Smart Manufacturing 5.0

1. Manufacturing at a crossroads: The emergence of
Smart Manufacturing 5.0
Daniel Alejandro Rossit and Foivos Psarommatis
1. Manufacturing at a crossroads: Historical roots and
contemporary imperatives
2. The three pillars: Sustainability, human-centricity, and
resilience

2.1 Sustainability: Manufacturing for a planet under pressure

2.2 Human-centricity: Restoring the worker to the center of
manufacturing

2.3 Resilience: Manufacturing systems that survive disruption
3. Why this book, and why now
4. Themes, structure, and the organization of the volume

4.1 The conceptual landscape: From Industry 4.0 to smart
manufacturing 5.0

4.2 Information architecture: The digital backbone of SM 5.0

4.3 Intelligence and autonomy: Artificial intelligence in
manufacturing

4.4 Quality, defects, and zero waste: Closing the loop on
production

4.5 Production scheduling: Orchestrating the smart factory in
real time

4.6 Supply chains and value networks: Beyond the smart
factory

4.7 Circularity: Smart manufacturing for a circular economy

4.8 Human-centric systems: Work design, knowledge, and
collaboration

4.9 Applications beyond manufacturing: Smart services and
healthcare
5. Cross-cutting themes and the architecture of SM 5.0
6. How to read this book
References
2. The role of humans in decision-making and the
integration of human—machine in Industry 5.0
Vı´tor Alca´cer, Rui Ferreira, David Mendes, Luan Schnaidt and
Tiago Guerreiro
1. Introduction

1.1 Industry 4.0 and the role of humans in decision-making

1.2 Chapter objectives and motivation
2. Toward Industry 5.0

2.1 Industry 5.0 technologies

2.2 Developments of Industry 5.0
3. Research method and data

3.1 Identification of studies

3.2 Screening of search results
4. Results

4.1 Bibliometric analysis

4.2 The role of humans in decision-making in Industry 5.0

4.3 Integration of human—machine in Industry 5.0
5. Discussion
6. Conclusions

6.1 Research gaps

6.2 Research outlook
Appendix A
References
3. The role of humans in smart manufacturing
Sotirios Panagou and Fabio Sgarbossa
1. Introduction
2. From Industry 4.0 to Industry 5.0: Repositioning the human
in manufacturing systems

2.1 The technology-centric paradigm of Industry 4.0

2.2 The socio-technical nature of manufacturing systems

2.3 The emergence of Industry 5.0 and human-centric
manufacturing

2.4 Reframing the role of humans in smart manufacturing
3. The human as a multidimensional system component

3.1 Moving beyond the “operator” perspective

3.2 Dimensions of the human factor in manufacturing
systems

3.3 Interdependencies between dimensions

3.4 Human variability and diversity

3.5 Toward human factor sustainability
4. Humans in smart manufacturing technologies

4.1 The technological landscape of smart manufacturing

4.2 From automation to augmentation

4.3 Human—robot collaboration

4.4 Cognitive assistive technologies and decision support

4.5 Challenges in human—technology integration
5. Human—technology interaction in smart manufacturing

5.1 The centrality of interaction in smart systems

5.2 Human-in-the-loop and human-on-the-loop paradigms

5.3 Cognitive load and information processing

5.4 Trust, transparency, and explainability

5.5 Human-centered interface design

5.6 Adaptivity and personalization
6. Humans in the manufacturing system: A socio-technical
integration perspective

6.1 Manufacturing systems as socio-technical systems

6.2 Interactions between humans, technology, and
organizations

6.3 Humans as integrators of system complexity

6.4 Organizational implications of human-centric design

6.5 Toward integrated human-centric manufacturing systems
7. Human-centric decision-making in smart manufacturing

7.1 The evolving nature of decision-making in
manufacturing systems

7.2 The role of artificial intelligence in decision support

7.3 Human-in-the-loop decision-making

7.4 Balancing automation and human control

7.5 Explainability and transparency in AI-supported
decisions

7.6 Human authority, responsibility, and governance

7.7 Toward collaborative intelligence in manufacturing
8. Challenges and barriers to human-centric smart
manufacturing

8.1 Cognitive and information overload

8.2 Technology acceptance and resistance

8.3 Skills gap and workforce transformation

8.4 Integration complexity and data fragmentation

8.5 Ethical, privacy, and trust concerns

8.6 Balancing performance and well-being
9. Future directions for human-centric smart manufacturing

9.1 Toward adaptive and context-aware manufacturing
systems

9.2 Personalization of human—technology interaction

9.3 Integration of human factors into digital twins and
system models

9.4 Evolution of human roles and skills

9.5 Ethical and responsible human—AI collaboration

9.6 Toward resilient and sustainable manufacturing systems
10. Conclusions
Further reading
4. Enabling technologies and methods for the smart
recycling sector in the Industry 5.0 era
Fotios Konstantinidis, Panagiotis Zoumpoulis,
Konstantinos Kokkalis, Savvas Sifnaios, Tina Katika,
Kostas Naskou, Georgios Tsimiklis and Angelos Amditis
1. Introduction
2. Waste management value chains in the Industry 5.0 era

2.1 Municipal solid waste value chain

2.2 Construction and demolition waste value chain

2.3 Urban mining value chain

2.4 Other types of waste value chains
3. Enabling technologies for Industry 5.0-aligned smart
recycling

3.1 Sensing and material identification

3.2 Robotic sorting and cyber-physical automation

3.3 Collaborative disassembly for WEEE and complex
assemblies

3.4 Mobile robots in collection and internal logistics

3.5 Human-centric interfaces: XR and safe human—robot
collaboration

3.6 Digital twins and simulation as decision infrastructure

3.7 Incentive and traceability infrastructures: Deposit-return
systems and digital product passports

3.8 Foundation models for operator support and
open-vocabulary perception
4. Operationalizing the Industry 5.0 pillars

4.1 Human centricity in recycling

4.2 Sustainability in recycling

4.3 Resilience in recycling
5. From pilots to digital transformation: Reference architecture
and maturity model

5.1 Reference architecture for Industry 5.0-aligned smart
recycling

5.2 Industry 5.0 maturity model for digitalized recycling
value chains
6. Transforming traditional recycling value chains into Industry
5.0-ready systems

6.1 Municipal solid waste 5.0: From opaque collection to
quality-aware recovery

6.2 Construction and demolition waste 5.0: Upstream
intelligence, selective deconstruction, and safe
high-force separation

6.3 WEEE and urban mining 5.0: Hybrid disassembly
guided by trustworthy product information
7. Discussion
8. Conclusion
References
5. Simulation-based resilience analysis in hybrid
flow-shop environments
Juan Jose´ Manuel Arza, Germa´n Prieto, Bruno de Athayde Prata
and Daniel Alejandro Rossit
1. Introduction
2. Background

2.1 Industrial wear: machining example

2.2 Concept of resilience

2.3 Resilience and tool wear monitoring

2.4 Summary contribution
3. Simulation experiment and proposed approach

3.1 Experimental model

3.2 Resilient rescheduling method
4. Experimentation and results

4.1 Description of the experiments

4.2 Analysis of results
5. Conclusions
Acknowledgments
References
Further reading
6. Digital transformation of supply chains: Ontologies
to enable interoperability and information visibility
Gabriela P. Henning
1. Introduction
2. Supply chain evolution
3. Digital supply chain
4. Interoperability
5. Ontologies

5.1 Ontology engineering
6. Available supply chain ontologies
7. Conclusions
References
7. Smart manufacturing key enabling technologies
Gokan May
1. Introduction
2. Connectivity and cyber-physical foundations
3. Data infrastructure, interoperability, and digital threads
4. Digital twins as integrative enablers
5. Intelligent analytics and AI as augmentation layers
6. Human system interfaces and orchestration technologies
7. Integration challenges and practical limits
8. Synthesis and outlook toward Industry 5.0
9. Closing reflections: what actually makes smart
manufacturing work
References
8. Smart manufacturing potential towards sustainability
Victor Azamfirei Ionita and Foivos Psarommatis
1. Introduction
2. Evolution of sustainability in manufacturing

2.1 Traditional manufacturing

2.2 Lean manufacturing

2.3 Green & eco manufacturing

2.4 Sustainable manufacturing (6R approach)
3. Sustainability in smart manufacturing systems

3.1 Environmental sustainability

3.2 Economic sustainability

3.3 Social sustainability
4. Key technologies for sustainable manufacturing

4.1 From subtractive to additive manufacturing

4.2 Increased visibility

4.3 Connectivity

4.4 Intelligence from tacit to artificial

4.5 Transparency and trustworthiness
5. Challenges and barriers to sustainable smart manufacturing
6. Discussions
7. Future trends and research directions
8. Conclusions
References
9. Smart manufacturing and circularity
Foivos Psarommatis and John Lindstro¨m
1. Introduction

1.1 Why circularity is an operational (not only policy)
challenge

1.2 Smart Manufacturing 5.0 as an enabler of circular
value retention

1.3 Chapter scope, structure, and key claims
2. Circularity foundations and the R-strategies

2.1 From linear product life cycle to sustainable product
life cycle (SPLC)

2.2 R-strategies and the value-retention hierarchy

2.3 Sustainable lifecycle loops: Reuse, repurpose, recycle
and “designing out disposal”

2.4 Barriers and failure modes in today’s circularity
implementations
3. Smart Manufacturing 5.0 capabilities for circularity

3.1 Cyber-physical connectivity and lifecycle data
acquisition

3.2 Digital twins for lifecycle optimization and what-if
analysis

3.3 AI/ML-based analytics for quality, condition assessment,
and decision support

3.4 Human-centric operations: Skills, trust, and
operator-in-the-loop control

3.5 Data governance across enterprises: Interoperability,
access control, accountability
4. Operationalizing R-strategies with Smart Manufacturing
(mapping and mechanisms)

4.1 R0—R1 (refuse/rethink/reduce): Circularity-by-design and
waste prevention

4.2 R2 (reuse): Condition-based reuse, verification, and
reverse logistics orchestration

4.3 R3 (repair): Predictive maintenance, service planning,
and spare-part readiness

4.4 R4 (refurbish): Grading, inspection workflows, and
requalification testing

4.5 R5 (remanufacture): Configuration control, process
planning, and certification evidence

4.6 R6 (repurpose): Compatibility rules, reconfiguration,
and functional reassignment

4.7 R7 (recycle): Smart sorting, material purity, and
secondary raw material quality control

4.8 R8 (recover): Residual pathway and system boundary
considerations

4.9 How Smart Manufacturing activates (or constrains)
R-strategies
5. Digital Product Passport and advanced traceability as the
circularity backbone

5.1 Why circular loops fail without persistent identity and
lifecycle lineage

5.2 DPP functional requirements for circularity (what data,
at what granularity, for whom)

5.3 Traceability architectures (centralized, federated, hybrid)
and evidence management

5.4 Interoperability: Identifiers, event models, semantics,
and minimum datasets
6. Limitations and shortcomings of current approaches
7. Conclusions, practice gaps and future directions
References
10. Smart manufacturing and supply chain
Gokan May
1. Introduction
2. From smart factory to connected value network
3. Digital backbone: Connectivity, interoperability, and
end-to-end data flow
4. Visibility and transparency across manufacturing and
supply networks
5. Planning, synchronization, and decision orchestration
under uncertainty
6. Digital twins, simulation, and scenario-based coordination
7. Resilience and adaptability in connected manufacturing
networks
8. Conclusions and future outlook
References
11. Agentic artificial intelligence in the smart
manufacturing era
Marios Tyrovolas, Georgios Mermigkis and Chrysostomos Stylios
1. Introduction
2. Background of agentic artificial intelligence

2.1 From predictive AI to agentic decision-making

2.2 Theoretical foundations of agentic AI
3. Prospects of agentic AI in manufacturing

3.1 Enabling technologies for agentic AI in smart
manufacturing

3.2 Frameworks and architectures for agentic AI in smart
factories

3.3 Applications of agentic AI in manufacturing
4. Discussion

4.1 Benefits and opportunities

4.2 Open challenges
5. Conclusion and future directions
Acknowledgments
References
12. Smart manufacturing and zero defect and waste
manufacturing
Foivos Psarommatis
1. Introduction

1.1 Why quality is important and its connection to smart
manufacturing

1.2 From Industry 4.0 to smart manufacturing 5.0

1.3 Why quality, sustainability, and resilience must be
treated jointly

1.4 Chapter scope and contributions
2. Smart manufacturing: core characteristics and quality
implications

2.1 Data-rich, connected, adaptive production systems

2.2 Mass customization, lot-size-one, and compressed
decision cycles

2.3 Cyber-physical systems, IoT, AI, and digital twins in
manufacturing

2.4 Why conventional quality management becomes
insufficient
3. Evolution from traditional quality management to ZDM

3.1 Lean, Six Sigma, TQM, TOC, Lean Six Sigma

3.2 Quality 4.0 and digital quality management

3.3 Limitations of legacy approaches in smart production
environments and evolution toward ZDM
4. ZDM: foundations and conceptual framework

4.1 Definition and scope of ZDM

4.2 Product-oriented and process-oriented ZDM

4.3 The four ZDM strategies: detect, predict, repair, prevent

4.4 Comparative positioning of ZDM versus classical and
digital QM paradigms

4.5 The ZDM cycle as a smart manufacturing decision
loop for holistic manufacturing quality
5. Enabling technologies for ZDM in smart manufacturing
6. From zero defect to zero waste manufacturing
7. Human-centric and Industry 5.0 dimensions
8. Research and practice gaps and future directions

8.1 From fragmented tools to integrated frameworks

8.2 Common implementation problems

8.3 Need for cross-lifecycle and cross-enterprise ZDM

8.4 Business models, cost-benefit evidence, and
implementation maturity

8.5 Toward autonomous, sustainable, and explainable quality
systems
9. Conclusions

9.1 Managerial and research implications

9.2 Final remarks for smart manufacturing 5.0
References
13. Data structuring in smart manufacturing: From
heterogeneous streams to actionable insights
Ale�s Jandera, Marko Varecha, Zoran Kascelan, George Siaterlis and
Foivos Psarommatis
1. Smart manufacturing as a data-intensive production
paradigm

1.1 The central problem: data abundance without
operational meaning

1.2 Key challenges in the domain

1.3 From raw signals to actionable information

1.4 Scope and contribution of the chapter
2. The landscape of heterogeneous data in manufacturing

2.1 Scalar and multivariate time-series data

2.2 Visual and spatial data

2.3 Event-based and transactional data

2.4 Frequency mismatch and synchronization complexity

2.5 Data quality, noise, and metadata requirements
3. Data integration architectures in smart manufacturing

3.1 The OT—IT integration problem

3.2 Communication protocols and ingestion mechanisms

3.3 Edge and cloud roles in industrial data architectures

3.4 Unified data architectures and interoperability

3.5 Semantic interoperability and ontologies

3.6 Security, governance, and controlled exposure
4. Data normalization, synchronization, and contextual
alignment

4.1 Timestamp normalization and temporal alignment

4.2 Aggregation of high-frequency signals

4.3 Vent-based contextualization

4.4 Semantic normalization

4.5 Preparing manufacturing data for analytics and digital
twins
5. Object-oriented modeling for structured manufacturing
data

5.1 Why manufacturing data needs an object-oriented
structure

5.2 Core modeling entities

5.3 Relationships between entities

5.4 Benefits of structured modeling
6. Case study: Digital product passport as an application of
structured manufacturing data

6.1 Why DPP is a relevant use case

6.2 DPP as a lifecycle-oriented data structure

6.3 Platform implementation logic

6.4 Node entity in the case study

6.5 Step entity in the case study

6.6 Logistics as a first-class structured entity

6.7 Lessons from the implementation
7. Discussions and limitations

7.1 Comparison with current data structuring practices

7.2 Limitations of current data structuring methodologies

7.3 Tensions and trade-offs in structured manufacturing data

7.4 Broader implications for smart manufacturing
8. Conclusion and future directions
References
Further reading
14. Model-driven manufacturing information
representation in smart manufacturing
Shouxuan Wu, Yufan Liu, Guoxin Wang, Yuanwei Qu,
Jianyu Huang, Jinzhi Lu, Foivos Psarommatis, Dimitris Kyritsis and
Arild Waaler
1. Introduction
2. Manufacturing information representation: characteristics,
methods and techniques

2.1 Main characteristics of manufacturing information

2.2 Manufacturing information representation

2.3 Model-based systems engineering for manufacturing
information representation
3. Information modeling framework (IMF) for smart
manufacturing

3.1 Information modeling framework (IMF)

3.2 IMF semantic and syntax

3.3 IMF modeling ecosystem
4. Application in photovoltaic (PV) product manufacturing

4.1 PV manufacturing

4.2 PV manufacturing information representation by IMF

4.3 Discussion on IMF and MBSE modeling in PV
manufacturing

4.4 Conclusions
References
15. Artificial intelligence applications in production
planning and control: A review and future
research challenges in Industry 5.0 era
Julia´n E. Tornillo, Guadalupe Pascal, Andre´s Redchuk
and Daniel Alejandro Rossit
1. Introduction
2. Background: technologies impacts and research description

2.1 Technologies and smart manufacturing

2.2 Artificial intelligence

2.3 Industry 5.0 approach

2.4 Scope of the investigation
3. Methodology: systematic literature review
4. Results

4.1 Bibliometric analysis

4.2 Literature review
5. Discussions on the results of the review and future
challenges to further explore the topic

5.1 Main insights from reviewed papers

5.2 Future research challenges
6. Conclusions
Data availability statement
References
16. Production scheduling in smart manufacturing
systems
Foivos Psarommatis
1. Scheduling model for smart manufacturing

1.1 Layered model: scheduling as a coordination pillar
in SM

1.2 Event-driven model: closed-loop scheduling driven by
shop-floor state and decision support

1.3 KPI-driven schedule regeneration: releasing only
“acceptable” schedules
2. Scheduling as the central decision layer in smart
manufacturing
3. Impact of smart manufacturing characteristics on
scheduling problem formulation
4. Scheduling for zero defect manufacturing in smart
manufacturing environments

4.1 Quality-driven events as first-class triggers for
(re)scheduling

4.2 Scheduling counter-actions: from detection to
time-feasible interventions

4.3 Criteria and KPI design: evaluating ZDM schedules
beyond classical objectives

4.4 ZDM-ready scheduling capability: what the scheduling
system must support
5. Scheduling across production, logistics, and inventory in
smart manufacturing
6. Scheduling responsiveness, stability, and human-centric
execution
7. Discussion
8. Conclusions and future directions

8.1 Conclusions

8.2 Future research and industrial development directions
References
17. Cooptimizing humans and algorithms: A simulation
study of replenishment in RMFS for Industry 5.0
D. Montemurri, D.G. Rossit and Daniel Alejandro Rossit
1. Introduction
2. Literature review
3. Experimental framework

3.1 Simulation structure and control policies

3.2 Operational performance measurement

3.3 The RAWSim-O simulation framework

3.4 Statistical analysis
4. Results and discussion
5. Conclusions and future research
References
18. Human-centered home healthcare delivery: A
trade-off analysis using multiobjective optimization
A. Felipe Torres-Ramos, Fabian Castan˜o and Nubia Velasco
1. Introduction
2. Related work
3. Problem description

3.1 Model formulation
4. Solution methods

4.1 Initial solution

4.2 Local searches. Variable Neighborhood Descent (VND)

4.3 ε-constraint metaheuristic
5. Computational experiments

5.1 MILP results

5.2 Constructive heuristics results

5.3 ε-constraint metaheuristic results
6. Trade-off analysis between objectives
7. Conclusions
Appendix A. Detailed results
Acknowledgments
References
19. Integrated knowledge management framework for
industry 5.0 in project-based organizations
Mihai Dragomir and Mircea Fulea
1. Introduction
2. Context and related literature
3. Proposed modeling framework

3.1 Beyond tacit-explicit: Emerging knowledge categories
in Industry 5.0

3.2 Knowledge lifecycle in projects

3.3 Framework architecture: The three-pillar model
4. Discussion and implementation

4.1 Phased implementation strategy

4.2 Validation considerations

4.3 Success factors, implementation risks and limitations
5. Conclusions
References
20. Adaptive work systems for smart manufacturing
Sebastian Schlund
1. Motivation
2. Work design to fulfill the user’s needs

2.1 Adaptive work systems

2.2 Demographics as a driver of development

2.3 Inclusive design in manufacturing

2.4 Context in adaptive work systems
3. Application examples

3.1 Adaptive information provision

3.2 Automated, person-specific ergonomics assessment

3.3 Adaptive task allocation
4. Discussion
References
21. From Industry 4.0 to Smart Manufacturing 5.0: A
historical parallel with Scientific Management
Daniel Alejandro Rossit
1. Introduction
2. The discussion in brief: is SM 5.0 more than a relabeling?
3. Scientific Management and the first industrial revolution:
background and links

3.1 Scientific achievements before Scientific Management

3.2 First industrial revolution development

3.3 Scientific Management and its impact

3.4 From Scientific Management to new descendant
disciplines
4. Industry 4.0 technology and the current scenario

4.1 Enabling technologies as an integrated substrate

4.2 Artificial intelligence: the newest enabling technology

4.3 Industry 4.0’s historic moment and its limits
5. Smart Manufacturing 5.0 as the conceptual reframing that
will capitalize on Industry 4.0

5.1 Why the Scientific Management analogy fits SM 5.0?

5.2 Sustainability and the broader notion of value

5.3 Human-centric perspective

5.4 Resilience as designing criterion
6. Conclusions
References
Index