 4, 14–15, 23, 84, 115, 122, 126

Political drivers
, 73

Porter’s five forces
, 28, 116

Possible futures
, 26, 33, 49

Pragmatism
, 19–20

Predicting future
, 31–36

Preferable futures
, 33

‘Principals’
, 6

Probabilistic modified trend scenario
, 67

Probable futures
, 33

Prognosis
, 141–143

Project by project basis
, 40

R&D strategy for global med-tech player
, 84

Ramadan War (see Yom Kippur War)

RAND Corporation
, 37–39, 67

‘Rank and yank’ policies
, 116

Real-time scenario modeling (see also Traditional scenario planning process)
, 153

AI augmenting modelling stage
, 156–159

AI augmenting monitoring stage
, 159–163

AI augmenting research stage
, 154–156

Real-time update
, 160

Relevance (see also Clarity; Validity)
, 13, 19–21

Renaissance scholar
, 12–13

Research and Development Corporation (RAND Corporation)
, 25, 37–39, 67

Revolutionary plots
, 92

Risk
, 54

Saint
, 12–13

Samsung
, 90, 122

Scenario planning (see also Traditional scenario planning process)
, 25–27, 28–30, 37–6, 79

application
, 46–52

in combination with AI
, 144

guidelines for designing scenarios
, 52–61

key principle
, 32

timeline
, 37

usage of management tools
, 46

Scenario process (see also Real-time scenario modeling)
, 103

AI-generated insights
, 103–105

crowdsourcing
, 103–105

democratising
, 105–106

new ways of telling stories
, 106–108

speeding up
, 106

Scenarios
, 23–31, 106

differing from predicting or forecasting future
, 31–36

framework
, 83–84, 158

health
, 161–163

modelling
, 144

monitoring
, 97–100, 159–161

narratives
, 84–93, 158–159

number of
, 58–59

process
, 68

team
, 57–59

thinking
, 14, 21, 33, 36, 39, 55, 67, 75–76, 141–142

Scenarios 5.0
, 103

Second spring
, 147

Self-learning approach
, 146

Shareholders
, 112, 123–124, 172, 177

Shell
, 41–43, 67

approach
, 42

legacy
, 44

Slides
, 107

Social drivers
, 73

Societies
, 7

Sound strategy process
, 97

Stakeholders
, 11, 16, 20, 31, 34, 49–50, 55–56, 47, 69, 75–76, 87, 99, 107–108, 111, 114, 117, 120–121, 139, 171–172

Stanford Research Institute
, 37, 39

Static strategic thinking
, 11–12

Static strategy, trap of
, 5–7

STEEP framework
, 73–74

Stochastic Neural Analog Reinforcement Computer
, 146

Strategic Analytics
, 113

Strategic goals
, 114, 116–118, 132, 137, 153–154, 163

Strategic objective
, 111, 115, 118, 154, 158

Strategic thinking
, 1–2, 116, 137, 165

dynamic
, 7–12

Strategy
, 8, 13–15, 17, 113

advisors
, 123–124

consultants
, 138

fiction to science to
, 145–148

narratives valid in
, 171–172

Structural uncertainties
, 79

Superiority
, 153

Superpower for strategists
, 111

attempts to protect against enemies
, 139–143

from fiction to science to strategy
, 145–148

original strength of strategists
, 116–124

secret of strategic success–and failure
, 114–116

against strongest enemies
, 143–145

true enemies
, 124–138

System Dynamics
, 139–144

Systems thinking
, 88

Technological drivers
, 73

Tectonic-change-plot
, 92

Tesla Roadster
, 118, 121

Time
, 117–119

horizon
, 15, 70

Traditional ‘rule-based’ programming
, 146

Traditional scenario planning process (see also Scenario planning; Real-time scenario modeling)

critical uncertainties
, 76–83

driving forces
, 72–76

focal question
, 68–72

implications and options
, 93–97

monitoring scenarios with cutting-edge technology
, 97–100

scenario framework
, 83–84

scenario narratives
, 84–93

‘Tulip Mania’
, 115

Turbulence
, 26

Turing Test (see ‘Imitation Game, The’)

Uncertainty
, 3, 10, 15, 25–26, 75, 83

critical
, 76–83, 85, 156–158

dimensions of dynamic strategy
, 12–21

dynamic strategic thinking
, 7–12

embracing
, 17–19

secondary
, 82

structural
, 79

trap of static strategy
, 5–7

Unified Planning Machinery system
, 40

Unknowingness
, 80

US Air Force and Douglas Aircraft technology
, 38

Validity (see also Clarity; Relevance)
, 13, 17–19

Volatility
, 80

Wall Street company
, 76

Wall Street Journal
, 35

What-if analyses
, 37

What-if principle
, 47, 86

Wind tunneling
, 160

analogy
, 96

Winners-and-losers plot
, 90

Yellow journalism
, 89

Yom Kippur War
, 41

Zoom-in approach
, 57

Zoom-out approach
, 57