{
"id": "2020-blayone-preparedworkindustry40-NEGAELZY",
"title": "Prepared for work in Industry 4.0? Modelling the target activity system and five dimensions of worker readiness",
"authors": [
"Blayone, Todd J.B.",
"vanOostveen, Roland"
],
"year": "2020",
"abstract": "Within Industry 4.0 research, the spotlight shines on technological and organisational challenges. This study shifts the focus to worker readiness, beginning with an analysis of twenty-three models to establish the state of research. Findings demonstrate that existing models are mostly early-stage proposals addressing competences featured in mainstream 21st-century and digital-competence frameworks. Worker-level factors explicitly aligned with emerging cyber-physical systems receive little attention. To construct a worker-readiness model calibrated to the needs of Industry 4.0, the authors devised a research procedure based on a two-phase integrative review of 135 publications. Firstly, they deployed an activity-system apparatus to produce a structured description of the target environment. Secondly, major worker competence groupings, aligned with this target, were extracted, tagged and reduced to five dimensions. The resulting model consolidates prior research and introduces two original competence groupings addressing human-machine partnering and decision-making in Industry 4.0. This study is a foundational step by the Educational Informatics Lab, Ontario Tech University, Canada, toward deploying a global online profile tool for generating, analysing and aggregating worker readiness profiles. This cross-disciplinary project will help researchers, educators, corporate trainers, human resource managers, policymakers, and systems designers more effectively diagnose the readiness of workers for Industry 4.0.",
"keywords": [],
"biblio": {
"type": "article-journal",
"container_title": "International Journal of Computer Integrated Manufacturing",
"volume": "34",
"issue": "1",
"pages": "1-19",
"doi": "10.1080/0951192X.2020.1836677",
"url": "",
"publisher": "",
"publisher_place": "",
"issn": "",
"isbn": ""
},
"sections": [
{
"label": "abstract",
"heading": "Abstract",
"level": 1,
"text": "Within Industry 4.0 research, the spotlight shines on technological and organisational challenges. This study shifts the focus to worker readiness, beginning with an analysis of twenty-three models to establish the state of research. Findings demonstrate that existing models are mostly early-stage proposals addressing competences featured in mainstream 21[st] -century and digital-competence frameworks. Worker-level factors explicitly aligned with emerging cyber-physical systems receive little attention. To construct a worker-readiness model calibrated to the needs of Industry 4.0, the authors devised a research procedure based on a two-phase integrative review of 135 publications. Firstly, they deployed an activity-system apparatus to produce a structured description of the target environment. Secondly, major worker competence groupings, aligned with this target, were extracted, tagged and reduced to five dimensions. The resulting model consolidates prior research and introduces two original competence groupings addressing human-machine partnering and decision-making in Industry 4.0. This study is a foundational step by the Educational Informatics Lab, Ontario Tech University, Canada, toward deploying a global online profile tool for generating, analysing and aggregating worker readiness profiles. This cross-disciplinary project will help researchers, educators, corporate trainers, human resource managers, policymakers, and systems designers more effectively diagnose the readiness of workers for Industry 4.0. \n\n## **ARTICLE HISTORY** \n\nReceived 10 June 2019 Accepted 11 October 2020 \n\n## **KEYWORDS** \n\nIndustry 4.0; work environment; worker readiness; worker competences; readiness model; digitalised work",
"page_span": {
"start": 1,
"end": 1
},
"confidence": "high"
},
{
"label": "introduction",
"heading": "1. Introduction",
"level": 1,
"text": "The digitalisation of industry is advancing steadily. Catalysed by international programs like Industry 4.0, academics, governments, and private companies are collaborating to reinvent manufacturing. Industry 4.0 is an established global innovation program aimed at making manufacturing facilities more intelligent, efficient and flexible (Orellana and Torres 2019). However, there are different views of Industry 4.0 among small- and medium-sized enterprises (Da Silva et al. 2019) and ongoing challenges producing a roadmap for its full realisation (Liao et al. 2017). Indeed, leading German companies are still working toward advanced stages of maturity (Bittighofer et al. 2018). Given the complexity and scope of industrial digitalisation, much of the academic research focuses on technological and organisational problems. Addressing ‘human factors,’ and in particular, conceptualising and measuring human readiness for digitalised work receives less attention, and remains an early-stage project (Shahlaei, Rangraz, and Stenmark 2017; Peruzzini, Grandi, and Pellicciari 2020). \n\nNevertheless, this project is crucial to the success of Industry 4.0 because aside from a few ‘dark factory’ scenarios (Oztemel and Gursev 2018), humans are considered more adaptive than machine entities and vital to future production (Leineweber et al. 2018; Ghobakhloo 2018). \n\nTo date, researchers have studied work transformations in digitalised industries from several perspectives. For example, economists have assessed the impact of automation on jobs (Frey and Osborne 2017; Autor 2015), and industrial management specialists have proposed strategies for realigning organisational resources, including personnel, with digitalised manufacturing models (Mittal et al. 2018; Pessl, Sabrina Romina, and Mayer 2017). At the worker level of analysis, the literature offers seminal case studies (Johansson 2017), industry reports (Canadian Apprenticeship Forum 2018), conceptual explorations (Karacay 2018; Romero et al. 2016a) and empirical analyses (Richert 2018). Most importantly, researchers have begun producing new competence models as foundations for Industry 4.0 worker development (Erol et al. 2016; Galaske et al. 2017). With few \n\nFaculty of Education, Ontario Tech University, Oshawa, Canada \n\n**CONTACT** Todd J. B. Blayone todd.blayone@ontariotechu.net © 2020 Informa UK Limited, trading as Taylor & Francis Group \n\nexceptions (van Deursen and Mossberger 2018; Blayone et al. 2020), however, specialised digital-competence researchers have not explored the ability requirements of digitalised industrial work. Instead, they have investigated mainstream digital competences of students and citizens from operator-tool perspectives misaligned with intelligent systems and new forms of human-machine partnering (van Deursen, Helsper, and Eynon 2016; Ferrari 2013; Eshet 2012; Blayone et al. 2018c). \n\nThis study bridges this divide and contributes to the advancement of Industry 4.0 readiness research at the worker level. It begins by establishing the state of research through a systematic review of 23 prior readiness models. Then, a new model is constructed from the literature via a two-stage research synthesis to consolidate previous efforts and address significant research gaps. In stage one, the salient characteristics of Industry 4.0 work environments are modelled as an activity-system. In stage two, major competence groupings aligned with these systems are synthesised, and an original five-dimensional model of worker readiness for Industry 4.0 is proposed. This model is a necessary _first step_ by the Educational Informatics Lab (EILAB), Ontario Tech University, Canada toward implementing an online application for generating and aggregating Industry 4.0 readiness profiles of individuals around the globe, supporting self-diagnosis and ongoing research to inform higher education, employee (re)training, human resource management and policymaking. Having already implemented a global readiness application for measuring the digital competences of students, teachers and knowledge professionals (Blayone 2018; Blayone et al. 2018a, 2018b, 2018c), this project pivots to the development needs of industrial workers and the requirements of digitalised manufacturing.",
"page_span": {
"start": 1,
"end": 2
},
"confidence": "high"
},
{
"label": "other",
"heading": "2. Establishing the state of research",
"level": 1,
"text": "At the individual and group level, readiness research has roots in learning psychology and technologysystems development (Thorndike 1932; Sullivan 1970). The common goal is to identify and measure _factors_ enabling successful human functioning within a _target_ context. Factors of interest most often include knowledge, skills, attitudes (KSAs) and related dispositions, which may be grouped as competences (Hoffmann 1999). These are ability complexes that individuals can develop through experience and \n\nlearning. Situational, cultural and personality factors, though less widely studied, may also be considered, particularly as mediating and moderating variables. \n\nTwenty-three readiness models addressing the needs of workers in Industry 4.0 were selected and reviewed to establish the state of research. They are presented in two groups, featuring thirteen models developed from an organisational perspective, which address workers as a collective entity (e.g. ‘workforce’ or ‘human resources’), and ten from a worker-level perspective. Each model was reduced to a tabular data set to identify common foci, key differences and research gaps. This data set included specified model type, derivation methodology, conceptual readiness structures, human readiness factors, and available instrumentation. The availability of instrumentation was used as a general indicator of a model’s maturity because successful operationalisation via the development and validation of a selfreport or expert-based assessment tool requires several stages of research beyond initial theorisation.",
"page_span": {
"start": 2,
"end": 2
},
"confidence": "high"
},
{
"label": "other",
"heading": "2.1. Organisation-level readiness models",
"level": 2,
"text": "Key findings from thirteen organisation-level models, shown in Table 1, are as follows. Firstly, these models use ‘readiness’ and ‘maturity’ interchangeably as descriptors, even though some studies distinguish between preparation for an initial implementation (readiness) and subsequent development (maturity) (Akdil, Ustundag, and Cevikcan 2018; Botha 2018). Secondly, about half of the models were based on small-scale literature reviews and first-hand theorisations. Others incorporated organisational surveys, interviews with managers, expert processes and assessment frameworks adapted from software development and IT. Thirdly, conceptual structures are diverse, but they position the performance capacities of workers as a critical facet or sub-facet of organisational preparedness. Fourthly, although most models address human readiness generally (1, 3–8 and 13), the rest mention specific worker-level factors, including technology/IT skills (9 and 10), social competences (11 and 12) and intrapersonal dispositions (2). Finally, six studies (1, 3, 9, 11,12 and 13) have produced instrumentation in the form of a survey instrument, checklist or interview guide, but reliability and validity testing is either not reported or planned as nextstage research. \n\n|#
Source
Model Type
Method
Conceptual Structure
Human Factor(s)
Instrument?|1
(Akdil, Ustundag,
and Cevikcan
2018)
Industry 4.0 Maturity
Small-scale literature review
Three Aspects: Smart Products and
Services; Smart Business Processes;
Strategy and Organisation. Four
Maturity Levels: Absence; Existence;
Survival; Maturity
General: Human resources
Yes. Validity and reliability not
addressed
2
(Botha2018)
Future Readiness
Theorised on a future-thinking
framework
Three Aspects: Technology; Behaviour;
Future thinking.9Five to ten
readiness levels in each dimension.)
Specifc: Human-machine harmony;
Liberated approach to work; Willingness
to re-skill; Embrace sharing culture
No. Conceptual structure validated
by surveying experts
3
(Canetta, Barni, and
Montini2018)
Digitalisation maturity,
with a focus on
workers and working
conditions
Based on a comparative review of 27
models and interviews
Four Aspects: Processes; Impact;
Technology and Human Resources;
Technological Process Assessment.
Four Maturity Levels: Absence;
Novice; Intermediate; Expert
General: Human resource requirement;,
Changes in worker skills owing to the
digitalisation
Yes. Five-part questionnaire (36
items). Validity and reliability not
addressed
4
(De Carolis et al.
2017)
Digital Readiness
Assessment Maturity
Model (DREAMY)
Capability Maturity Model Integration
framework. Literature review and
expert input
Four Aspects: Process; Monitoring and
Control; Technology; Organisation.
Five Maturity Levels: Initial;
Managed; Defned; Integrated and
Interoperable; Digital-oriented
General: Worker skills to be added to this
modular framework (De Carolis et al.
2017)
Unknown. Mentioned but neither
described nor provided
5
(Ganzarain and
Errasti2016)
Industry 4.0 maturity
model for business
diversifcation
towards Industry 4.0.
Theoretical proposal without a formal
methodology
Three Stages: Envision; Enable; Enact.
Five Maturity Levels: Initial;
Managed; Defned; Transform;
Detailed Business Model
General: Employeetraining
No. Visual model only
6
(Geissbauer, Vedso,
and Schrauf2015)
PcW Maturity Model for
manufacturing
managers to assess
Industry 4.0 maturity.
Based on a survey involving 2000
+ respondents from nine industrial
sectors in 26 countries
Seven Aspects: Digital Business
Models; Digitisation Oferings; Data
and Analytics; Agile IT
Infrastructure; Compliance; Security,
Legal and Tax; Organisation
(Including Employees and Culture).
Four Maturity Levels: Digital Novice,
Vertical Integrator, Horizontal
Collaborator, Digital Champion
General: Worker capacities; Organisation’s
digital culture
No. Although based on survey
research, instrument not
published
7
(Gökalp, Şener, and
Eren2017)
SPICE Maturity Model to
assess Industry 4.0
maturity.
Small-scale review of models. Software
Process Improvement and
Capability Determination
framework.
Five Aspects: Asset Management; Data
governance; Application
Management; Process
Transformation, Organisational
Alignment. Six Maturity Stages:
Incomplete; Performed; Managed;
Established; Predictable; Optimising
General: Skills of IT personnel; Other
human resource requirements for
Industry 4.0 transformation
No
8
(Leineweber et al.
2018)
Industry 4.0 migration
model to help
manufacturing
production
environments
mature.
Based on defnitional analysis from the
literature, from a socio-technical
perspective
Three Aspects: Technological (machine
data acquisition, maintenance, data
evaluation); Organisational
(security, personnel deployment
and capacity data); Personnel
(expertise, development/
qualifcation). Four to Six Maturity
Levels.
General: Worker training
No. Accessible instrument and online
application is a project goal|(_Continued_)|\n|---|---|---|\n\n|#
Source
Model Type
Method
Conceptual Structure
Human Factor(s)
Instrument?|9
(Lichtblau et al.
2015)
IMPULS model measures
the willingness and
capacity of
companies to
implement Industry
4.0
Mixed methodology (literature review,
expert workshops and survey data)
Five Aspects: Strategy and
Organisation; Smart Factory; Smart
Operations; Smart Products; Data-
driven Services; Employees. Six
Readiness Levels: Outsider;
Beginner; Intermediate;
Experienced; Expert; Top Performer
Specifc: Worker skills; Willingness to learn
IT skills; Competent at implementing
assistance systems
Yes. Industry 4.0 Online Readiness
Check. Validity and reliability not
addressed
10 (Samaranayake,
Ramanathan, and
Laosirihongthong
2017)
Technological readiness
model organises and
weights factors for
Industry 4.0
Small-scale literature review. Factors
weighted via Q-Sort and expert
analytical process
Six_Ranked_Aspects: Human
Technology Skills; Device and
Systems Interconnectivity; Big-Data
Management; Data Sharing
Between and Within Organisations;
Internet System Development; Data
security
Specifc: Technology expertise; Knowledge,
skills, abilities and motivations of staf,
data scientists, and support staf
No
11 (Schuh et al.2017)
Industry 4.0 Maturity
Index for assessing
a company’s Industry
4.0 maturity
stage and next steps
Expert consultations workshops and
case studies. Instrument Validated
through applications at companies
Five Aspects: Resources; Information
Systems; Organisational Structure;
Culture. Five Areas: Development;
Production; Logistics; Services;
Marketing/Sales. Six Maturity
Stages: Computerisation;
Connectivity; Visibility;
Transparency; Predictive Capacity;
Adaptability
Specifc: Digital-communication abilities of
humans and machines; Worker
capacities: Openness to change and
social collaboration
Yes. A sample item provided only.
Stich, Gudergan, and Zeller (2018)
note this instrument has 600
items. Validity and reliability not
addressed
12 (Ganzarain and
Errasti2016)
Industry 4.0 Maturity
Model
Mixed-methods. Expert interviews,
literature review of 72 items and
concept-mapping
Nine Aspects: Strategy; Leadership;
Customers; Products; Operations;
Culture; People; Governance;
Technology. Five Maturity Levels:
Measured on a 5-point scale.
Specifc: Collaboration skills; ICT
competences; Employee openness and
autonomy; Mobile technology
competences
Yes, but not provided. Piloted via
two Austrian studies. Validity and
reliability not addressed.
13 (Scremin et al.2018)
Adoption Maturity
Model (AMM)
assessing the
maturity level of
Industry 4.0
companies
Literature review, structured interviews
of managers, case studies, design of
maturity thresholds and indicators,
and development of archetype
matrix
Eight Aspects: Business Strategy;
Technology Strategy; Networking
and Integration; Infrastructure;
Analytical Skills; Absorptive
Capacity; Benefts of Adoption;
Impact on Efciency. Maturity
Levels: Assessed by researchers via
mixed-methods analysis of
interview responses.
General: Human factors addressed as
‘absorptive capacity’ of an organisation;
Availability of employee training and
awareness of skill requirements for
using systems
Interview guide is published.
Framework validated through ten
case studies. Further validation is
planned.|\n|---|---|",
"page_span": {
"start": 2,
"end": 5
},
"confidence": "high"
},
{
"label": "other",
"heading": "2.2. Worker-level readiness models",
"level": 2,
"text": "The ten reviewed worker-level models are shown in Table 2. At this level of analysis, most models identify detailed complexes of readiness factors derived from literature reviews, roadmaps, survey data and expert interviews. Several models incorporate a generic competence framework addressing technical, methodological, social and personal abilities (Hecklau et al. 2016). Overall, the proposed factor groupings feature social/collaboration competences (2, 3, 5, 6, 8, 9, 10), technical/ICT knowledge and skills (2, 5, 6, 8, 10) and cognitive flexibility (1, 3, 5, 6, 8, 10). Less prominent groupings include intrapersonal competences (3, 8, 9 and 10) and intercultural skills (8 and 9). Departing from predefined factor structures, three models (4, 5 and 7) theorise worker readiness by envisioning new job types, professional archetypes and situational characteristics tied to work dynamics of Industry 4.0 environments. Only two models (5 and 8) report the availability of measurement instrumentation. Although these instruments appear to be sophisticated expert-assessment tools, information is not provided about validation and reliability testing.",
"page_span": {
"start": 5,
"end": 5
},
"confidence": "high"
},
{
"label": "other",
"heading": "2.3. Patterns, limitations and gaps",
"level": 2,
"text": "On aggregate, the reviewed models present a diversity of conceptual structures and constituent readiness factors. At both the organisational and worker levels of analysis, technological and social/communication skills are most prominent. At the worker level, personal flexibility is also emphasised, but elaborated in several ways (e.g. motivation to learn and openness to change). Methodologically, several proposals are grounded tentatively on ad hoc theorisations or small-scale literature reviews, and few provide a systematic description of the target. Also, two conceptual gaps emerge. Firstly, technology-focused dispositional subfactors (e.g. trust and acceptance/enthusiasm) receive little attention. Secondly, competences addressing readiness for new forms of human-computer interaction precipitated by Industry 4.0 automation and augmentation technologies are mostly absent. Here, researchers may be drawing too heavily from mainstream competence discourses that have not adapted to the novel dynamics of humanmachine partnering (Grudin 2017). Finally, the meagre availability of valid and reliable instrumentation at both the organisational and worker-levels of analysis suggests these models are mostly early-stage proposals. Given \n\nthese findings, an original modelling procedure was initiated to consolidate prior research, address conceptual gaps and establish a theoretical foundation for implementing a worker readiness profiling application.",
"page_span": {
"start": 5,
"end": 5
},
"confidence": "high"
},
{
"label": "methods",
"heading": "3. Purpose and method",
"level": 1,
"text": "A two-phase modelling procedure based on an integrative literature analysis and synthesis was conducted. As shown in Figure 1, phase one deployed an activitysystem apparatus (Engeström 2015) for deriving a structured description of the target work environment. Activity theory was chosen because it has demonstrated usefulness over many decades for modelling workplace transformations (Virkkunen and Newnham 2013; Engeström 2005) and the socio-technical dynamics of human-computer interaction (Nardi 1996; Kaptelinin and Nardi 2012). Activity theory originated in early Soviet psychology of the 1920s with Vygotsky’s (1978) triangular model in which _tools and technologies_ were positioned as mediators between _subjects_ and their _objects_ . This foundational social-psychological structure was elaborated into a system of collective (labour) activity by Leontiev (1977, 2005, 2006) in which _rules, other actors_ (e.g. members of a workgroup) and _divisions of labour_ were added. Finally, the Finnish scholar Engeström consolidated these elements visually in a (multi-triangle) activity system apparatus, which has been used extensively in several scientific disciplines for abstracting, explaining, positioning and contextualising work activity (Bligh and Flood 2017). \n\nPhase two focused on coding, extracting and organising KSAs into thematic clusters aligned with the target. This phase relied on a multidisciplinary base of social science, human factors and Industry 4.0 literature, which addressed worker/operator competences supporting optimal functioning in digitalised work environments. \n\nFor both phases, English-language literature was identified and analysed via an emergent process. An initial set was established by querying Google Scholar and Web of Science using predefined keywords (e.g. [readiness OR skills OR competence] AND [manufacturing OR digitalisation OR Industry 4.0]). As items were reviewed, additional phrases (e.g. cyber-physical systems; Industry 4.0 human factors; Operator 4.0) were gathered to extend the set. Published articles and highquality conference papers were preferred, but other publication types were included if they provided \n\n|#
Source
Model Type
Method
Factor Types
Factor Clusters
Instrument?|1
(Adolph, Tisch, and
Metternich2014)
Workforce competences and
learning for production
efciencies
Workforce competences derived from
production challenges and megatrends via
small-scale literature review
Competences
FlexibilityChangeabilityResource
efciencyProcess efciency
No
2
(Dworschak and Zaiser
2014)
Competences of workers in
manufacturing contexts of
cyber-physical systems.
Skills drawn from technology forecasts and
organisation structures. Tool scenario:
humans contribute to decisions; Automation
scenario: IT makes decisions
SkillsKnowledge
TechnicalSocial and Collaboration; Deep
Operational and Business Informational;IT
and Engineering knowledge
No
3
(Erol et al.2016)
Taxonomy of competences of
Industry 4.0 workers and
scenario-based learning-
factory (TU Wien)
Based on a small-scale literature review of
competences for digitalised production, and
experience developing a learning factory
Competences
Personal (refect, act autonomously, learn;
trust); Social (communicate, cooperate, use
social media); Action (interdisciplinarity,
manage parallel structures); Domain (model,
analyse)
No
4
(Fareri et al.2018)
Efects of Industry 4.0 on
business value chains, and
the competences of worker
job profles
Modifed Porter value-chain model to map
business functions and select literature.
Automated text mining to analyse literature.
Matrix created to cross-reference business
functions and Industry 4.0 worker profle
archetypes
Archetypes
Data Architect (all depts); IT Architect (logistics
and IT); Geek (management, facilities);
Investigator (facilities, QC); Perfectionist
(facilities, QC, accounting); Prophet (IT,
production); Strategist (marketing,
management, R&D)
No
5
(Galaske et al.2017)
Toolbox Workforce
Management 4.0. Readiness
of businesses, workforce
competences and work
conditions
Small-scale literature review. Theorised using
Guideline Industrie 4.0 and Generic
Procedure Model for SMEs. Matrix with
application felds as vertical elements and
development stages as horizontal elements
Skills
CompetencesEnvironmental
variables
Hard Skills: IT, business and manufacturing;Soft
Skills: personal, social and methodical
competence
Environment: assistance systems, human-
machine interaction, decision support,
security/privacy, organisational fexibility
Graphical model to guide
interviews and
assessment.
6
(Gehrke et al.2015)
Skills and qualifcationsfor
future manufacturing
workers
Industry 4.0 modelled via the experience of 10
engineers. Contextual factors: cooperation,
working environment; Organisation and
structure; Tools and technologies; Tasks:
from physical objects to information, models
and simulations
SkillsQualifcations
(organised by ‘Must have’,
‘Should have’ and ‘Could
have’)
Technical: Must have IT, data processing,
organisational understanding; Should have
knowledge management skills; Could have
programming abilities;
Personal: Must have adaptability and social
skills; Should have trust in technologies and
a learning mindset; No could haves
No
7
(Hartmann and
Bovenschulte2013)
Proposal for deriving skill
needs for Industry 4.0 from
technology roadmaps
Skills derived from road-mapping expert input.
Roadmaps address equipment; robotics and
automation; human-machine collaboration
and bio-engineering
Skills (adaptive to business
contexts)Roles
Organisational Scenarios: yield diferent skill
needs; Roles: Industrial ICT Specialist,
Industrial Cognitive Scientist, Automation
Bionics Specialists
No
8
(Hecklau et al.2016)
Holistic human resource
management for Industry
4.0
Employee competences derived from Industry
4.0 drivers/challenges identifed via
a literature review. Challenges organised in
fve categories: political, economic, social,
technical, environmental and legal
Competences
Technical: media, coding, security;
Methodological: creativity, entrepreneurial
thinking, problem-solving;
Social: intercultural, communication,
teamwork, negotiation; Personal: fexibility,
ambiguity tolerance, learning
Radar charts for
competence
visualisation
9
(Mittelmann2018)
Competences for Work 4.0,
success factors for
businesses of the future.
Small-scale literature review. Characteristics of
Work 4.0 identifed as digitalisation,
collaboration with cyber-systems, fexible,
work independent of location and time,
complex non-routine tasks, and diverse
teams
Competences
Intrapersonal: critical thinking, sense-making,
adaptive thinking, transdisciplinarity, self-
direction; Interpersonal: communication,
virtual collaboration, social and intercultural
intelligence; ICT: computational thinking,
social media, Information security
No
10 (Mourtzis2018)
Skills and competences for
Industry 4.0
Competences derived from literature review of
Industry 4.0 technology descriptions.
Proposal for Education 4.0 based on
teaching factories
Knowledge
Skills
Technical: technological, learning, process
understanding; Methodological: creativity,
problem-solving, analytical, research; Social:
communication, cooperation, networking;
Personal: autonomy, responsibility,
organisational, fexibility
No|\n|---|---|\n\noriginal perspectives and evidence-based analyses of Industry 4.0 work environments or worker competences. Literature was admitted from several academic domains and national contexts without prejudice. (Within the coded literature, 35% of the first authors reported Germany or Sweden as their host nation, with 36 nations represented.) Host journal domains included engineering, business, information technology, computer science, psychology, ergonomics and education. Most items dated from 2015 (115). Searching stopped when the thematic extraction/coding of activity-system characteristics and worker competences (in Excel) reached saturation (Mason 2010). The final (135-item) data set (maintained in Endnote X9) included 63 articles, 44 conference papers, 12 chapters and 16 reports. A semantic analysis of abstracts and keywords (conducted in NVivo 12) highlighted both context-specific (e.g. Industry, manufacturing, technology, systems and digitising) and worker-related items (e.g. humans, work, skills and competence) among the top 100 lexemes, which is consistent with the study’s twin focus. _To achieve economy in reporting, references provided in this text are selective and do not represent the full data set._ \n\nnational context, industrial sector, management culture and level of technological maturity (Da Silva et al. 2019).",
"page_span": {
"start": 5,
"end": 7
},
"confidence": "high"
},
{
"label": "other",
"heading": "4.1. Object(s)",
"level": 2,
"text": "The object of an activity system incorporates motivating objectives and desired outcomes. A principal object of Industry 4.0 is rooted in its conception. Industry 4.0 was announced as a strategic initiative to bolster the German manufacturing sector and national economy (Xu, Xu, and Li 2018). Other nations followed with similar digitalisation initiatives to maintain global competitiveness. For example, South Korea introduced ‘manufacturing innovation 3.0ʹ, and Japan launched ‘Society 5.0ʹ (Oztemel and Gursev 2018). Beyond strengthening economies, Industry 4.0 is also widely regarded as a pathway towards greater environmental sustainability (Chen, Olhager, and Tang 2014; Jackson 2016; Sutherland et al. 2016) and social innovation (Morrar, Arman, and Mousa 2017), which function as secondary objects. A third object emphasises technological innovation as a pathway to a new industrial age (Zhong et al. 2017). Of course, objects are always shaped by, and formulated within, organisational and cultural contexts of activity.",
"page_span": {
"start": 7,
"end": 7
},
"confidence": "high"
},
{
"label": "other",
"heading": "4.2. The worker",
"level": 2,
"text": "A structured multidimensional description of Industry 4.0 work environments was achieved by addressing each of the six activity-system elements (shown in Figure 1). This approach resulted in a generic target model, and it is expected that the characteristics of any particular industrial workplace will be shaped by \n\nAs a system entity, the literature positions Industry 4.0 workers as adaptable, ‘informationalised’ and hybridised. Technologically complex production environments supporting customised products in small batch sizes (Järvenpää, Lanz, and Lammervo 2016) requires \n\n**Figure 1.** Two-phase deductive literature analysis and modelling approach. \n\nworkers who are _adaptable_ to reconfiguration and emergent problems. The _informationalised worker_ calls attention to humans as data producers and processors. As producers, workers routinely input data (e.g. task logging) or generate physiological and environmental information with wearable sensors. As information processors, they are required to generate actionable intelligence from multiple data streams (Dworschak and Zaiser 2014). The _hybridised worker_ emerges through advanced (e.g. braincomputer) interfaces and augmentation systems (Zhong et al. 2017; Mourtzis 2018). For example, SOPHOS-MS incorporates VR and natural-languageprocessing, enabling operators to receive expert knowledge on-demand through a question-andanswer approach (Longo, Nicoletti, and Padovano 2017). Similarly, the Operator 4.0 paradigm envisions the dynamic augmentation of a worker’s physical, sensorial and cognitive capacities (Romero et al. 2016a; Peruzzini, Grandi, and Pellicciari 2020; Romero et al. 2016b) to serve several manufacturing functions (Ruppert et al. 2018). Other hybridisation models, such as intelligent-adaptive assistance, have also been proposed (Wilkesmann and Wilkesmann 2018).",
"page_span": {
"start": 7,
"end": 8
},
"confidence": "high"
},
{
"label": "other",
"heading": "4.3. Technologies",
"level": 2,
"text": "Companies striving toward Industry 4.0 seek to exploit the full capabilities of cyber-physical systems (Choi et al. 2017). Lists of enabling technologies feature the Internet of Things, cybersecurity, big-data analytics, cloud computing, additive manufacturing, augmented reality and advanced interfaces (Rüßmann et al. 2015; Geissbauer, Vedso, and Schrauf 2015). Going beyond lists, some researchers have developed holistic frameworks. For example, Frank, Dalenogare, and Ayala (2019) distinguish between ‘front-end technologies,’ supporting operational and market needs, and ‘base technologies,’ providing connectivity and intelligence. Base technologies include IoT, Cloud computing and big data, enabling advanced applications like digital modelling (‘twinning’) of machines and factory environments to automate decision making and anticipate maintenance requirements. Front-end technologies include four ‘smart’ technological clusters related to manufacturing, products, supply chain and working. From a greater level of abstraction, Qin, Liu, and Grosvenor (2016) reduce Industry 4.0 technologies to _interoperability_ and _consciousness_ . Interoperability includes technologies of \n\ncommunication, flexibility, real-time responsibility and customizability. Consciousness emerges through those technologies facilitating predictive intelligence, decision making, self-awareness, self-optimisation and selfconfiguration.",
"page_span": {
"start": 8,
"end": 8
},
"confidence": "high"
},
{
"label": "other",
"heading": "4.4. Workgroups",
"level": 2,
"text": "Workgroup configurations in Industry 4.0 are described as hybridised, diverse, geographically distributed and dynamic (Mittelmann 2018). Patterns of interaction, ranging from coexistence to collaboration, and incorporating human and machine entities (often situated in different physical locations), will be deployed strategically and contextually to address emergent operational opportunities and challenges (Galaske et al. 2017). Workgroups are expected to favour network structures that flatten hierarchy and promote interaction across traditional departmental boundaries (Schuh et al. 2014). Owing to the twin dynamics of vertical and horizontal systems integration, communication requirements within and between workgroups will intensify (Schuh et al. 2014). Within these well-connected network structures, managers and other human authorities will increasingly share decision-making control with nonhuman actants (Fischer and Pöhler 2018).",
"page_span": {
"start": 8,
"end": 8
},
"confidence": "high"
},
{
"label": "other",
"heading": "4.5. Rules and culture",
"level": 2,
"text": "Worker autonomy and decentralised decision-making are leading themes in this category (Fischer and Pöhler 2018; Karacay 2018; Galaske et al. 2017). Production environments will achieve optimal performance where front-line operators, supported by expert systems, have the resources to address problems directly (Järvenpää, Lanz, and Lammervo 2016; Shamim et al. 2016). Realising the advantages of distributed problem solving, however, can be challenging in contexts where centralised control, associated with scale economics, is well established (Frank, Dalenogare, and Ayala 2019). Two less emphasised themes address service- and technology-oriented subcultures (Herterich, Uebernickel, and Brenner 2015; Soulé and Warrick 2015). A service-oriented subculture is vital to supporting data-driven offerings requiring close relationships with customers throughout a product’s lifespan (Ibarra, Ganzarain, and Igartua 2018). A technologyoriented subculture promotes enthusiasm for learning new systems, applications and interfaces (Richter et al. \n\n2017). Some case studies demonstrate that technology enthusiasm can be cultivated effectively ‘from below.’ For example, Johansson (2017) observed a young machine worker with computer gaming experience successfully mentoring mature operators (with tremendous domain knowledge) as they struggled to use new frontline, computer systems.",
"page_span": {
"start": 8,
"end": 9
},
"confidence": "high"
},
{
"label": "other",
"heading": "4.6. Division of labour",
"level": 2,
"text": "To address the impact of digitalisation on divisions of labour, researchers have shifted focus from studying job roles to _tasks_ (Nokelainen, Nevalainen, and Niemi 2018). The dated model of Autor, Levy, and Murnane (2003) analysed tasks along two axes (manual/cognitive and routine/non-routine), positing that both manual and cognitive _routine_ tasks, which follow explicit rules, are susceptible to automation. Frey and Osborne (2017) recognised that advances in AI and machine learning have significantly increased the automation of _nonroutine_ tasks. However, they argued that humans remain advantaged in three categories: (a) perception and manipulation, (b) creative intelligence, and (c) social intelligence. Similarly, Koorn, Leopold, and Leopold (2018) organised work tasks into eight types and found that creative and adaptive tasks were the most difficult to automate. Difficult or not, generative AI models like GPT-3 continue to extend the creative capacities and potential roles of non-human agents (OpenAI 2020; Elkins and Chun 2020). \n\nOther researchers have explored emerging divisions of labour through the lenses of dynamic augmentation and human-robot cooperation (Peruzzini, Grandi, and Pellicciari 2020). Romero et al. (2016a), for example, design systems that respond dynamically to deficits in human physical, sensorial and cognitive performance, and Richert (2018) describes hybrid forms of collaboration in a virtual factory. In general, Industry 4.0 research highlights a shift from the design-time allocation of human tasks to _run-time_ and _mixed-agent_ task distributions.",
"page_span": {
"start": 9,
"end": 9
},
"confidence": "high"
},
{
"label": "conclusion",
"heading": "4.7. Summary of target activity characteristics",
"level": 2,
"text": "Based on the research synthesis presented above, Table 3 summarises the activity-system characteristics of Industry 4.0. The next section identifies and organises worker-level readiness factors aligned with this target. \n\n**Table 3.** Key characteristics of Industry 4.0 modelled as an activity system. \n\n||System||\n|---|---|---|\n|#|Element|Key Characteristics|\n|1|Object(s)|Productivity and global competitiveness|\n|||Sustainability and social innovation|\n|||Technological innovation|\n|2|Worker|Adaptive entity, responsive to dynamic work|\n|||environments|\n|||Driven, maintained and defned by data|\n|||Hybridized or machine augmented|\n|3|Technologies|Enabling technologies: Internet of Things, Cloud, AR/|\n|||VR, big data, additive manufacturing, cybersecurity,|\n|||robotics, advanced interfaces, etc.|\n|||Holistic typology: Technologies of smart|\n|||manufacturing, smart products, smart supply chain|\n|||and smart working|\n|||Core purposes: interoperability and consciousness|\n|4|Workgroups|Hybridised, featuring new agentic entities and roles|\n|||Culturally diverse and geographically dispersed|\n|||Emergent teams, roles and goals|\n|5|Rules and|Increasing levels of autonomy|\n||Culture|Decentralised decision-making|\n|||Service-oriented, customer-relationship culture|\n|||Culture of techno enthusiasm|\n|6|Division of|Increasing automation of non-routine and complex|\n||Labour|tasks|\n|||Human workforce more focused on creative strengths|\n|||Tasks taken up dynamically based on situational needs|\n|||and an agent’s/operator’s (measured) level of|\n|||performance|",
"page_span": {
"start": 9,
"end": 9
},
"confidence": "high"
},
{
"label": "results",
"heading": "5. Stage 2 findings: dimensions of worker readiness",
"level": 1,
"text": "Worker competences, incorporating KSAs aligned with the target, were extracted from the literature and organised into five dimensions. Three dimensions (technological, flexibility and interpersonal readiness) consolidate and extend complexes addressed by other models. Inter-agent and innovation readiness are original proposals synthesising KSAs represented strongly in Industry 4.0 literature but not yet prominent in most worker readiness models.",
"page_span": {
"start": 9,
"end": 9
},
"confidence": "high"
},
{
"label": "other",
"heading": "5.1. Technological readiness",
"level": 2,
"text": "This dimension aggregates three abilities complexes. The first represents _foundational digital skills_ . Typically developed through regular use of mainstream hardware (e.g. personal computers and mobile devices) and software/apps, these skills offer an essential starting point for digitalised work (Canadian Apprenticeship Forum 2018; Johansson 2017). Digital-competence researchers have produced validated instruments to measure these skills across several categories of use (van Deursen, Helsper, and Eynon 2016; Blayone et al. 2018a). The Industry 4.0 literature, however, places greater emphasis on _advanced technology/IT competences_ often requiring \n\nformal learning and on-the-job training (Fonseca 2018; Foresti and Varvakis 2018; Ghobakhloo 2018; Muro et al. 2017). This complex includes KSAs supporting networking and information processing, data analysis, and working with raw materials, smart objects, automated guided vehicles (AGVs) and complex software interfaces (Schallock et al. 2018; Erol et al. 2016). A third complex, addressing _attitudinal dispositions_ toward information technology also achieve prominence. For example, Oesterreich and Teuteberg (2016) and Johansson (2017) highlight technology acceptance and low technology anxiety as orientations promoting effective human-machine interaction. Interest in learning about IT (Gokhale, Brauchle, and Machina 2013), self-direction (Raemdonck, Thijssen, and de Greef 2017) and personal initiative (Frese and Fay 2001) are all intrapersonal factors supporting IT-skills development. One caveat regarding skill acquisition is that emerging augmentation systems are expected to provide unskilled operators with situationally relevant expert procedures as required (Longo, Nicoletti, and Padovano 2017). Therefore, it appears that some operator jobs will require well-aligned attitudes and general digital skills rather than specialist knowledge of materials and procedures.",
"page_span": {
"start": 9,
"end": 10
},
"confidence": "high"
},
{
"label": "other",
"heading": "5.2. Interpersonal readiness",
"level": 2,
"text": "Research in affective computing (Wu, Huang, and Hwang 2016) and social robots (Belpaeme et al. 2018) describe systems that capture, reproduce and even ‘feel’ emotion. Nevertheless, a capacity to respond situationally to the states and behaviours of humans remains a challenge for machines and is considered distinctly human strength (Frey and Osborne 2017). Thus, workers do well to cultivate networking competences (Erol et al. 2016) and social and negotiation skills (Bhattacharyya 2018). Others highlight cross-cultural and onlinecommunication competences for working effectively within geographically dispersed teams (Hämäläinen, Lanz, and Koskinen 2018; Holtkamp, Lau, and Pawlowski 2015). \n\nDespite the prominence of interpersonal skills among Industry 4.0, 21[st] -century skills and digitalcompetence readiness frameworks, manufacturers are advised to consider worker competence priorities against their operational goals. To address this point, Weber, Butschan, and Heidenreich (2017) investigated the effects of cognitive, social and processual \n\ncompetences on technological maturity and performance outcomes at German factories by surveying 284 employees in production and innovation departments. A key finding was that, unlike cognitive and processual competences, social competences showed no positive relationship to levels of technological maturity. This finding suggests a situational exception to the general importance of interpersonal competences. Namely, that those organisations focused on technological transformation should give the development of cognitive and processual competences of mission-critical workers the highest priority.",
"page_span": {
"start": 10,
"end": 10
},
"confidence": "high"
},
{
"label": "other",
"heading": "5.3. Flexibility readiness",
"level": 2,
"text": "Flexibility readiness incorporates three sub-groupings incorporating (a) _multidisciplinary_ knowledge (Freddi 2018; Ghobakhloo 2018), (b) _openness_ to dynamic roles and emergent problems (Erol et al. 2016), and (c) _comfort_ with technological change (Fischer and Pöhler 2018; Schallock et al. 2018). In each case, levels of human adaptability will be shaped by cultural and personal dispositions. For example, tolerance of ambiguity is a well-studied value orientation in the field of cross-cultural analysis (Hofstede 2001), and openness to new experiences is a ‘Big 5ʹ personality trait (Azucar, Marengo, and Settanni 2018). Thus, Nokelainen, Nevalainen, and Niemi (2018) argue that workers may often carry a predisposition towards stability (an ‘entity mindset’) or openness to change (an ‘incremental mindset’). Nevertheless, some empirical studies find that cognitive flexibility and environmental adaptiveness are increased through education and training (Hytönen et al. 2016). From this orientation, learning factories have been designed to augment classroom learning and simulate the dynamic working conditions of digitalised industrial environments (Schallock et al. 2018).",
"page_span": {
"start": 10,
"end": 10
},
"confidence": "high"
},
{
"label": "other",
"heading": "5.4. Inter-agent readiness",
"level": 2,
"text": "This dimension focuses on interactions between different types of _agents_ inclusive of human, non-human and hybrid entities (Gladden 2019). An agent is an autonomous entity with the capacity to act towards its goals and interact with other agents when it cannot reach its objectives alone (Leitão 2009). An _intelligent_ agent must be able to mobilise resources, reflect on actions and adapt to changing circumstances (Romero et al. 2016a). \n\nWithin cyber-physical systems of Industry 4.0, human, system and robotic entities will achieve intelligent agency, at least when functioning optimally. Human workers must prepare to achieve optimal levels of comfort and performance within tightly integrated human-machine assemblages – a challenge greatly influenced by the design of human-machine interfaces (Peruzzini, Grandi, and Pellicciari 2020). \n\nReadiness factors enabling optimal inter-agent functioning include numerous KSAs, with attitudes achieving as much prominence as knowledge and skills. Two critical _attitudes_ are openness to human-machine partnering (Becker and Stern 2016) and trust toward technological entities (Hoff and Bashir 2015), including robots (Richert 2018), decision-automation systems (Lee and See 2004), big-data analytics (Akdil, Ustundag, and Cevikcan 2018) and augmentation apparatuses (Mourtzis 2018). Pacaux-Lemoine et al. (2017) find that a worker’s willingness to trust a machine agent is inversely related to their level of self-confidence, highlighting a vital intrapersonal nuance for measuring readiness. In short, self-confidence is a double-edged sword. Rajaonah et al. (2008) extend this scheme with three mediating variables: perceived workload, perceived risk, and perceptions of system effectiveness. \n\nIn this dimension, essential knowledge and skills include the ability to (a) model the functioning of nonhuman agents; (b) communicate in ways that nonhuman agents can readily process; and (c) calibrate levels of dispositional trust by assessing the situation, performance histories and potential consequences (PacauxLemoine et al. 2017). Pacaux-Lemoine et al. (2017) designed a manufacturing scenario to explore the performance dynamics of human-machine partnering. The experimental design dictated that throughput and energy use were to be balanced in a production environment where some machines malfunctioned and moving products to different machines increased energy use. The three control conditions tested were automation, human control, and human-machine cooperation. Human-machine cooperation achieved the best performance, but the inter-agent skills of the human operator emerged as a significant mediating variable. \n\nMore generally, within the research, human-machine partnering and co-agency are challenging traditional human-tool interaction perspectives (Grudin 2017). Jones, Romero, and Wuest (2018) explain that the wellestablished concept of _interacting with_ machines addresses two general system configurations. On the \n\none hand, tasks may be performed by a human operator monitoring and controlling a machine. On the other hand, a machine may perform all activities under normal circumstances with a human taking over when a problem has been identified. In both bases, a _physical_ human-machine interface is available to be mastered by the operator. The move towards co-agency between humans and machines shifts focus to _cognitive_ interfaces that introduce new requirements for communication and collaboration between agents. Of course, identifying these requirements remains an essential work in progress because such interfaces are still being designed in research labs.",
"page_span": {
"start": 10,
"end": 11
},
"confidence": "high"
},
{
"label": "other",
"heading": "5.5. Innovation readiness",
"level": 2,
"text": "Frey and Osborne (2017) identified innovative thinking and creative intelligence as an incredibly difficult area for machine-automation. Although AIs are generating impressive forms of visual art, music and creative writing (McCormack, Gifford, and Hutchings 2019; Sturm et al. 2018), it remains challenging to automate problem-solving in dynamic environments where solutions must achieve situational effectiveness (Nokelainen, Nevalainen, and Niemi 2018). Therefore, human workers tend to be the most capable entities for generating (useful) novel ideas, refining and evaluating these ideas, functioning collaboratively and learning from failures (Soulé and Warrick 2015). \n\nNevertheless, given the increasing need to ground innovative solutions on big-data analyses and simulations (Vaidyaa, Ambadb, and Bhosle 2018), creative problem-solving is expected to evolve as a hybrid process incorporating humans and automation systems (Carlsson 2018). To what extent intelligent machine agents will ultimately direct human operations (‘automation scenario’) or merely suggest courses of action (‘tool scenario’) in any given context remains an open question (Dworschak and Zaiser 2014). Indeed, there are still several systems-integration challenges hindering the effective coordination of multiple actors in strategic processes (Sanchez, Exposito, and Aguilar 2020). In the end, owing to the ongoing need for humans to generate or support creative data-driven solutions to emergent operational problems, innovation readiness is proposed as a fifth dimension.",
"page_span": {
"start": 11,
"end": 12
},
"confidence": "high"
},
{
"label": "conclusion",
"heading": "5.6. Summary of proposed worker readiness dimensions",
"level": 2,
"text": "Table 4 summarises, and Figure 2 visualises, the resulting five-dimensional model of worker readiness for Industry 4.0. Although technological, interpersonal and flexibility readiness are addressed by previous research, this model calls attention to additional subfactors. The inter-agent and innovation dimensions are new proposals featuring KSAs with high prominence in the sampled literature. Contextual considerations are suggested to emphasise that optimal factor structures must be adapted to local needs/ cultures.",
"page_span": {
"start": 12,
"end": 12
},
"confidence": "high"
},
{
"label": "discussion",
"heading": "6. Discussion",
"level": 1,
"text": "This study produced a new model of worker readiness for Industry 4.0 via a two-stage research synthesis addressing research gaps in 23 prior models. In stage one, it deployed an activity-system apparatus to generate a structured description of the target work environment addressing six elements: the worker/operator, technological systems, guiding objectives, organisational culture and divisions of labour. This approach improved upon many previous ad hoc descriptions. In stage two, this study identified and organised five dimensions of worker readiness strongly aligned with the target. Like 21[st] -century and digital-skill frameworks \n\n**Table 4.** Proposed five-dimensional model of worker readiness for Industry 4.0. \n\n||Readiness|||\n|---|---|---|---|\n|#|Factor|Readiness Subfactors (KSAs)|Contextual Considerations|\n|1|Technological|(1) Foundational digital skills|Level of Industry 4.0 technological maturity|\n|||(2) Advanced IT skills|Presence of adaptive augmentation systems towards Operator 4.0|\n|||(3) Attitudinal orientations and intrapersonal skills supporting||\n|||enthusiastic IT use skill development||\n|2|Flexibility|(1) Multidisciplinary knowledge|Stubbornness of traditional (hierarchical) organisational cultures|\n|||(2) Openness to dynamically assigned roles and tasks|Broader socioeconomic pressures on manufacturing operations|\n|||(3) Tolerance of environmental dynamism and emergent|Level of Industry 4.0 technological maturity|\n|||problems||\n|3|Inter-agent|(1) Attitudes of openness and comfort toward human-|Availability and sophistication of collaborative robots, adaptive|\n|||machine partnering|augmentation systems, wearable tech, expert systems and machine|\n|||(2) A well-calibrated level of trust toward technological|agents|\n|||agents and automation systems|Organisational decision-making protocols|\n|||(3) Knowledge and skills for modelling, communicating and||\n|||calibrating trust with machine agents/robots||\n|4|Interpersonal|(1) Social-networking competencies|Distribution of responsibilities among agents in cyber-physical systems|\n|||(2) Communication and negotiation skills|Location and diversity of teams Levels of interaction, ranging from|\n|||(3) Attitudes and skills supporting digital-mediated|coexistence to collaboration, required to achieve objectives|\n|||collaboration problem-solving in dispersed, cross-cultural||\n|||teams||\n|5|Innovation|(1) Creative and adaptive strategic-thinking skills|Creative capabilities of humans and other agents|\n|||(2) Data-analysis knowledge and software application skills|Organisational culture/rules related to conservatism and innovation|\n||||Level of Industry 4.0 technological maturity|\n\n**Figure 2.** Visual presentation of the proposed five-dimensional worker readiness model, with the two original dimensions highlighted. \n\n(van Laar et al. 2017), the proposed model acknowledged the importance of technical, social and flexibilityrelated KSAs also featured in other Industry 4.0 models. However, it extended these dimensions, emphasising _advanced_ technological skills and mediating attitudes, such as trust in technology. More importantly, it introduced two original dimensions (comprised of interagent and innovation competences) vital to successful human-machine partnering and creative problemsolving in dynamic Industry 4.0 environments. Noteworthy empirical investigations of human-robot collaboration (Richert 2018), trust in automation systems (Hoff and Bashir 2015) and operator-augmentation architectures (Longo, Nicoletti, and Padovano 2017) were selected to highlight emerging operating scenarios characteristic of Industry 4.0 overlooked by mainstream digital-competence frameworks (Eshet 2012; Ferrari 2013; Desjardins, Lacasse, and Belair 2001; van Deursen, Helsper, and Eynon 2016). \n\nThe authors’ next steps are to (a) theorise the constituent competences of inter-agent and innovation readiness more fully, (b) triangulate the model via case studies and observational data, and (c) proceed towards operationalisation and the launch of an online readiness profiling application based on the Global Readiness Explorer (GREx) platform developed by the EILAB (vanOostveen et al. 2019). Existing survey tools or selected subscales may be repurposed to measure technological, interpersonal and flexibility readiness, but a review of available validated instruments must be undertaken. The development of original scales will likely be required to measure inter-agent and innovation readiness. Moreover, industry partnerships will be sought to gather relevant case studies and performance data for model triangulation. This endeavour builds on the authors’ prior experience of digitally recording and analysing interactions with mobile devices to validate a digital-competence instrument (Blayone et al. 2018b). \n\nA new readiness measurement application explicitly designed to measure, visualise and compare worker readiness for Industry 4.0 will bring benefits to students, workers, researchers, educators, organisational trainers, human resource professionals, and policymakers. Individual students and workers will have the opportunity to generate a personal readiness profile and compare their results to a target or group profile. Researchers will gain access to an aggregate database with which to perform comparative research or relate self-report \n\nmeasures to performance data. Educators and trainers can use relevant group profiles to help identify readiness gaps and adapt educational experiences (technologies, learning methods and content) to address them. Human resource professionals and policymakers will gain access to empirical data with which to align competencydevelopment targets and training interventions with workers’ preparation needs. Readiness profile data and accompanying research might also help technologists and engineers design more robust human-machine interfaces and worker-augmentation systems. All of this contributes to a more prepared workforce and more robust industrial systems. \n\nThree limitations of this study are acknowledged. Firstly, the selection of Industry 4.0 as a defining construct skewed the focus towards a German and European perspective. However, the authors recognise Industry 4.0 to be a dominant innovation program (Xu, Xu, and Li 2018; Wang 2018), and the proposed readiness model to be adaptable to digitalised manufacturing more broadly conceived. Secondly, Industry 4.0 remains a rapidly evolving construct. The dynamics of inter-agent readiness may be especially sensitive to change as several research fields (e.g. machine learning, collaborative robotics and dynamic augmentation systems) are actively redefining human-machine interaction. Finally, the proposed model represents a _firststage_ conceptual contribution to a fully operationalised Industry 4.0 readiness framework and worker profile application. The dimensions of inter-agent and innovation readiness, as new proposals, require further conceptual specification incorporating findings from the most current empirical studies.",
"page_span": {
"start": 12,
"end": 13
},
"confidence": "high"
},
{
"label": "conclusion",
"heading": "6.1. Conclusion",
"level": 2,
"text": "This study was initiated by a broader research-anddevelopment program aimed at implementing an Industry 4.0 readiness profile application to collect individual-level data around the globe. Achieving a wellstructured conceptual model of worker readiness for Industry 4.0 grounded on a significant literature base is a necessary first step towards this goal. Owing to the dependence of digital and 21[st] -century skills frameworks on mainstream human-computer interaction models (Grudin 2017) and conceptual gaps in Industry 4.0 readiness models, a more comprehensive model was developed through a two-phase research synthesis. This five- \n\ndimensional model consolidates three dimensions featured in prior research. It also introduces inter-agent and innovation readiness as new proposals addressing human-machine partnering and dynamic problemsolving within digitalised workplaces. As noted, the next steps for participating researchers are to (a) review the availability of existing instrumentation for measuring worker readiness in each dimension, (b) generate casestudy and observational data to triangulate the conceptual proposal, and (c) select or develop scales for generating reliable and valid profiles of worker readiness for Industry 4.0. \n\nIn the end, this study presents an original and reasonably comprehensive foundation on which to develop new tools that enable researchers, educators, trainers, human resource managers, policymakers and system designers to equip workers with the competences and technological affordances for success in Industry 4.",
"page_span": {
"start": 13,
"end": 14
},
"confidence": "high"
},
{
"label": "acknowledgements",
"heading": "Acknowledgments",
"level": 1,
"text": "The authors acknowledge the tremendous support and input of Christian Desjardins (ictin.us) during both the planning and execution stages of this project. In addition, the authors recognise the feedback of Dr Olena Mykhailenko (collaboritsi.com) during the writing and editing stages, and suggestions from EILAB-associated colleagues during the initial scoping phase. This project would not have been possible without the EILAB infrastructure (eilab.ca) at Ontario Tech University, Canada (ontariotechu.ca) and the seminal research contributions of its founding director Dr François Desjardins.",
"page_span": {
"start": 14,
"end": 14
},
"confidence": "high"
},
{
"label": "other",
"heading": "Dedication",
"level": 1,
"text": "We dedicate this study to the workers of General Motors automotive plants in Oshawa, Canada who, in the face of plant closures, are conquering the challenge of learning new skills as manufacturing transitions towards Industry 4.0.",
"page_span": {
"start": 14,
"end": 14
},
"confidence": "high"
},
{
"label": "other",
"heading": "Disclosure statement",
"level": 1,
"text": "No potential conflict of interest was reported by the authors.",
"page_span": {
"start": 14,
"end": 14
},
"confidence": "high"
},
{
"label": "funding",
"heading": "Funding",
"level": 1,
"text": "This work was supported by the Ontario Centres of Excellence (www.oce-ontario.org) and Punchtime (ictin.us), Voucher for Innovation and Productivity, Application [#30859].",
"page_span": {
"start": 14,
"end": 14
},
"confidence": "high"
},
{
"label": "other",
"heading": "ORCID",
"level": 1,
"text": "Todd J. B. Blayone http://orcid.org/0000-0001-6965-7033 Roland VanOostveen http://orcid.org/0000-0001-8767-2894",
"page_span": {
"start": 14,
"end": 14
},
"confidence": "high"
},
{
"label": "references",
"heading": "References",
"level": 1,
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Kohl. 2018. “Learning Factory for Industry 4.0 To Provide Future Skills beyond Technical Training.” _Procedia Manufacturing_ 23: 27–32. doi:10.1016/j.promfg.2018.03.156. \n\n- Schuh, G., R. Anderl, J. Gausemeier, M. t. Hompel, and W. Wahlster. 2017. _Industrie 4.0 Maturity Index: Managing the Digital Transformation of Companies (acatech Study)_ . Herbert Utz Verlag. Accessed 17 October 2020. https://www.acatech. de/wp-content/uploads/2018/03/acatech_STUDIE_ Maturity_Index_eng_WEB.pdf \n\n- Schuh, G., T. Potente, C. Wesch-Potente, A. R. Weber, and J.P. Prote. 2014. “Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0.” _Procedia CIRP_ 19: 51–56. doi:10.1016/j.procir.2014.05.016. \n\n- Scremin, L., F. Armellini, A. Brun, L. Solar-Pelletier, and C. Beaudry. 2018. “Towards a Framework for Assessing the Maturity of Manufacturing Companies in Industry 4.0 Adoption.” In _Analyzing the Impacts of Industry 4.0 in Modern Business Environments_ , edited by R. BrunetThornton and F. Martínez, 224–254. Hershey, PA: IGIGlobal. \n\n- Shahlaei, C., M. Rangraz, and D. Stenmark. 2017. “Transformation of Competence – The Effects of Digitalization on Communicators’ Work.” In _Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, June 5–10, 2017_ , edited by I. Ramos, V. Tuunainen, and H. Krcmar, 195–209. Illinois, USA: Association for Information Systems. \n\n- Shamim, S., S. Cang, H. Yu, and Y. Li. 2016. “Management Approaches for Industry 4.0: A Human Resource Management Perspective.” In _2016 IEEE Congress on Evolutionary Computation (CEC). Vancouver, British Columbia, Canada 24–29 July 2016_ , 5309–5316. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). \n\n- Soulé, H., and T. Warrick. 2015. “Defining 21st Century Readiness for All Students: What We Know and How to Get There.” _Psychology of Aesthetics, Creativity, and the Arts_ 9 (2): 178–186. doi:10.1037/aca0000017. \n\n- Stich, V., G. Gudergan, and V. Zeller. 2018. “Need and Solution to Transform the Manufacturing Industry in the Age of \n\nIndustry 4.0 – A Capability Maturity Index Approach.” In _Collaborative Networks of Cognitive Systems: 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE_ 2018, edited by L. M. Camarinha-Matos, H. Afsarmanesh, and Y. Rezgui, 33–42. Cardiff, UK: Springer Nature. \n\n- Sturm, B. L., O. Ben-Tal, Ú. Monaghan, N. Collins, D. Herremans, E. Chew, G. Hadjeres, E. Deruty, and F. Pachet. 2018. “Machine Learning Research that Matters for Music Creation: A Case Study.” _Journal of New Music Research_ 48 (1): 36–55. doi:10.1080/09298215.2018.1515233. \n\n- Sullivan, E. V. 1970. “The Issue of Readiness in the Design and Organization of the Curriculum: A Historical Perspective.” In _Curriculum Design in a Changing Society_ , edited by R. W. Burns and G. D. Brooks, 39–48. Englewood Cliffs, New Jersey: Educational Technology Publications. \n\n- Sutherland, J. W., J. S. Richter, M. J. Hutchins, D. Dornfeld, R. Dzombak, J. Mangold, S. Robinson, et al. . 2016. “The Role of Manufacturing in Affecting the Social Dimension of Sustainability.” _CIRP Annals Manufacturing Technology_ 65 (2): 689–712. doi:10.1016/j.cirp.2016.05.003. \n\n- Thorndike, E. L. 1932. _The Fundamentals of Learning_ . New York, \n\n - NY: Teachers College Bureau of Publications. \n\n- Vaidyaa, S., P. Ambadb, and S. Bhosle. 2018. “Industry 4.0 - a Glimpse.” _Procedia Manufacturing_ 20: 233–238. doi:10.1016/ j.promfg.2018.02.034. \n\n- van Deursen, A. J. A. M., E. J. Helsper, and R. Eynon. 2016. “Development and Validation of the Internet Skills Scale (ISS).” _Information, Communication & Society_ 19 (6): 804–823. doi:10.1080/1369118X.2015.1078834. \n\n- van Deursen, A. J. A. M., and K. Mossberger. 2018. “Any Thing for Anyone? A New Digital Divide in Internet-of-Things Skills.” _Policy & Internet_ 10 (2): 122–140. doi:10.1002/poi3.171. \n\n- van Laar, E., A. J. A. M. van Deursen, J. A. G. M. van Dijk, and J. de Haan. 2017. “The Relation between 21st-Century Skills and Digital Skills: A Systematic Literature Review.” _Computers in Human Behavior_ 72: 577–588. doi:10.1016/j. chb.2017.03.010. \n\n- vanOostveen, R., E. Childs, W. Barber, M. DiGiuseppe, J. Percival, and C. Desjardins. 2019. “Introducing the Global Educational Learning Observatory (GELO) and the Global Readiness Explorer (Grex): A Framework and Dashboard to Investigate Tech Competence and Culture.” In _18th International Conference on Information Technology Based Higher Education and Training_ . Magdeburg, Germany. \n\n- Virkkunen, J., and D. S. Newnham. 2013. _The Change Laboratory: A Tool for Collaborative Development of Work and Education_ . Rotterdam: Sense Publishers. \n\n- Vygotsky, L. S. 1978. _Mind in Society: Development of Higher Psychological Processes_ . Cambridge, MA: Harvard University Press. \n\n- Wang, B. 2018. “The Future of Manufacturing: A New Perspective.” _Engineering_ 4 (5): 722–728. doi:10.1016/j. eng.2018.07.020. \n\n- Weber, B., J. Butschan, and S. Heidenreich. 2017. “Tackling Hurdles to Digital Transformation – The Role of Competencies for Successful IIOT Implementation.” In _2017 IEEE Technology & Engineering Management Conference (TEMSCON)_ . Santa Clara, CA: IEEE. \n\n- Wilkesmann, M., and U. Wilkesmann. 2018. “Industry 4.0 – Organizing Routines or Innovations?” _VINE Journal of Information and Knowledge Management Systems_ 48 (2): 238–254. doi:10.1108/vjikms-04-2017-0019. \n\n- Wu, C.-H., Y.-M. Huang, and J.-P. Hwang. 2016. “Review of Affective Computing in Education/Learning: Trends and Challenges.” _British Journal of Educational Technology_ 47 (6): 1304–1323. doi:10.1111/bjet.12324. \n\n- Xu, L. D., E. L. Xu, and L. Li. 2018. “Industry 4.0: State of the Art and Future Trends.” _International Journal of Production Research_ 56 (8): 2941–2962. doi:10.1080/00207543.2018. 1444806. \n\n- Zhong, R. Y., X. Xu, E. Klotz, and S. T. Newman. 2017. “Intelligent Manufacturing in the Context of Industry 4.0: A Review.” _Engineering_ 3 (5): 616–630. doi:10.1016/j. Eng.2017.05.015. \n\n**International Journal of Computer Integrated Manufacturing** \n\n**ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tcim20** \n\n## **Prepared for work in Industry 4.0? Modelling the target activity system and five dimensions of worker readiness** \n\n## **Todd J. B. Blayone & Roland VanOostveen** \n\n**To cite this article:** Todd J. B. Blayone & Roland VanOostveen (2021) Prepared for work in Industry 4.0? Modelling the target activity system and five dimensions of worker readiness, International Journal of Computer Integrated Manufacturing, 34:1, 1-19, DOI: 10.1080/0951192X.2020.1836677 \n\n**To link to this article:** https://doi.org/10.1080/0951192X.2020.1836677 \n\nPublished online: 10 Nov 2020. \n\nSubmit your article to this journal \n\nArticle views: 101 \n\nView related articles \n\nView Crossmark data \n\nCiting articles: 1 View citing articles \n\nFull Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tcim20",
"page_span": {
"start": 14,
"end": 20
},
"confidence": "high"
}
],
"tables": [
{
"headers": [
"#
Source
Model Type
Method
Conceptual Structure
Human Factor(s)
Instrument?",
"1
(Akdil, Ustundag,
and Cevikcan
2018)
Industry 4.0 Maturity
Small-scale literature review
Three Aspects: Smart Products and
Services; Smart Business Processes;
Strategy and Organisation. Four
Maturity Levels: Absence; Existence;
Survival; Maturity
General: Human resources
Yes. Validity and reliability not
addressed
2
(Botha2018)
Future Readiness
Theorised on a future-thinking
framework
Three Aspects: Technology; Behaviour;
Future thinking.9Five to ten
readiness levels in each dimension.)
Specifc: Human-machine harmony;
Liberated approach to work; Willingness
to re-skill; Embrace sharing culture
No. Conceptual structure validated
by surveying experts
3
(Canetta, Barni, and
Montini2018)
Digitalisation maturity,
with a focus on
workers and working
conditions
Based on a comparative review of 27
models and interviews
Four Aspects: Processes; Impact;
Technology and Human Resources;
Technological Process Assessment.
Four Maturity Levels: Absence;
Novice; Intermediate; Expert
General: Human resource requirement;,
Changes in worker skills owing to the
digitalisation
Yes. Five-part questionnaire (36
items). Validity and reliability not
addressed
4
(De Carolis et al.
2017)
Digital Readiness
Assessment Maturity
Model (DREAMY)
Capability Maturity Model Integration
framework. Literature review and
expert input
Four Aspects: Process; Monitoring and
Control; Technology; Organisation.
Five Maturity Levels: Initial;
Managed; Defned; Integrated and
Interoperable; Digital-oriented
General: Worker skills to be added to this
modular framework (De Carolis et al.
2017)
Unknown. Mentioned but neither
described nor provided
5
(Ganzarain and
Errasti2016)
Industry 4.0 maturity
model for business
diversifcation
towards Industry 4.0.
Theoretical proposal without a formal
methodology
Three Stages: Envision; Enable; Enact.
Five Maturity Levels: Initial;
Managed; Defned; Transform;
Detailed Business Model
General: Employeetraining
No. Visual model only
6
(Geissbauer, Vedso,
and Schrauf2015)
PcW Maturity Model for
manufacturing
managers to assess
Industry 4.0 maturity.
Based on a survey involving 2000
+ respondents from nine industrial
sectors in 26 countries
Seven Aspects: Digital Business
Models; Digitisation Oferings; Data
and Analytics; Agile IT
Infrastructure; Compliance; Security,
Legal and Tax; Organisation
(Including Employees and Culture).
Four Maturity Levels: Digital Novice,
Vertical Integrator, Horizontal
Collaborator, Digital Champion
General: Worker capacities; Organisation’s
digital culture
No. Although based on survey
research, instrument not
published
7
(Gökalp, Şener, and
Eren2017)
SPICE Maturity Model to
assess Industry 4.0
maturity.
Small-scale review of models. Software
Process Improvement and
Capability Determination
framework.
Five Aspects: Asset Management; Data
governance; Application
Management; Process
Transformation, Organisational
Alignment. Six Maturity Stages:
Incomplete; Performed; Managed;
Established; Predictable; Optimising
General: Skills of IT personnel; Other
human resource requirements for
Industry 4.0 transformation
No
8
(Leineweber et al.
2018)
Industry 4.0 migration
model to help
manufacturing
production
environments
mature.
Based on defnitional analysis from the
literature, from a socio-technical
perspective
Three Aspects: Technological (machine
data acquisition, maintenance, data
evaluation); Organisational
(security, personnel deployment
and capacity data); Personnel
(expertise, development/
qualifcation). Four to Six Maturity
Levels.
General: Worker training
No. Accessible instrument and online
application is a project goal",
"(_Continued_)"
],
"rows": [],
"raw": "|#
Source
Model Type
Method
Conceptual Structure
Human Factor(s)
Instrument?|1
(Akdil, Ustundag,
and Cevikcan
2018)
Industry 4.0 Maturity
Small-scale literature review
Three Aspects: Smart Products and
Services; Smart Business Processes;
Strategy and Organisation. Four
Maturity Levels: Absence; Existence;
Survival; Maturity
General: Human resources
Yes. Validity and reliability not
addressed
2
(Botha2018)
Future Readiness
Theorised on a future-thinking
framework
Three Aspects: Technology; Behaviour;
Future thinking.9Five to ten
readiness levels in each dimension.)
Specifc: Human-machine harmony;
Liberated approach to work; Willingness
to re-skill; Embrace sharing culture
No. Conceptual structure validated
by surveying experts
3
(Canetta, Barni, and
Montini2018)
Digitalisation maturity,
with a focus on
workers and working
conditions
Based on a comparative review of 27
models and interviews
Four Aspects: Processes; Impact;
Technology and Human Resources;
Technological Process Assessment.
Four Maturity Levels: Absence;
Novice; Intermediate; Expert
General: Human resource requirement;,
Changes in worker skills owing to the
digitalisation
Yes. Five-part questionnaire (36
items). Validity and reliability not
addressed
4
(De Carolis et al.
2017)
Digital Readiness
Assessment Maturity
Model (DREAMY)
Capability Maturity Model Integration
framework. Literature review and
expert input
Four Aspects: Process; Monitoring and
Control; Technology; Organisation.
Five Maturity Levels: Initial;
Managed; Defned; Integrated and
Interoperable; Digital-oriented
General: Worker skills to be added to this
modular framework (De Carolis et al.
2017)
Unknown. Mentioned but neither
described nor provided
5
(Ganzarain and
Errasti2016)
Industry 4.0 maturity
model for business
diversifcation
towards Industry 4.0.
Theoretical proposal without a formal
methodology
Three Stages: Envision; Enable; Enact.
Five Maturity Levels: Initial;
Managed; Defned; Transform;
Detailed Business Model
General: Employeetraining
No. Visual model only
6
(Geissbauer, Vedso,
and Schrauf2015)
PcW Maturity Model for
manufacturing
managers to assess
Industry 4.0 maturity.
Based on a survey involving 2000
+ respondents from nine industrial
sectors in 26 countries
Seven Aspects: Digital Business
Models; Digitisation Oferings; Data
and Analytics; Agile IT
Infrastructure; Compliance; Security,
Legal and Tax; Organisation
(Including Employees and Culture).
Four Maturity Levels: Digital Novice,
Vertical Integrator, Horizontal
Collaborator, Digital Champion
General: Worker capacities; Organisation’s
digital culture
No. Although based on survey
research, instrument not
published
7
(Gökalp, Şener, and
Eren2017)
SPICE Maturity Model to
assess Industry 4.0
maturity.
Small-scale review of models. Software
Process Improvement and
Capability Determination
framework.
Five Aspects: Asset Management; Data
governance; Application
Management; Process
Transformation, Organisational
Alignment. Six Maturity Stages:
Incomplete; Performed; Managed;
Established; Predictable; Optimising
General: Skills of IT personnel; Other
human resource requirements for
Industry 4.0 transformation
No
8
(Leineweber et al.
2018)
Industry 4.0 migration
model to help
manufacturing
production
environments
mature.
Based on defnitional analysis from the
literature, from a socio-technical
perspective
Three Aspects: Technological (machine
data acquisition, maintenance, data
evaluation); Organisational
(security, personnel deployment
and capacity data); Personnel
(expertise, development/
qualifcation). Four to Six Maturity
Levels.
General: Worker training
No. Accessible instrument and online
application is a project goal|(_Continued_)|\n|---|---|---|"
},
{
"headers": [
"#
Source
Model Type
Method
Conceptual Structure
Human Factor(s)
Instrument?",
"9
(Lichtblau et al.
2015)
IMPULS model measures
the willingness and
capacity of
companies to
implement Industry
4.0
Mixed methodology (literature review,
expert workshops and survey data)
Five Aspects: Strategy and
Organisation; Smart Factory; Smart
Operations; Smart Products; Data-
driven Services; Employees. Six
Readiness Levels: Outsider;
Beginner; Intermediate;
Experienced; Expert; Top Performer
Specifc: Worker skills; Willingness to learn
IT skills; Competent at implementing
assistance systems
Yes. Industry 4.0 Online Readiness
Check. Validity and reliability not
addressed
10 (Samaranayake,
Ramanathan, and
Laosirihongthong
2017)
Technological readiness
model organises and
weights factors for
Industry 4.0
Small-scale literature review. Factors
weighted via Q-Sort and expert
analytical process
Six_Ranked_Aspects: Human
Technology Skills; Device and
Systems Interconnectivity; Big-Data
Management; Data Sharing
Between and Within Organisations;
Internet System Development; Data
security
Specifc: Technology expertise; Knowledge,
skills, abilities and motivations of staf,
data scientists, and support staf
No
11 (Schuh et al.2017)
Industry 4.0 Maturity
Index for assessing
a company’s Industry
4.0 maturity
stage and next steps
Expert consultations workshops and
case studies. Instrument Validated
through applications at companies
Five Aspects: Resources; Information
Systems; Organisational Structure;
Culture. Five Areas: Development;
Production; Logistics; Services;
Marketing/Sales. Six Maturity
Stages: Computerisation;
Connectivity; Visibility;
Transparency; Predictive Capacity;
Adaptability
Specifc: Digital-communication abilities of
humans and machines; Worker
capacities: Openness to change and
social collaboration
Yes. A sample item provided only.
Stich, Gudergan, and Zeller (2018)
note this instrument has 600
items. Validity and reliability not
addressed
12 (Ganzarain and
Errasti2016)
Industry 4.0 Maturity
Model
Mixed-methods. Expert interviews,
literature review of 72 items and
concept-mapping
Nine Aspects: Strategy; Leadership;
Customers; Products; Operations;
Culture; People; Governance;
Technology. Five Maturity Levels:
Measured on a 5-point scale.
Specifc: Collaboration skills; ICT
competences; Employee openness and
autonomy; Mobile technology
competences
Yes, but not provided. Piloted via
two Austrian studies. Validity and
reliability not addressed.
13 (Scremin et al.2018)
Adoption Maturity
Model (AMM)
assessing the
maturity level of
Industry 4.0
companies
Literature review, structured interviews
of managers, case studies, design of
maturity thresholds and indicators,
and development of archetype
matrix
Eight Aspects: Business Strategy;
Technology Strategy; Networking
and Integration; Infrastructure;
Analytical Skills; Absorptive
Capacity; Benefts of Adoption;
Impact on Efciency. Maturity
Levels: Assessed by researchers via
mixed-methods analysis of
interview responses.
General: Human factors addressed as
‘absorptive capacity’ of an organisation;
Availability of employee training and
awareness of skill requirements for
using systems
Interview guide is published.
Framework validated through ten
case studies. Further validation is
planned."
],
"rows": [],
"raw": "|#
Source
Model Type
Method
Conceptual Structure
Human Factor(s)
Instrument?|9
(Lichtblau et al.
2015)
IMPULS model measures
the willingness and
capacity of
companies to
implement Industry
4.0
Mixed methodology (literature review,
expert workshops and survey data)
Five Aspects: Strategy and
Organisation; Smart Factory; Smart
Operations; Smart Products; Data-
driven Services; Employees. Six
Readiness Levels: Outsider;
Beginner; Intermediate;
Experienced; Expert; Top Performer
Specifc: Worker skills; Willingness to learn
IT skills; Competent at implementing
assistance systems
Yes. Industry 4.0 Online Readiness
Check. Validity and reliability not
addressed
10 (Samaranayake,
Ramanathan, and
Laosirihongthong
2017)
Technological readiness
model organises and
weights factors for
Industry 4.0
Small-scale literature review. Factors
weighted via Q-Sort and expert
analytical process
Six_Ranked_Aspects: Human
Technology Skills; Device and
Systems Interconnectivity; Big-Data
Management; Data Sharing
Between and Within Organisations;
Internet System Development; Data
security
Specifc: Technology expertise; Knowledge,
skills, abilities and motivations of staf,
data scientists, and support staf
No
11 (Schuh et al.2017)
Industry 4.0 Maturity
Index for assessing
a company’s Industry
4.0 maturity
stage and next steps
Expert consultations workshops and
case studies. Instrument Validated
through applications at companies
Five Aspects: Resources; Information
Systems; Organisational Structure;
Culture. Five Areas: Development;
Production; Logistics; Services;
Marketing/Sales. Six Maturity
Stages: Computerisation;
Connectivity; Visibility;
Transparency; Predictive Capacity;
Adaptability
Specifc: Digital-communication abilities of
humans and machines; Worker
capacities: Openness to change and
social collaboration
Yes. A sample item provided only.
Stich, Gudergan, and Zeller (2018)
note this instrument has 600
items. Validity and reliability not
addressed
12 (Ganzarain and
Errasti2016)
Industry 4.0 Maturity
Model
Mixed-methods. Expert interviews,
literature review of 72 items and
concept-mapping
Nine Aspects: Strategy; Leadership;
Customers; Products; Operations;
Culture; People; Governance;
Technology. Five Maturity Levels:
Measured on a 5-point scale.
Specifc: Collaboration skills; ICT
competences; Employee openness and
autonomy; Mobile technology
competences
Yes, but not provided. Piloted via
two Austrian studies. Validity and
reliability not addressed.
13 (Scremin et al.2018)
Adoption Maturity
Model (AMM)
assessing the
maturity level of
Industry 4.0
companies
Literature review, structured interviews
of managers, case studies, design of
maturity thresholds and indicators,
and development of archetype
matrix
Eight Aspects: Business Strategy;
Technology Strategy; Networking
and Integration; Infrastructure;
Analytical Skills; Absorptive
Capacity; Benefts of Adoption;
Impact on Efciency. Maturity
Levels: Assessed by researchers via
mixed-methods analysis of
interview responses.
General: Human factors addressed as
‘absorptive capacity’ of an organisation;
Availability of employee training and
awareness of skill requirements for
using systems
Interview guide is published.
Framework validated through ten
case studies. Further validation is
planned.|\n|---|---|"
},
{
"headers": [
"#
Source
Model Type
Method
Factor Types
Factor Clusters
Instrument?",
"1
(Adolph, Tisch, and
Metternich2014)
Workforce competences and
learning for production
efciencies
Workforce competences derived from
production challenges and megatrends via
small-scale literature review
Competences
FlexibilityChangeabilityResource
efciencyProcess efciency
No
2
(Dworschak and Zaiser
2014)
Competences of workers in
manufacturing contexts of
cyber-physical systems.
Skills drawn from technology forecasts and
organisation structures. Tool scenario:
humans contribute to decisions; Automation
scenario: IT makes decisions
SkillsKnowledge
TechnicalSocial and Collaboration; Deep
Operational and Business Informational;IT
and Engineering knowledge
No
3
(Erol et al.2016)
Taxonomy of competences of
Industry 4.0 workers and
scenario-based learning-
factory (TU Wien)
Based on a small-scale literature review of
competences for digitalised production, and
experience developing a learning factory
Competences
Personal (refect, act autonomously, learn;
trust); Social (communicate, cooperate, use
social media); Action (interdisciplinarity,
manage parallel structures); Domain (model,
analyse)
No
4
(Fareri et al.2018)
Efects of Industry 4.0 on
business value chains, and
the competences of worker
job profles
Modifed Porter value-chain model to map
business functions and select literature.
Automated text mining to analyse literature.
Matrix created to cross-reference business
functions and Industry 4.0 worker profle
archetypes
Archetypes
Data Architect (all depts); IT Architect (logistics
and IT); Geek (management, facilities);
Investigator (facilities, QC); Perfectionist
(facilities, QC, accounting); Prophet (IT,
production); Strategist (marketing,
management, R&D)
No
5
(Galaske et al.2017)
Toolbox Workforce
Management 4.0. Readiness
of businesses, workforce
competences and work
conditions
Small-scale literature review. Theorised using
Guideline Industrie 4.0 and Generic
Procedure Model for SMEs. Matrix with
application felds as vertical elements and
development stages as horizontal elements
Skills
CompetencesEnvironmental
variables
Hard Skills: IT, business and manufacturing;Soft
Skills: personal, social and methodical
competence
Environment: assistance systems, human-
machine interaction, decision support,
security/privacy, organisational fexibility
Graphical model to guide
interviews and
assessment.
6
(Gehrke et al.2015)
Skills and qualifcationsfor
future manufacturing
workers
Industry 4.0 modelled via the experience of 10
engineers. Contextual factors: cooperation,
working environment; Organisation and
structure; Tools and technologies; Tasks:
from physical objects to information, models
and simulations
SkillsQualifcations
(organised by ‘Must have’,
‘Should have’ and ‘Could
have’)
Technical: Must have IT, data processing,
organisational understanding; Should have
knowledge management skills; Could have
programming abilities;
Personal: Must have adaptability and social
skills; Should have trust in technologies and
a learning mindset; No could haves
No
7
(Hartmann and
Bovenschulte2013)
Proposal for deriving skill
needs for Industry 4.0 from
technology roadmaps
Skills derived from road-mapping expert input.
Roadmaps address equipment; robotics and
automation; human-machine collaboration
and bio-engineering
Skills (adaptive to business
contexts)Roles
Organisational Scenarios: yield diferent skill
needs; Roles: Industrial ICT Specialist,
Industrial Cognitive Scientist, Automation
Bionics Specialists
No
8
(Hecklau et al.2016)
Holistic human resource
management for Industry
4.0
Employee competences derived from Industry
4.0 drivers/challenges identifed via
a literature review. Challenges organised in
fve categories: political, economic, social,
technical, environmental and legal
Competences
Technical: media, coding, security;
Methodological: creativity, entrepreneurial
thinking, problem-solving;
Social: intercultural, communication,
teamwork, negotiation; Personal: fexibility,
ambiguity tolerance, learning
Radar charts for
competence
visualisation
9
(Mittelmann2018)
Competences for Work 4.0,
success factors for
businesses of the future.
Small-scale literature review. Characteristics of
Work 4.0 identifed as digitalisation,
collaboration with cyber-systems, fexible,
work independent of location and time,
complex non-routine tasks, and diverse
teams
Competences
Intrapersonal: critical thinking, sense-making,
adaptive thinking, transdisciplinarity, self-
direction; Interpersonal: communication,
virtual collaboration, social and intercultural
intelligence; ICT: computational thinking,
social media, Information security
No
10 (Mourtzis2018)
Skills and competences for
Industry 4.0
Competences derived from literature review of
Industry 4.0 technology descriptions.
Proposal for Education 4.0 based on
teaching factories
Knowledge
Skills
Technical: technological, learning, process
understanding; Methodological: creativity,
problem-solving, analytical, research; Social:
communication, cooperation, networking;
Personal: autonomy, responsibility,
organisational, fexibility
No"
],
"rows": [],
"raw": "|#
Source
Model Type
Method
Factor Types
Factor Clusters
Instrument?|1
(Adolph, Tisch, and
Metternich2014)
Workforce competences and
learning for production
efciencies
Workforce competences derived from
production challenges and megatrends via
small-scale literature review
Competences
FlexibilityChangeabilityResource
efciencyProcess efciency
No
2
(Dworschak and Zaiser
2014)
Competences of workers in
manufacturing contexts of
cyber-physical systems.
Skills drawn from technology forecasts and
organisation structures. Tool scenario:
humans contribute to decisions; Automation
scenario: IT makes decisions
SkillsKnowledge
TechnicalSocial and Collaboration; Deep
Operational and Business Informational;IT
and Engineering knowledge
No
3
(Erol et al.2016)
Taxonomy of competences of
Industry 4.0 workers and
scenario-based learning-
factory (TU Wien)
Based on a small-scale literature review of
competences for digitalised production, and
experience developing a learning factory
Competences
Personal (refect, act autonomously, learn;
trust); Social (communicate, cooperate, use
social media); Action (interdisciplinarity,
manage parallel structures); Domain (model,
analyse)
No
4
(Fareri et al.2018)
Efects of Industry 4.0 on
business value chains, and
the competences of worker
job profles
Modifed Porter value-chain model to map
business functions and select literature.
Automated text mining to analyse literature.
Matrix created to cross-reference business
functions and Industry 4.0 worker profle
archetypes
Archetypes
Data Architect (all depts); IT Architect (logistics
and IT); Geek (management, facilities);
Investigator (facilities, QC); Perfectionist
(facilities, QC, accounting); Prophet (IT,
production); Strategist (marketing,
management, R&D)
No
5
(Galaske et al.2017)
Toolbox Workforce
Management 4.0. Readiness
of businesses, workforce
competences and work
conditions
Small-scale literature review. Theorised using
Guideline Industrie 4.0 and Generic
Procedure Model for SMEs. Matrix with
application felds as vertical elements and
development stages as horizontal elements
Skills
CompetencesEnvironmental
variables
Hard Skills: IT, business and manufacturing;Soft
Skills: personal, social and methodical
competence
Environment: assistance systems, human-
machine interaction, decision support,
security/privacy, organisational fexibility
Graphical model to guide
interviews and
assessment.
6
(Gehrke et al.2015)
Skills and qualifcationsfor
future manufacturing
workers
Industry 4.0 modelled via the experience of 10
engineers. Contextual factors: cooperation,
working environment; Organisation and
structure; Tools and technologies; Tasks:
from physical objects to information, models
and simulations
SkillsQualifcations
(organised by ‘Must have’,
‘Should have’ and ‘Could
have’)
Technical: Must have IT, data processing,
organisational understanding; Should have
knowledge management skills; Could have
programming abilities;
Personal: Must have adaptability and social
skills; Should have trust in technologies and
a learning mindset; No could haves
No
7
(Hartmann and
Bovenschulte2013)
Proposal for deriving skill
needs for Industry 4.0 from
technology roadmaps
Skills derived from road-mapping expert input.
Roadmaps address equipment; robotics and
automation; human-machine collaboration
and bio-engineering
Skills (adaptive to business
contexts)Roles
Organisational Scenarios: yield diferent skill
needs; Roles: Industrial ICT Specialist,
Industrial Cognitive Scientist, Automation
Bionics Specialists
No
8
(Hecklau et al.2016)
Holistic human resource
management for Industry
4.0
Employee competences derived from Industry
4.0 drivers/challenges identifed via
a literature review. Challenges organised in
fve categories: political, economic, social,
technical, environmental and legal
Competences
Technical: media, coding, security;
Methodological: creativity, entrepreneurial
thinking, problem-solving;
Social: intercultural, communication,
teamwork, negotiation; Personal: fexibility,
ambiguity tolerance, learning
Radar charts for
competence
visualisation
9
(Mittelmann2018)
Competences for Work 4.0,
success factors for
businesses of the future.
Small-scale literature review. Characteristics of
Work 4.0 identifed as digitalisation,
collaboration with cyber-systems, fexible,
work independent of location and time,
complex non-routine tasks, and diverse
teams
Competences
Intrapersonal: critical thinking, sense-making,
adaptive thinking, transdisciplinarity, self-
direction; Interpersonal: communication,
virtual collaboration, social and intercultural
intelligence; ICT: computational thinking,
social media, Information security
No
10 (Mourtzis2018)
Skills and competences for
Industry 4.0
Competences derived from literature review of
Industry 4.0 technology descriptions.
Proposal for Education 4.0 based on
teaching factories
Knowledge
Skills
Technical: technological, learning, process
understanding; Methodological: creativity,
problem-solving, analytical, research; Social:
communication, cooperation, networking;
Personal: autonomy, responsibility,
organisational, fexibility
No|\n|---|---|"
},
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[
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[
"",
"",
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[
"",
"",
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[
"",
"",
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"",
"",
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[
"",
"",
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[
"",
"",
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[
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],
[
"",
"",
"Culturally diverse and geographically dispersed"
],
[
"",
"",
"Emergent teams, roles and goals"
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[
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],
[
"",
"Culture",
"Decentralised decision-making"
],
[
"",
"",
"Service-oriented, customer-relationship culture"
],
[
"",
"",
"Culture of techno enthusiasm"
],
[
"6",
"Division of",
"Increasing automation of non-routine and complex"
],
[
"",
"Labour",
"tasks"
],
[
"",
"",
"Human workforce more focused on creative strengths"
],
[
"",
"",
"Tasks taken up dynamically based on situational needs"
],
[
"",
"",
"and an agent’s/operator’s (measured) level of"
],
[
"",
"",
"performance"
]
],
"raw": "||System||\n|---|---|---|\n|#|Element|Key Characteristics|\n|1|Object(s)|Productivity and global competitiveness|\n|||Sustainability and social innovation|\n|||Technological innovation|\n|2|Worker|Adaptive entity, responsive to dynamic work|\n|||environments|\n|||Driven, maintained and defned by data|\n|||Hybridized or machine augmented|\n|3|Technologies|Enabling technologies: Internet of Things, Cloud, AR/|\n|||VR, big data, additive manufacturing, cybersecurity,|\n|||robotics, advanced interfaces, etc.|\n|||Holistic typology: Technologies of smart|\n|||manufacturing, smart products, smart supply chain|\n|||and smart working|\n|||Core purposes: interoperability and consciousness|\n|4|Workgroups|Hybridised, featuring new agentic entities and roles|\n|||Culturally diverse and geographically dispersed|\n|||Emergent teams, roles and goals|\n|5|Rules and|Increasing levels of autonomy|\n||Culture|Decentralised decision-making|\n|||Service-oriented, customer-relationship culture|\n|||Culture of techno enthusiasm|\n|6|Division of|Increasing automation of non-routine and complex|\n||Labour|tasks|\n|||Human workforce more focused on creative strengths|\n|||Tasks taken up dynamically based on situational needs|\n|||and an agent’s/operator’s (measured) level of|\n|||performance|"
},
{
"headers": [
"",
"Readiness",
"",
""
],
"rows": [
[
"#",
"Factor",
"Readiness Subfactors (KSAs)",
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],
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],
[
"",
"",
"(2) Advanced IT skills",
"Presence of adaptive augmentation systems towards Operator 4.0"
],
[
"",
"",
"(3) Attitudinal orientations and intrapersonal skills supporting",
""
],
[
"",
"",
"enthusiastic IT use skill development",
""
],
[
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"Stubbornness of traditional (hierarchical) organisational cultures"
],
[
"",
"",
"(2) Openness to dynamically assigned roles and tasks",
"Broader socioeconomic pressures on manufacturing operations"
],
[
"",
"",
"(3) Tolerance of environmental dynamism and emergent",
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],
[
"",
"",
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""
],
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],
[
"",
"",
"machine partnering",
"augmentation systems, wearable tech, expert systems and machine"
],
[
"",
"",
"(2) A well-calibrated level of trust toward technological",
"agents"
],
[
"",
"",
"agents and automation systems",
"Organisational decision-making protocols"
],
[
"",
"",
"(3) Knowledge and skills for modelling, communicating and",
""
],
[
"",
"",
"calibrating trust with machine agents/robots",
""
],
[
"4",
"Interpersonal",
"(1) Social-networking competencies",
"Distribution of responsibilities among agents in cyber-physical systems"
],
[
"",
"",
"(2) Communication and negotiation skills",
"Location and diversity of teams Levels of interaction, ranging from"
],
[
"",
"",
"(3) Attitudes and skills supporting digital-mediated",
"coexistence to collaboration, required to achieve objectives"
],
[
"",
"",
"collaboration problem-solving in dispersed, cross-cultural",
""
],
[
"",
"",
"teams",
""
],
[
"5",
"Innovation",
"(1) Creative and adaptive strategic-thinking skills",
"Creative capabilities of humans and other agents"
],
[
"",
"",
"(2) Data-analysis knowledge and software application skills",
"Organisational culture/rules related to conservatism and innovation"
],
[
"",
"",
"",
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]
],
"raw": "||Readiness|||\n|---|---|---|---|\n|#|Factor|Readiness Subfactors (KSAs)|Contextual Considerations|\n|1|Technological|(1) Foundational digital skills|Level of Industry 4.0 technological maturity|\n|||(2) Advanced IT skills|Presence of adaptive augmentation systems towards Operator 4.0|\n|||(3) Attitudinal orientations and intrapersonal skills supporting||\n|||enthusiastic IT use skill development||\n|2|Flexibility|(1) Multidisciplinary knowledge|Stubbornness of traditional (hierarchical) organisational cultures|\n|||(2) Openness to dynamically assigned roles and tasks|Broader socioeconomic pressures on manufacturing operations|\n|||(3) Tolerance of environmental dynamism and emergent|Level of Industry 4.0 technological maturity|\n|||problems||\n|3|Inter-agent|(1) Attitudes of openness and comfort toward human-|Availability and sophistication of collaborative robots, adaptive|\n|||machine partnering|augmentation systems, wearable tech, expert systems and machine|\n|||(2) A well-calibrated level of trust toward technological|agents|\n|||agents and automation systems|Organisational decision-making protocols|\n|||(3) Knowledge and skills for modelling, communicating and||\n|||calibrating trust with machine agents/robots||\n|4|Interpersonal|(1) Social-networking competencies|Distribution of responsibilities among agents in cyber-physical systems|\n|||(2) Communication and negotiation skills|Location and diversity of teams Levels of interaction, ranging from|\n|||(3) Attitudes and skills supporting digital-mediated|coexistence to collaboration, required to achieve objectives|\n|||collaboration problem-solving in dispersed, cross-cultural||\n|||teams||\n|5|Innovation|(1) Creative and adaptive strategic-thinking skills|Creative capabilities of humans and other agents|\n|||(2) Data-analysis knowledge and software application skills|Organisational culture/rules related to conservatism and innovation|\n||||Level of Industry 4.0 technological maturity|"
}
],
"references": [
{
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},
{
"raw": "- Akdil, K. Y., A. Ustundag, and E. Cevikcan. 2018. “Maturity and Readiness Model for Industry 4.0 Strategy.” In _Industry 4.0: Managing the Digital Transformation_ , edited by A. Ustundag and E. Cevikcan, 61–94. Switzerland: Springer International Publishing."
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"raw": "**International Journal of Computer Integrated Manufacturing**"
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"raw": "**ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tcim20**"
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"raw": "## **Prepared for work in Industry 4.0? Modelling the target activity system and five dimensions of worker readiness**"
},
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"raw": "## **Todd J. B. Blayone & Roland VanOostveen**"
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"raw": "**To cite this article:** Todd J. B. Blayone & Roland VanOostveen (2021) Prepared for work in Industry 4.0? Modelling the target activity system and five dimensions of worker readiness, International Journal of Computer Integrated Manufacturing, 34:1, 1-19, DOI: 10.1080/0951192X.2020.1836677",
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"doi": "10.1080/0951192X.2020.1836677"
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"raw": "Published online: 10 Nov 2020."
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