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