Index

Smart Industry – Better Management

ISBN: 978-1-80117-715-3, eISBN: 978-1-80117-712-2

ISSN: 1877-6361

Publication date: 18 July 2022

This content is currently only available as a PDF

Citation

(2022), "Index", Bondarouk, T. and Olivas-Luján, M.R. (Ed.) Smart Industry – Better Management (Advanced Series in Management, Vol. 28), Emerald Publishing Limited, Leeds, pp. 191-198. https://doi.org/10.1108/S1877-636120220000028012

Publisher

:

Emerald Publishing Limited

Copyright © 2022 Tanya Bondarouk and Miguel R. Olivas-Luján. Published by Emerald Publishing Limited


INDEX

A-Scan
, 173, 177, 179

Accounting tools

biomimicry
, 158

eco-costs
, 158

environmental assessment and
, 157–158

IO
, 157

LCA
, 157

MFA
, 157

Action Design Research (ADR)
, 3, 177

Adam optimization
, 180

Additive manufacturing
, 13

Advanced manufacturing
, 1, 6, 8

Advanced robotics
, 9

Agent-based modelling (ABM)
, 155–156

Ambidexterity
, 52

best practices for ambidextrous organization
, 54–57

‘BMW’ as example of sequential ambidexterity
, 56–57

challenges for HRM
, 57–59

HR practices
, 59–63

‘Munich Re’ as example of structural ambidexterity
, 55

‘Trumpf’ as example of contextual ambidexterity
, 55–56

Ambiguity
, 5–6

Ancient Greek Philosophy
, 38–40

Ancients freedoms
, 44

ANOVA
, 114, 116–117

multiple hierarchical regression analysis
, 116–117

Artificial intelligence (AI)
, 2, 9, 20, 54–56, 106, 152

and advantages and disadvantages
, 21–23

benefits
, 22

in business-to-business relationships
, 20–21

contributions to social value of
, 27–30

ethical considerations arising from digital technologies
, 21

incubator ecosystems
, 27

and post-Covid-19 recovery
, 19–21

and related technologies
, 21–22

risks
, 22–23

social value of
, 20, 23

stakeholders in B2B
, 23–27

Assessment
, 154

Augmented reality (AR)
, 13–14

Ausbildung
, 38

Automation
, 52, 57–58

Autonomous mobile vehicles (AMV)
, 12–13

‘Autonomous’ systems
, 12

B-Scans
, 173–174, 177, 179

Big data
, 22, 51–52

analytics
, 9

Bildung Ideal
, 38–40

governmental misuse of
, 42–43

Biomimicry analysis
, 157–158

Blanqui, inventor of term ‘Industrial Revolution’
, 40–41

Blockchain technology
, 9, 22, 152

‘BMW’ as example of sequential ambidexterity
, 56–57

Boston Consulting Group
, 36–38

Brokerage process
, 154

Business customers
, 25–26

AI creates value for and
, 28–29

Business ethics
, 21

Business value
, 182, 185

Business-to-business (B2B)
, 20–21

AI in
, 20–21

efficiency gains in B2B exchanges
, 28

stakeholders in
, 23–27

CAD4COVID software
, 20

California privacy law
, 22–23

Capability-building processes
, 56–57

Capability-shifting processes
, 56–57

Carbon emissions
, 152

Cargo
, 70

Categorical cross-entropy loss
, 180

Central Product Classification (CPC)
, 159–160

Circular business
, 152

Circular economy (CE)
, 2–3, 152

Circularly integration
, 175

Civilization
, 39

Cloud computing
, 9, 22

Cognitive proximity
, 129

Collaboration
, 128–130

Collaborative HR systems
, 110–111, 113

Collaborative robot (cobot)
, 106

in smart industry
, 107

Collective learning process
, 154

Competitors
, 26

Computer anxiety
, 109, 112

Confirmatory factor analysis (CFA)
, 114

Connectivity
, 1

Consistencies
, 114–115

Consolidating less-than-truckload (LTL)
, 71–72

Contextual ambidexterity
, 54–55

‘Trumpf’ as example of
, 55–56

Continuous Professional Development (CPD)
, 59

Control variables
, 113

Convolutional neural networks (CNNs)
, 3, 177

architecture
, 178–179

‘Coopetition’ business model
, 158

Coordination
, 154

Corporate Digital Responsibility (CDR)
, 21

Correlations
, 114–115

Cost-benefit approach
, 2, 30–31

Covid-19 pandemic
, 25

Critical infrastructures
, 171–172

Cross-case analysis
, 138–142

Cross-docking
, 2, 70

Bibliometric network concerning Industry 4. 0
, 95

characteristics
, 84–86

characteristics and Industry 4. 0 adoption
, 89–90

extended search on Industry 4. 0 in cross-docking
, 94, 97, 100

frequency per cross-docking characteristics
, 91

frequency per performance measure setting
, 92

gaps and future research directions for Industry 4. 0 in cross-docking
, 98–100

general overview of cross-docking literature
, 79

inclusion and exclusion criteria
, 77–78

Industry 4. 0 components in cross-docking literature
, 94–98

Industry 4. 0 components in manufacturing and logistics
, 75–76

in Industry 4. 0 era
, 71–76

methodology
, 76–78

operational problems
, 86–89

performance indicators
, 86, 90, 94

performance measures in
, 87–88

research and industry practice
, 72–75

results
, 78–86

scope and review questions
, 76

search terms
, 76–77

solution methods and Industry 4. 0 adoption in cross-docking literature
, 80–84

Customer engagement
, 29

Customer lifetime value (CLV)
, 25

Customer relationship management (CRM)
, 29

improved customer relationship management and customer engagement
, 29

Customized smart products and services
, 28–29

Cyber physical systems
, 1, 9, 51–52, 75–76

Cybersecurity solutions
, 9

Data pre-processing

GprMax simulations
, 179

MI performance
, 180

Data Pro Code
, 27

Data usage
, 11–12

Decision-support framework for IS
, 153

Decision-support tools

industrial symbiosis development
, 153–154

industrial symbiosis support framework
, 161–165

techniques and methodologies for industrial symbiosis development
, 155–161

Deep learning (DL)
, 22, 176

Descriptive statistics
, 114–115

Development
, 43–44

in cross-docking systems
, 70

Digital age
, 54

Digital currency
, 13–14

Digital models
, 13–14

Digital technologies
, 19–20, 22

ethical considerations arising from
, 21

Digital twins
, 14

Digitalization
, 54

of business process
, 51–52

Digitization
, 1

Dimensions of proximity
, 140

Discontinuation
, 154

Discrete cosine transform (DCT)
, 177–178

Distance
, 140

‘Distant capabilities integration’
, 126

Distinctive characteristics
, 9–10

Diversification
, 28

Diversity
, 8

of labels
, 7–8

Downstream smart industry initiatives
, 126

e-HRM
, 52–53

Eco-cluster development
, 154

Eco-costing tool
, 157–158

Educational insights from first industrial revolution
, 38–45

Efficiency gains in B2B exchanges
, 28

Effort expectancy
, 108, 112

Embedding game theory
, 159

Embedding system into GPR analysis process
, 175–176, 182, 185

Employee

engagement
, 28

might resist work automation
, 24

Employment practices
, 54

Enablers
, 9–10

Energy management solutions
, 9

Energy requirements
, 152

Enhanced relationships with suppliers
, 29

Environmental assessment and accounting tools
, 157–158

Ethical considerations arising from digital technologies
, 21

European Association of Institutions in Higher Education (EURASHE)
, 36–37

European Digital Competence Framework for Citizens
, 58–59

European University Association (EUA)
, 36–37

European Waste Catalogue (EWC)
, 159–160

Exclusion criteria
, 77–78

Executives seek efficiency gains associated with AI
, 24

Exploitation
, 52–53

External collaborative partners
, 128

External organizational complexity
, 126

External stakeholders. See also Societal stakeholders
, 25–26

business customers
, 25

competitors
, 26

suppliers and supply chain partners
, 25–26

Facilitators
, 154

Factories of the Future
, 6

Finite-difference time-domain method (FDTD method)
, 179

Firm-related outcomes of AI adoption
, 27–28

AI creates value for business customers and supply chain partners
, 28–29

AI creates value for societal stakeholders
, 29–30

improved health and well-being
, 30

improving work quality and employee engagement
, 28

increased operational productivity and process efficiency
, 27

informed decision-making
, 28

innovation and diversification
, 28

new opportunities in labour market
, 30

reduced environmental impact
, 30

First industrial revolution

Blanqui, inventor of term ‘Industrial Revolution’
, 40–41

concept of liberty and personality education
, 43–44

criticism on governmental misuse of Bildung ideal
, 42–43

educational insights from
, 38–45

liberalism and two conceptions of liberty
, 44–45

Scotland
, 41–42

soft skills education in
, 36–38

Von Humboldt and Bildung Ideal
, 38–40

Food industry
, 152

Foundational technology

foundation technology-oriented smart industry initiatives
, 145

foundational technology-focused initiatives
, 139–142

foundational technology-focused projects
, 142

smart industry project focused on
, 136–138

Fourth industrial revolution (Industry 4. 0)
, 1, 5–7, 36, 51–52, 71

components in manufacturing and logistics
, 75–76

cross-docking characteristics and Industry 4. 0 adoption
, 89–90

cross-docking in
, 71–76

educational insights from first industrial revolution
, 38–45

freedom crucial for personal development
, 45–46

intelligence amplification
, 174–176

paradigm
, 153

soft skills education in first industrial revolution lesson for
, 36–38

solution methods and Industry 4. 0 adoption in cross-docking literature
, 80–84

Freedom crucial for personal development
, 45–46

Front-end technologies, business impact through
, 175

Full truckloads (FTL)
, 71–72

Game theory
, 158–159

Geographic proximity
, 129

Geographical distances
, 126

Geographical information systems (GIS)
, 155, 159

GIS-based techniques
, 161–164

Google
, 30

Governance
, 128–130

governance/coordination
, 142

Government planning
, 154

Governmental bodies
, 26–27

Governmental planning
, 154

GprMax simulations
, 179

Greenhouse gas emissions
, 152

Ground bounce
, 174

Ground Penetrating Radar (GPR)
, 3, 171–172

challenges experienced during utility mapping
, 174

embedding system into GPR analysis process
, 182–183, 185

underground mapping and
, 173–174

visualizing GPR data
, 173

Hard skills
, 37

High-density polyethylene (HDPE)
, 179

Histograms
, 177–178

Horizontal integration
, 175–176

Horizontally integration
, 175

Human resource (HR)

data analytics
, 59

informants
, 2

learning results from interviews
, 60–63

practices
, 59–63

professionals
, 57

Human resource management (HRM)
, 52, 106, 109, 111

challenges for
, 57–59

practical implications for
, 119–120

practices
, 57

system
, 113

systems as moderator
, 110–111

theoretical implications for
, 118–119

Human-robot collaboration (HRC)
, 106, 123

ANOVA
, 116–117

collaborative robots in smart industry
, 107

conceptual framework
, 111

control variables
, 113

data analysis
, 113–114

descriptive statistics, consistencies and correlations
, 114–115

intention to collaborate
, 112–113

limitations and suggestions for research
, 120

measurement models
, 115

measures
, 112–113

method
, 112–114

research design and data collection
, 112

results
, 114–115

role of human resource management
, 109–111

technology acceptance
, 107–109

technology acceptance factors
, 112

theoretical background
, 107–111

Human-robot interaction
, 13

Identification of symbiotic opportunity
, 154

Implementation
, 154

Impracticality
, 5–6

Inclusion criteria
, 77–78

‘Independent’ systems
, 12

Indústria 4. 0
, 1

Industrial Internet
, 6

‘Industrial Revolution’
, 40–41

Industrial symbiosis (IS)
, 152

agent-based modelling
, 155–156

assessment phase
, 164

development
, 153–154

dynamics
, 153–154

embedding SMARTness into IS techniques and methodologies
, 165

environmental assessment and accounting tools
, 157–158

game theory
, 158–159

GIS
, 159

identification phase
, 161–164

implementation
, 164

life cycle
, 154

machine learning
, 160

material passports
, 156–157

material selection tools
, 160–161

monitoring phase
, 164–165

network optimization
, 161

rule-based matching
, 159–160

support framework
, 161–163, 165

techniques and methodologies aligned with IS phases
, 161–165

techniques and methodologies for
, 155–161

‘Industrial Symbiosis Development’
, 153

‘Industrial Symbiosis Support Framework’
, 153

Industry associations and accreditation bodies
, 27

Industry practice, cross-docking research and
, 72–75

Information technology (IT)
, 61, 152

Informed decision-making
, 28

Innovation
, 28

Input-output analysis (IO analysis)
, 157

Institutional proximity
, 129

Instruction Set Architecture (ISA)
, 138

Intellectual capital
, 53–54

Intelligence amplification (IA)
, 3, 171–172, 176

base technologies
, 174–175

business impact through front-end technologies
, 175

industry 4. 0 and
, 174–176

need for integration
, 175–176

symbiotic relationship between humans and technology
, 176

Intelligent system
, 175–177, 182

ML models underlying
, 177–180

Inter-organizational governance mechanism
, 126–127

Internal collaborative partners
, 128

Internet of Things (IoT)
, 9, 22, 51–52, 75, 126, 174–175

Interoperability
, 9

IPAR4. 0 National Technology Initiative
, 1

Key enabling technologies
, 9

Knowledge search, proximity dimensions and
, 129

Kraftvolle
, 39

Labour market, new opportunities in
, 30

Learning rate (LR)
, 180

Levels of Automation (LoA)
, 176

Liberalism
, 44–45

Liberty
, 43–44

two conceptions of
, 44–45

Life cycle analysis (LCA)
, 157

Linear decay rate
, 180

Logistics-related activities
, 70

LoRa Alliance
, 136–137

LoRaWAN standard
, 136–137

Low-power wide-area network (LPWAN)
, 136

Machine learning (ML)
, 3, 9, 21–22, 160, 171–172, 174–175

CNN architecture
, 178–179

data pre-processing
, 179–180

driven intelligent system
, 182–185

early validation comments
, 185

embedding system into GPR analysis process
, 182–183, 185

GprMax simulations
, 179

hyperparameter tuning
, 180

initial model performance
, 180–182

initial performance of DL models
, 181

models underlying intelligent system
, 177–180

performance
, 180–182

rationale
, 177–178

for recognizing material type, shape and size
, 177

system design and instantiation
, 182

training, validation and testing data
, 179–180

for underground mapping
, 176–177

variation performance
, 182

Machine-to-machine communications
, 22

Made in China 2025
, 1, 6

Make in India
, 1, 6

Material flow analysis (MFA)
, 157

Material passports
, 156–157

Material selection tools
, 160–161, 164

Matlab software
, 156

Meaning, understanding of
, 8–14

Measurement models
, 115

Meta-heuristic solutions
, 70–71

Mixed-integer linear programming (MILP)
, 161

Modelling
, 9

Modern freedoms
, 44

Multiple facets
, 9–10

Multiple hierarchical regression analysis
, 116–117

‘Munich Re’ as example of structural ambidexterity
, 55

Netlogo software
, 156

Network optimization
, 161

NGOs
, 26–27

Non-profit technology alliance
, 136–137

Open Radio Area Network (Open RAN)
, 137

Operations technology (OT)
, 60

Optimization

models
, 70

techniques
, 164

Organizational ambidexterity
, 52, 54, 59

Organizational boundaries changes
, 153

Organizational distances
, 126

Organizational proximity
, 129

Organizational support
, 109, 112

Partner decision criteria
, 141

Partner Search Goal
, 139

Perfect electricity conductors (PECs)
, 179

Performance expectancy
, 108, 112

Performance indicators
, 86, 90, 94

Personal development, freedom crucial for
, 45–46

Personality education
, 43–44

Physical internet hubs (PI-hubs)
, 81–84

Pilot facilitation and dissemination
, 154

Platform economy
, 11

Popularity
, 5–6

Post-Covid-19 recovery
, 19–21

Predictive analytics
, 29

Principles of Political Economy
, 43–44

PRISMA method
, 76, 78

Process, smart industry initiatives focused on
, 132–134

Process-oriented smart industry
, 139, 143

Process-oriented smart industry initiatives
, 141

Product, smart industry initiative focused on
, 134–136

Product-focused smart industry initiatives
, 139–143

Product-oriented smart industry initiatives
, 144

Production technologies
, 106

Productivity-based HR system
, 110–111, 113

Proximity
, 128

collaboration, governance and
, 129–130

dimensions
, 128–129

and knowledge search
, 129

Public administration institutions
, 26–27

Public-private partnership (ppp)
, 23

Quantitative methods
, 70

Quantitative solution approaches
, 70

Radio access network (RAN)
, 136

Reconfiguration
, 13, 154

Research and development (R&D)
, 60

RFID tracking
, 98

RISC V
, 138

Robotic Process Automation (RPA)
, 52

Robotics
, 22, 57–58

Rule-based matching
, 159–160

Sales organizations, AI in
, 24–25

Scopus
, 76–77

Scotland, Mill Inspired by Von Humboldt
, 41–42

Self-organization pattern
, 153

Separation
, 13–14

Sequential ambidexterity
, 55

‘BMW’ as example of
, 56–57

Servitization
, 11

Simulation
, 9

Small and medium sized enterprises (SME)
, 6–7

Smart companies
, 54

Smart industry
, 1, 6

analysis
, 142–146

architectures
, 127–128

‘autonomous’ systems
, 12

collaborative robots in
, 107

cross-case analysis
, 138–139, 142

data analysis
, 132

data sources
, 132

data usage
, 11–12

development
, 106

digital capabilities
, 126

diversity of labels
, 7–8

fourth industrial revolution
, 6–7

human-robot interaction
, 13

initiative focused on product
, 134–136

initiatives focused on process
, 132–134

knowledge search
, 129

layers
, 127

management in era of phenomenon
, 15–16

meaning, understanding of
, 8–14

multiple facets
, 9–10

platform economy
, 11

project focused on foundational technology
, 136–138

projects leverage technology
, 126

proximity, collaboration and governance
, 129–130

proximity, search, collaboration and governance
, 128

proximity dimensions
, 128–129

reconfiguration
, 13

research context
, 131

research design
, 130–131

research methods
, 130

separation
, 13–14

servitization
, 11

terminology, understanding of
, 6–8

theoretical background
, 127

traits of different types of relationships
, 130

within-case analysis
, 132

workable phenomenon
, 10–11

Smart manufacturing
, 6, 175

Smart products
, 175

Smart sensors
, 98

Smart supply chain
, 175

Smart technologies
, 153, 155, 165

Smart working
, 175

SMARTness into IS techniques and methodologies
, 165

Social and emotional learning (SEL)
, 36

Social network analysis
, 157

Social proximity
, 129

Social support
, 108–109, 112

Social value of AI
, 20, 22, 27, 30

firm-related outcomes of AI adoption
, 27–28

Societal stakeholders. See also External stakeholders

AI creates value for
, 29–30

and other interest groups
, 26–27

Soft skills
, 37

education in first industrial revolution
, 36–38

Stakeholders in B2B
, 23–27

AI in sales organizations
, 24–25

employees might resist work automation
, 24

executives seek efficiency gains associated with AI
, 24

external stakeholders
, 25–26

macro perspective
, 26–27

stakeholders within organization
, 24–25

State-of-the-art literature research
, 70

Strategic decisions
, 73

Structural ambidexterity
, 54–55

Student-centred learning
, 36

Supervisory control and data acquisition (SCADA)
, 133

Suppliers
, 25–26

enhanced relationships with
, 29

Supply chain distribution
, 70

Supply chain partners
, 25–26

AI creates value for and
, 28–29

Support Vector Machines (SVMs)
, 177

Sustainability of IS
, 158

Sustainable Development Goals (SDGs)
, 29

System design and instantiation
, 182

Systematic literature review
, 70–71

Tactical decisions
, 73–74

‘Techniques and Methodologies for Industrial Symbiosis Development’
, 153

Technological disruption
, 57–58

Technological heterogeneity
, 141

Technology
, 7

Technology acceptance
, 107–109

computer anxiety
, 109

effort expectancy
, 108

factors
, 107–108, 112

organizational support
, 109

performance expectancy
, 108

social support
, 108–109

trust
, 108

Telecom Infra Project (TIP)
, 137–138

Terminology, understanding of
, 6–8

Theory of Planned Behaviour (TPB)
, 108–109

3D printing
, 9–10, 13

Time of arrival (ToA)
, 173

Traceability
, 12

Traditional mechanical engineering practices
, 55–56

Truck-to-door assignment
, 74

Truck-to-door sequencing
, 74

Truck-to-door sequencing and scheduling
, 74

‘Trumpf’ as example of contextual ambidexterity
, 55–56

Trust
, 108, 112

Unified Theory of Acceptance and Use of Technology (UTAUT)
, 107–108

Upstream industry initiatives
, 126

Utility mapping

effectiveness of variations
, 185–186

explaining performance differences between CNNs
, 185

industry 4. 0 and intelligence amplification
, 174–176

machine learning approaches for underground mapping
, 176–177

ML driven intelligent system
, 182–185

ML models underlying intelligent system
, 177–180

ML performance
, 180–182

origins of excavation damage
, 172–173

research methodology
, 177

study limitations
, 186–187

underground mapping and GPR
, 173–174

Vertically integration
, 175

Virtual reality (VR)
, 13–14

Visualization technologies
, 9

‘Vocational training’
, 38

Von Humboldt
, 38–40

Web of Science
, 76–77

Well-being
, 30

Within-case analysis
, 132

Work

employees might resist work automation
, 24

practices
, 54

quality improvement
, 28

Workable phenomenon
, 10–11

World Economic Forum (WEF)
, 36