Co-Chairs: ^(a)Riccardo Rosati, ^(b)Luca Romeo, ^(c)Pedro Antonio Gutiérrez, ^(c)Victor Vargas

Affiliation: ^(a)Università Politecnica delle Marche (Italy), ^(b)Università degli Studi di Macerata (Italy), ^(c)University of Córdoba (Spain)

Track Description: Quality control and inspection is a relevant part of modern production processes, which can be performed during different phases of production. The design of novel ML and DL methodologies that can be integrated in a Decision Support System (DSS) may improve the generalization performance by reducing the inter and intra-operator variability, and by mitigating any unwanted bias, which can influence the quality and productivity of the work and the repeatability of the results. The development of software and vision systems based on ML or DL techniques would allow to replicate human behaviour in manual operations of quality control, with the aim of improving defect detection, conformity assessment and the resulting commercial value of industrial products and materials. In the long term, this is reflected as an algorithm that will be integrated into a DSS to support the decisions of the human operator during the various phases of industrial processing, in order to speed up the production cycle and make quality control more robust and accurate.

Please be sure to select “​59UT6D” as main topic during the submission.

Co-Chairs: ^(a)Maryam Gallab, ^(a)Nabila Zrira, ^(b)Hafida Bouloiz, ^(a)Ibtissam Benmiloud

Affiliation: ^(a)Mines-Rabat School (Morocco), ^(b)ENSA-Agadir (Morocco)

Track Description: Artificial intelligence contributes today to many developments in the industry in the era of the 4.0 revolution. Artificial intelligence is more than a disruptive technology, it is a set of techniques (machine learning, deep learning, computer vision, natural language processing …) that enter into the daily life of the industry and whose impact is growing rapidly especially in maintenance activities and more specifically in Predictive Maintenance. Anticipating a material failure, foreseeing a service interruption, detecting a defect on a part is the purpose of Predictive Maintenance. Predictive Maintenance relies on weak signals or environmental elements that allow to anticipate a failure and therefore to trigger a targeted maintenance action. In this sense, Artificial Intelligence offers effective tools to address this issue. This session aims to share the most recent contributions in this area. Researchers and professionals are invited to present their work in the following or related fields: – Predictive Maintenance – Smart Maintenance – Industry 4.0 / Industry X.0 – Artificial intelligence (AI) – Modeling and Simulation – Lean Manufacturing

Please be sure to select “​JM392Y” as main topic during the submission.

Co-Chairs: ^(a)Tim Jeske, ^(b)Verena Nitsch

Affiliation: ^(a)ifaa – Institute of Applied Industrial Engineering and Ergonomics (Germany), ^(b)RWTH Aachen University (Germany)

Track Description: Human work is crucial for a successful development of companies. Today, it is strongly influenced by the opportunities and increasing spread of digitalization. Digitalization facilitates existing forms of work and collaboration, enables innovative approaches and is the basis for the use of artificial intelligence. Examples include the increased proportion of employees working from home during the corona pandemic, a human-robot collaboration which combines the individual strengths of human and robots as well as self-adapting assistance systems. A successful application of digitalization an artificial intelligence requires changes not only in technological aspects but also in organizational circumstances and personal aspects like development of qualifications and skills. The focus of “Artificial Intelligence for Supporting Human Work” is the innovative design of socio-technical systems for supporting human work by using digitalization and artificial intelligence.

Please be sure to select “​7266PV” as main topic during the submission.

Co-Chairs: ^(a)Raffaele Gravina, ^(b)Antonio Guerrieri, ^(c)Claudio Savaglio, ^(d)Francesco Cauteruccio

Affiliation: ^(a)(c)University of Calabria (Italy), ^(b)Italian National Research Council (CNR)-ICAR (Italy), ^(d)Polytechnic University of Marche (Italy)

Track Description: Cyber-Physical Systems (CPS) and Industrial Internet of Things (IIoT) technologies are key enabling factors for the creation of a connected value chain in Smart Factories. Integrated with other emerging paradigms like Digital Twins, Edge Computing and Blockchain, CPS and IIoT support advanced data sensing, transmission and analytics, thus fostering the creation of a Smart Factory ecosystem where humans, machines and products are seamlessly internetworked aiming at the maximum efficiency, effectiveness and safety. The way towards cognitive and autonomic Smart Factories necessarily goes through a multidisciplinary approach involving the latest advancements in the whole ICT landscape: the potentialities are huge as well as the challenges to face. As a consequence, the track aims at stimulating both practical and theoretical contributions focused on the integration of CPS and IIoT in the Smart Factory domain. Topics of interest include, but are not limited to: IIoT-based approaches for Smart Factories, Digital Twins and Digital Thread, Body Sensor Networks for Safety in Smart Factories, CyberSecurity, Privacy and Trust for Smart Factories, Development methodologies and simulation tools for Smart Factories, Smart Products and Business models for product-service hybrids, Edge-Cloud continuum for architecting Smart Factories, Cognitive Factories, Smart Factories Applications.

Please be sure to select “​L679GD” as main topic during the submission.

Co-Chairs: ^(a)Radu Godina, ^(b)Pedro Espadinha da Cruz

Affiliation: ^(a)(b)Research and Development Unit in Mechanical and Industrial Engineering (UNIDEMI), Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa (Portugal)

Track Description: Data mining techniques can be skilfully employed to improve the performance of manufacturing or industrial processes in which substantial volumes of structured information has to be processed. And the volume is ever growing. The main goal of data mining application in industry is to transform raw data into useful knowledge for decision making. Thanks to the new paradigm of Industry 4.0 and by employing information technology, this process becomes easier. Therefore, organizations could extract more benefits from information to propose more precise, effective, and applicable strategies and decisions in shorter periods of time. The technologies that emerge in this context offer an unprecedented capacity to reorganize production processes. The speed with which these transformations are taking place poses a formidable challenge for industrial and service stakeholders. The potential benefits that data mining applications can bring are widespread. As such, contributions from distinct areas of research are welcome. Researchers are encouraged to submit contributions that address numerous aspects of data mining applications in the Industry 4.0 context and its relationship with several bordering topics.

Please be sure to select “​770XLR” as main topic during the submission.

Co-Chairs: ^(a)Herbert Jodlbauer, ^(b)Katherine Gundolf

Affiliation: ^(a)(b)University of Applied Sciences Upper Austria (Austria)

Track Description: Leveraging data can help companies improve operations and increase profits. Incumbent manufacturers, for example, can use data to digitalize products and services, ‘clone’ physical activities in digital form, optimize manufacturing processes, and achieve digitalized production. However, to take full advantage of data analytics, companies must undergo a digital transformation and reconfigure their business models. The lack of a framework for leveraging data-driven process optimization leaves potential for business model innovation and value creation, delivery and capture untapped. The objective is to identify and develop data-driven methods/tools for sustainable business model innovation for incumbent manufacturers. Submissions should address business model innovations at the intersection of data-driven methods/tools and (economic, environmental, and social) sustainability. Different research methodologies (e.g., empirical studies, literature reviews, data mining, machine learning) can be applied.

Please be sure to select “​TUDTRK” as main topic during the submission.

Co-Chairs: ^(a)Maurizio Faccio, ^(b)Irene Granata

Affiliation: ^(a)(b)Università degli Studi di Padova (Italy)

Track Description: Among the recent technologies and methods constituting Industry 4.0, collaborative robots (or cobots) provide unique advantages. Introduced in the last decade, this new category of robots aims to physically interact with human operators in a shared environment thus, avoiding the need for the safety measures typical of traditional robotic systems. Workspace sharing, under proper condition, improves flexibility and reduces the cycle time. Moreover, the absence of safety fences allows to quickly change the layout, helping to implement a dynamic productive cell, capable of adapting to volume and model changes. However, the cobots’ ability to cooperate with human operators could decrease their efficiency if this interaction is not properly studied. When an operator works near an automatic system, safety is always a concern. Moreover, the needs and perceptions of the operator must be considered in terms of system performance, like productivity and production costs. This is in accordance with the human-centered direction of Industry 4.0, which introduces the concept of Operator 4.0, i.e., the augmentation of the human capabilities through these novel technologies. This open special session aims to gather the latest research achievements, findings, and ideas regarding collaborative robots, with particular attention to the influence of human factors. These include advanced technologies, mathematical models and methods, automation, management techniques and approaches; moreover, industrial case studies are also welcome.

Please be sure to select “​NBCUZB” as main topic during the submission.

Co-Chairs: ^(a)Fabio De Felice, ^(b)Antonella Petrillo

Affiliation: ^(a)(b)University of Napoli “Parthenope” (Italy)

Track Description: The increase in global competitiveness challenges the manufacturing market to integrate design, manufacturing, and product in order to improve quality and process. In today’s market, even large companies need flexibility. Thus, in recent years, the focus on smart manufacturing systems is pushing companies toward a new variety of highly specific technical solutions. These solutions are characterized by an integrated approach to manufacturing termed “digital manufacturing”. In fact, digital manufacturing systems often incorporate optimization capabilities to reduce time, cost, and improve the efficiency of most processes. Despite the recognition of importance for digital manufacturing, most organizations feel they lack the necessary capabilities. The digital revolution is now our “present” is not the future. There are many different tooling processes that digital manufacturing utilizes such as artificial intelligence, automation and robotics, additive technology, and human-machine interaction, IoT, etc. The metaverse has recently been added to these tools becoming an increasingly popular form of collaboration within virtual worlds. The tools characterizing digitization are destined to evolve and increase. In any case, these tools are unleashing innovations that will change the nature of manufacturing itself. Industry and academic leaders agree that digital manufacturing technologies will transform every link in the manufacturing value chain, from research and development, supply chain, and factory operations to marketing, sales, and service. This transformation is known as fourth industrial revolution, also referred to as Industry 4.0 but the real new trend refers to Industry 5.0, the future, already penetrating trend, of change processes directing towards closer cooperation between man and machine, and systematic prevention of waste and wasting including industrial upcycling. The aim of the track session is to host a selection of papers from researchers, academics, as well as practitioners providing significant insights in the context of Industry 4.0 towards Industry 5.0 to solve complex problems in the field of manufacturing planning, management, and control.

Please be sure to select “​V694UQ” as main topic during the submission.

Co-Chairs: ^(a)Erwin Rauch, ^(b)Manuel Woschank, ^(c)Corina Pacher, ^(d)Bernd Markus Zunk, ^(e)Mariaelena Murphy

Affiliation: ^(a)Free University of Bozen-Bolzano (Italy), ^(b)(e)Montanuniversität Leoben (Austria), ^(c)(d)Graz University of Technology (Austria)

Track Description: Mankind is currently facing major challenges that will radically change the way we live and work as well as our environment. The economy and society are subject to a permanent and hyper-dynamic change, that require transformational solutions that go beyond incremental innovation strategies. Therefore, business sectors need to adopt new mindsets, build new business models, and develop new technologies, actions defined as “breakthrough innovation”. Thereby, engineers play a critical role in finding technologically innovative solutions that balance economic competitiveness, environmental protection, and social acceptance towards facing grand challenges of the world, such as climate change, resource scarcity, energy transition, etc. The investigation and subsequent transparent and empirical-based operationalization of future educational needs and necessary transversal key competences for the engineers of tomorrow represent a key element of Engineering Education. Generally valid competences must be manifested for the entire field of Engineering Education to guarantee quality assurance, systematic development, competitiveness, and employability of future engineers. This track invites scientists as well as practitioners to share their experiences and innovative concepts from best practices and thus contribute towards Engineering Education 5.0.

Please be sure to select “​M6RV47” as main topic during the submission.

Co-Chairs: ^(a)Rui Pinto, ^(b)Gil Gonçalves, ^(c)Miroslav Žilka, ^(d)Udayanto Atmojo Zunk

Affiliation: ^(a)(b)SYSTEC-ARISE, Faculty of Engineering, University of Porto (Portugal), ^(c)Czech Technical University (Czech Republic), ^(d)Aalto University (Finland)

Track Description: Education is key to pursue strategic objectives in the 2030 Agenda for Sustainable Development, such as competitive manufacturing skills, social sustainability, manufacturing innovation ecosystems, resilient manufacturing, and an overall manufacturing fit for the digital age. Furthermore, Education contributes in supporting business and innovation along Digitalization, Green transition, increase of Resilience, and other relevant trajectories in Manufacturing. In the Industry 5.0 context, education focuses on humans: connect, transform and empower them to become the backbone of a strong manufacturing innovation community, and a propserous and inclusive society. On the one hand, human empowerment, by leveraging learning paths and educational programmes for students and professionals. On the other hand, connect and transform, to create the infrastructures and the learning experiences that enable individuals and organizations to network, skill, upskill and reskill. This track invites researchers and practitioners to share their experiences and innovative concepts related to education for transforming organisations, skill-driven learning, educational programmes for upskilling and reskilling, and strategies to improve the Industry 4.0 adoption and knowledge transfer. Topics of interest include: Innovative teaching and learning experiences; Emerging educational technologies; Learning Factories and Industry 5.0; Training and knowledge transfer approaches for professionals.

Please be sure to select “​9S7T33” as main topic during the submission.

Co-Chairs: ^(a)Steven Umbrello, ^(b)Carmela Guarascio

Affiliation: ^(a)TU Delft (The Netherlands), ^(b)University of Macerata (Italy)

Track Description: Technologies and Innovation have always been an engine of change into organizations and labour market. With the design and deployment of nano-bio-info-cogno technologies becoming evermore ubiquitous, their impacts can likewise be seen across a variety of human and organizational domains. Industry and engineering are a privileged place to study these changes, because they are the nexus for the development of those emerging and converging technologies. Because these impacts have been predicted and are manifesting themselves as being substantial and profound, the ethical deliberations on how to meet these challenges becomes just as significant. Moreover, they could address some challenges towards the relationships between emerging technologies and the sustainability and responsible innovation. This Open Track aims to address this lacuna by exploring research on how ethical theories and responsible innovation can bridge the gap in industry and engineering to meet these challenges. Researchers are invited to present papers on topics such as, but not limited to: • how to address the specific ethical issues that emerge in industry 4.0 systems and infrastructures • how to account for value change in sociotechnical systems over time • philosophical and technical explorations for embedding values into sociotechnical systems • design strategies particularly capable of aligning artificial intelligence systems with human values in the context of Industry 4.0 • design approaches and methodologies that can be adopted for value-alignment across engineering domains • Define the analysis of the impact of emerging technologies on labour market and organizational structure (ex. Agile, teal organizations, CSR and innovation…). • Relationships among emerging technologies and strategy of local development, with territorial implications.

Please be sure to select “​B893YU” as main topic during the submission.

Co-Chairs: ^(a)Michele Fiorentino, ^(b)Michele Gattullo, ^(c)Vito Modesto Manghisi

Affiliation: ^(a)(b)(c)Polytechnic University of Bari (Italy)

Track Description: Virtual, Mixed and Augmented Reality technologies (called also extended reality or XR) are evolving at very fast pace in terms of display quality and ergonomics, ease of use, interaction design and software tools and algorithms. XR applications will be ubiquitous, and they will deliver benefits in many fields, like maintenance, training, marketing, tourism, medicine and many others to come. However, there are still many research challenges to be addressed, like the lack of well established guidelines, human factors, usability, cybersickness, and social and cognitive aspects. This Special Track is intended to report on latest innovations and applications of Virtual, Mixed and Augmented Reality in the contex of Industry 4.0. Both practical and theoretical papers are welcome, as well as case studies and innovative multidisciplinary applications.

Topics of interest include, but are not limited to:

• Innovative Virtual, Mixed and Augmented Reality Applications,
• Case studies and user experience reports,
• AR\VR design guidelines and implementation of future standards,
• Human computer interface design, solutions and challenges,
• Performance comparisons and implications of traditional processes versus VR\AR counterparts,
• Innovative AR\VR devices, approaches and algorithms,
• Cybersickness and ergonomics in AR\VR.

Please be sure to select “​68C8QZ” as main topic during the submission.

Co-Chairs: ^(a)Ihsan Ullah, ^(b)Michael Madden, ^(c)Umair Ul Hassan, ^(d)Ali Intizar

Affiliation: ^(a)(b)(c)University of Galway (Ireland), ^(c)Dublin City University (Ireland)

Track Description: Machine learning sub-branch called deep learning (DL) brought a revolution in various sectors and is one of the key contributors in Industry 4.0. Majority of the industries are enthusiastic to use and adopt DL for Internet of things (DL on the edge). However, there are several concerns around this including privacy, cost, speed, reliability, security, networking, and trust in the systems that uses such models or approaches. One of the limitations in DL is its data hungry nature i.e. it needs more data for training to give good results. However, after GDPR and other laws, data sharing became a big concern. Therefore, Google in 2016 presented its Federated Learning (FL) approach that does not need data to be stored on a central location for training rather the data remains at its own premises, but the model parameters are shared to be trained remotely over the data and then the trained model is sent back to be globally aggregated on the server. It helps in securely and efficiently training a model without violating any privacy concerns. Several researchers and practitioners have expressed their interest in this area with the expectation of profound effect in the context of Industry 4.0. However, the topic is still in early stages and needs to be investigated. There is a still more to do in literature from both a theoretical and an empirical point of view. Therefore, this special track is dedicated to present the effectiveness and advantages of FL for advancing industrial IoT, smart manufacturing, and related fields.

Please be sure to select “​59UT6D” as main topic during the submission.

Co-Chairs: ^(a)Samuel Fosso Wamba, ^(b)Maciel M. Queiroz

Affiliation: ^(a)TBS Busines School (France), ^(b)FGV EAESP (Brazil)

Track Description: Generative artificial intelligence (Gen-AI) is currently attracting much attention worldwide because of its disruptive nature in transforming all industries. For example, it can build new content (i.e., text, images, equations, articles, music, etc.). Since the recent emergence of ChatGPT, all types of businesses have been shaken. This track aims to foment a debate about the challenges, benefits, and trends of Gen-AI in operations and supply chain management (O&SCM). Accordingly, we expect novel contributions to unlock the discussion involving scholars, practitioners, and policymakers about Gen-AI in O&SCM and how to add business value ethically. Topics of this track include, but are not limited to: • How can Gen-AI improve the efficiency of the O&SCM? • Is Gen-AI a powerful approach to making the O&SCM more resilient? • What is the role of Gen-AI in creating business value? • Which are the tradeoffs of Gen-AI in O&SCM? • What main benefits and risks can Gen-AI bring to the O&SCM?

Please be sure to select “​P7089H” as main topic during the submission.

Co-Chairs: ^(a)Luigino Filice, ^(b)Francesco Cicione

Affiliation: ^(a)DIMEG, University of Calabria (Italy), ^(b)Entopan S.r.l. (Italy)

Track Description: The traditional development models based on the richness and growth pushed by the industry are demonstrating their points of weakness looking at the recent events all over the World. Actually, it is not only a matter of philosophy but industry commonly shows a limited resilience and antifragility giving not adequate answers to society needs. Harmonic industry is a way to gain human centrality, defining models and technologies able to bring together value and wellbeing, in a perspective of “long-term enterprises”, those that act beyond short-term interests and generate widespread value, ensuring freedom, stability and social welfare. This requires a transition toward new and smartest models, allowing a sustainable innovation in which linear development model becomes circular, both in material treatment and social welfare terms. Technology is neutral vs society: the track is an opportunity to discuss its impact on the people belonging to the world community, today and tomorrow. Topics include, but are not limited, to: Harmonic innovation; Transition models; Green society and manufacturing; Industry development models; Circular innovation; Impact of technology on society; Smart factories; Smart society; Human wellbeing; Social responsibility.

Please be sure to select “​VLQMHU” as main topic during the submission.

Co-Chairs: ^(a)Fabio Fruggiero, ^(b)Giulio Paolo Agnusdei, ^(c)Maria Grazia Gnoni, ^(d)Sotirios Panagou

Affiliation: ^(a)(d)University of Basilicata (Italy), ^(b)(c)University of Salento (Italy)

Track Description: The technology driven progress of Industry 4.0 has emphasized the social dimension of the production. Digital technologies, in a service oriented approach, are adapting to worker’s need. New models for individualized human-machine interaction systems are developing for facing with new challenges. This is forcing a human centric approach that points on the sustainable resilience of smart operators, i.e. operators with «augmented» collaborative capabilities. In addition, the massive diffusion of digital technologies is forcing new way of interactions between humans and machines. Smart operators have to collaborate with automatic devices in a flexible, reliable, safe and inclusive work environment. One examples are collaborative robots (cobots), which are becoming popular in several industrial sectors from logistics to manufacturing. Cobots realize the highest level of adaptability with low investment cost. Their increasing diffusion is due to their high productivity and reliability as they are designed with a smarter collaborative perspective. The presence of cobot in a working environment requires a re-assessment of safety issues from risk analysis to risk control: thus, an integration of these issues is essential in the design and management of Industry 4.0 production environments. The emerging risk scenarios, due to the complexity of the task carried out by the operators as well as the dynamic interactions between operators and automatic systems, require new tools and dedicated approaches. Traditional ergonomics methods for designing and evaluating human-machine interactions needs to be updated. Operators’ physical, cognitive and sensorial skills (aged by time), smart human actions and perceptive capabilities have to be investigated in a pro-active approach. The objective of this proposed section is to collect contributions to share knowledge on design, control, safety, concerning the introduction of several digital technologies in a working environments by focusing on outlining human-machine interactions.

Please be sure to select “​5308TP” as main topic during the submission.

Co-Chairs: ^(a)Frederik Schulte, ^(b)Yaxu Niu

Affiliation: ^(a)Delft University of Technology (The Netherlands), ^(b)North University of China (China)

Track Description: Human-robot collaboration is widely considered one of the greatest challenges in the final steps of the 4th Industrial Revolution and an anticipated central question of the 5th Industrial Revolution. Order-picking in e-commerce warehouses is one of the examples in which human-robot collaboration is expected maintain important despite in the light of ongoing automation. While Industry 4.0 focuses on cyber-physical (production) systems and their potential to create self-organizing operations, Industry 5.0, following a recent position paper of the European Commission, places the wellbeing of the worker in the center of production process. Building on the foundations of Industry 4.0, this naturally leads the operations management of Industry 5.0 to collecting human (sensor) data and learning to interpret and integrate this information into existing objectives. In this way, it may turn robots and intelligent machines into caring colleagues for workers. However, related integrated research is still limited. This open track aims to attract innovative research related, but not limited, to the following topics: Innovative works integrating human-robot collaboration in operations management; (Wearable) sensor data models to analyze human stress levels and recovery; Machine learning approaches to let robots understand human behavior and their conditions; Integration of physiological human models and operations management approaches.

Please be sure to select “​L2793P” as main topic during the submission.

Co-Chairs: ^(a)Radu Godina, ^(b)Aurélien Bruel

Affiliation: ^(a)Universidade NOVA de Lisboa (Portugal), ^(b)Capgemini engineering (France)

Track Description: Industry 4.0 and Circular Economy (CE) are two concepts that has been described as two independent fields of research. Recently, studies have made attempts to bring these two concepts closer together and to study their overlaps. Based on these studies, Industry 4.0 would appear to have significant potential for deploying CE and accelerating the transition from a linear to a circular model in organizations. However, the literature still neglects how to implement various technologies of Industry 4.0, particularly at the level of the entire product life cycle, the supply chain or to promote inter-company relations such as in industrial and urban symbiosis networks to avoid cite just a few examples. Thus, contributions studying the relationships between these two concepts are encouraged, as are applications of Industry 4.0 technologies (Internet of things, big data, blockchain technology, artificial intelligence, machine learning, etc.) to different aspects of CE.

Please be sure to select “​4R5B8R” as main topic during the submission.

Co-Chairs: ^(a)Cecilia Silvestri, ^(b)Antonio Forcina, ^(c)Luca Silvestri, ^(d)Michela Piccarozzi

Affiliation: ^(a)(d)University of “Tuscia” (Italy), ^(b)University of Naples “Parthenope” (Italy), ^(c)University of Nicolò Cusano (Italy)

Track Description: Manufacturing has gone through many evolutions and times of dramatic innovations that have improved its capability to produce ever higher quality products at lower and lower costs. Epochs of these transformations are known as ‘industrial revolutions’ and each one of them has added great value to the way we manufacture and sell products through the implementation of new technologies and systems. The present day is not an exception. The manufacturing industry is currently going through a transformation as well (Fourth Industrial Revolution’ or ‘Industry 4.0). Material handling and logistics represent suitable application areas for Industry 4.0. Indeed, the integration of technologies such as Cyber Physical Systems (CPS), Internet of things (IoT), Augmented Reality (AR), and smart devices into logistics guarantees to enable a real-time tracking of material flows, enhanced transport handling, and higher safety. For example, through smart glasses and IOT technologies it is possible to deliver standard computer functions by a head mounted display, presenting visual information (e.g., textual, graphical, video) within a user’s field of view, assisting workers in adopting and retaining safe material handling techniques to reduce overexertion injuries. In this scenario, the track aims at stimulating scholars from different research fields to propose contributions aimed at highlighting and analyzing the benefits of using Industry 4.0 technologies in material handling and logistics.

Please be sure to select “​W93AP2” as main topic during the submission.

Co-Chairs: ^(a)Giovanni Mirabelli, ^(a)Vittorio Solina, ^(b)Emilio Jiménez

Affiliation: ^(a)DIMEG, University of Calabria (Italy), ^(b)Universidad de La Rioja (Spain)

Track Description: In recent years, interest and concern for the environmental, social and economic effects linked to the production and consumption of food have significantly grown. Agri-food supply chains usually face very complex challenges in achieving sustainable development. In this context, innovative solutions linked to the Industry 4.0 paradigm are rapidly spreading. The Internet of Things (IoT) enables the sharing of information in real-time and improves coordination between supply chain players. The blockchain technology is very promising for food traceability. Many other innovations deserve to be explored and debated. The emerging solutions should aim to make the entire supply chain more sustainable, improving agricultural production, food processing, packaging, distribution, consumption.

The main aim of this track is to stimulate a fruitful discussion about innovative solutions for making agri-food supply chains smart and sustainable. Topics of interest include, but are not limited to:

• Food production planning and control
• Blockchain and IoT in the food industry
• Food traceability systems
• Food circular economy
• Operations management in the food industry: optimization and simulation
• Smart strategies for food waste management
• Energy-efficient food processing technologies
• Artificial intelligence and machine learning in the food industry
• Eco-friendly solutions for food logistics
• Solutions for facing COVID-19 effects on agri-food supply chains

Please be sure to select “​U0105F” as main topic during the submission.

Co-Chairs: ^(a)Stefan Wagner, ^(b)Johannes Karder

Affiliation: ^(a)(b)University of Applied Sciences Upper Austria, Hagenberg (Austria)

Track Description: From a management perspective, integrated and dynamic planning within the scope of business (prescriptive) analytics are key factors for economic success. Especially in the era of smart manufacturing, an increasing digitalization and therefore integration of different sub areas is indispensable in order to utilize synergy effects. Exchanging information and decisions, i.e. coordinating, integrating, and analyzing different optimization and simulation systems, also considering changing information, is necessary to manage all manufacturing-related processes in a smart and efficient way. Nevertheless, the current state of the art on optimization and simulation methods for integrated and dynamic planning problems still cannot compete with their popular sequential and deterministic counterparts. Therefore, we cordially invite researchers and managers to propose their ideas and solution approaches concerning integrated and dynamic planning problems in order to support efficient smart manufacturing. Topics of interest include, but are not limited to: integration of multiple manufacturing and related (e.g. logistics) planning problems, objectives or decisions; integration of prescriptive analytics into smart manufacturing and logistics planning; integration of dynamic problem models, optimization algorithms, machine learning approaches, and simulation-based approaches into one solution framework; evaluation criteria for performance assessment and analytics; (conceptual) solution frameworks for dynamic optimization and prescriptive analytics.

Please be sure to select “​85596W” as main topic during the submission.

Co-Chairs: ^(a)Giuseppina Ambrogio, ^(b)Luigino Filice, ^(c)Romina Conte

Affiliation: ^(a)(b)(c)DIMEG, University of Calabria (Italy)

Track Description: Evolving industry needs and shorter product life cycle demand for new methods and services from production technologies and facilities. Manufacturing try to innovate themselves with a synergic use of creativity and technologies. Concepts of intelligent control of manufacturing system (extended to the whole supply chain), advanced manufacturing, hybrid manufacturing and sustainability are widely diffused and have changed the way to design the manufacturing processes. These new paradigms represent the way for making industry more competitive. According to these new approaches, papers based on the application of such methods as welcome.

Please be sure to select “​R377FF” as main topic during the submission.

Chair: Eric Kaigom

Affiliation: Frankfurt University of Applied Sciences (Germany)

Track Description: Immersive technologies are permeating society and industry at a fast pace. However, the decentralized interconnection of virtualized physical ecosystems combined with multi-access edge computing, cognitive digital twins, swarm learning, blockchain, as well as next generation semantics and goal-driven wireless communication have together the potential to generate new revenues and value streams. Collective and pervasive intelligence, privacy-aware contents and smart transactions, itinerant robotized presence, and mobile workspaces could offer new personal and professional opportunities for self-fulfillment and competitive advantages to citizens and factories and thereby deeply impact society and industry in terms of e.g. global inclusion, parallel intelligence and ambient robotized automation, co-innovation, resilience, and sustainability. This track aims at identifying current advances and future challenges as well as discussing opportunities of the Metaverse as a mediator and catalyzer between society, academia, and industry regardless of geographical locations.

Please be sure to select “​78IK4L” as main topic during the submission.

Co-Chairs: ^(a)Tânia Daniela Felgueiras de Miranda Lima, ^(b)Pedro Miguel de Figueiredo Dinis Oliveira Gaspar, ^(c)Joel Marques Alves

Affiliation: ^(a)(b)(c)University of Beira Interior (Portugal)

Track Description: The work environment in the Smart Factories will imply a paradigm shift with regard to existing work systems. The introduction of new technologies on the shop floor and in management will result in new ways of managing production systems and human resources, but also in the area of Occupational Health and Safety. It will be necessary to define new ways of preventing occupational risks that will emerge in the new work reality. Managing more complex work environments supported by technology will be a challenge for the ageing workforce, which is neither familiar with new technologies nor comfortable with human-technology interaction. It is therefore important to find strategies to ease the transition of these workers into the industrial environment of the future by promoting safe and healthy work conditions. New technologies can be used to create Occupational Health and Safety monitoring systems, as a facilitator for a more ergonomic work environment, and also as a facilitating strategy for human-technology interaction and collaborative work. This approach will make it possible to use technology for the benefit of the worker and refocus the factories of the future on the human factor.

Please be sure to select “​D6W52K” as main topic during the submission.

Co-Chairs: ^(a)Kamar Zekhnini, ^(b)Abla Chaouni Benabdellah

Affiliation: ^(a)Moulay Ismail University, ENSAM (Morocco), ^(b)Rabat Business School, International University of Rabat (Morocco)

Track Description: In an increasingly uncertain and highly challenging market, adopting operation management tools and industry 4.0 technologies has been recognized as a necessity for enhancing supply chain resilience. Operations management is the administration of business procedures inside an organization to achieve the best degree of efficiency achievable. It is concerned with transforming materials and labor as effectively as feasible into goods and services to maximize an organization’s profit. Besides, Industry 4.0 technologies, including artificial intelligence, the Internet of Things (IoT), and advanced analytics, offer additional opportunities for organizations to improve their supply chain operations. These technologies can help businesses optimize their processes, improve supply chain visibility, and enhance their ability to predict and respond to disruptions. Therefore, by adopting both operation management paradigms and industry 4.0 technologies, organizations can create more robust and resilient supply chains. This approach enables businesses to identify and mitigate risks, respond quickly to disruptions, and maintain continuity in their operations. Therefore, research in this sector remains largely unexplored. Thus, implementing operation management theories using industry 4.0 technologies throughout the supply chain necessitates many efforts on multiple fronts. Based on these premises, the aim of this track session is to host both theoretical and methodological studies dealing with operations management and industry 4.0 for supply chain performance.

Please be sure to select “​N92J48” as main topic during the submission.

Co-Chairs: ^(a)Stefano Saetta, ^(b)Valentina Caldarelli

Affiliation: University of Perugia (Italy)

Track Description: The manufacturing world will face increasingly intense competition in terms of both productivity and environmental sustainability. It is interesting to investigate how to combine both aspects today, where a lot of data is available, and a trade-off must be made between short-term and long-term goals. Performance evaluation can involve, for example, the introduction of new manufacturing techniques (such as soft automation, new image recognition techniques, 3D printers, etc.), lean and green practices, circular economy, and resilience. The main focus will not be so much on the techniques themselves, but rather on the model developed for evaluation (the management of performances, the design of performances dashboard, the use of simulation techniques, etc.).

Please be sure to select “​9936FW” as main topic during the submission.

Chair: ^(a)Florian Sobieczky, ^(b)Ivo Bukovsky

Affiliation: ^(a)Software Competence Center Hagenberg (SCCH) (Austria), ^(b)University of South Bohemia (Czech Republic)

Track Description: Physics Informed Machine Learning is concerned with the problem of integrating existing physical knowledge bases into the context of predictive analysis. Knowledge bases of industrial systems, such as the engineering facts of a machine or statistical results about a service invention are ubiquitous. Embracing them in the framework of machine learning has only gained popularity after advanced AI techniques such as deep learning have become a standard tool in Industry 4.0. Namely, as much as their power is improving a multitude of applications in the form of more accurate predictions, as data hungry they. E.g., if the training data set of a purely data driven model in a supervised learning model for anomaly detection lacks the necessary amount of sample points, the performance in terms of accuracy will be poor. Also, the lack of interpretability of complex machine learning models forbids to ‘learn from the AI’, even after it has been trained sufficiently well to provide reasonable predictive improvements. Physics Informed Machine Learning helps, by taking the burden of learning the known facts off from the training of learning algorithms, and by providing a theoretical context for explaining the machine learning models used. All contributions in this context are welcome.

Please be sure to select “​ST2483” as main topic during the submission.

Co-Chairs: ^(a)Michael Affenzeller, ^(b)Lukas Fischer, ^(c)Roxana Holom, ^(d)Kaifeng Yang

Affiliation: ^(a)(d)FHOOE, Hagenberg (Austria), ^(b)Software Competence Center Hagenberg (Austria), ^(c)RISC Software GmbH, RISC (Austria)

Track Description: Prescriptive analytics makes use of scientific disciplines like machine learning, simulation and optimization to analyze and optimize the effects of various options for action on a result. A profound and comprehensive prescriptive analytics is built on the results of descriptive, diagnostic, and predictive analytics. Therefore, the prescriptive analytic research topics demands various methods and algorithms from data mining, statistics, mathematics, machine learning, optimization algorithms, decision making, and operations research. In real-world applications, existing techniques in this field still face challenges in terms of explainability/interpretability of prediction models, dynamic optimization in data streams, robust/preference-based optimization, multi-criteria decision making, and visualization in high dimensional spaces.

This open track is dedicated to discovering and solving the current challenges and collecting review papers with new scientific innovations or practical applications of prescriptive analytics in the production field. Both practical and theoretical papers are welcome. Topics in this open track include, but are not limited to:

• Explainable/Interpretable machine learning
• Surrogate/White-box models
• Single-/multi-/many- objective (meta-)heuristic optimization
• Preference-based/Robust/Dynamic/ Surrogate-assisted (large-scale) optimization
• Multi-criteria decision making
• High-dimensional visualization

Please be sure to select “​XC7423” as main topic during the submission.

Chair: ^(a)Foivos Psarommatis, ^(b)Victor Azamfirei

Affiliation: ^(a)University of Oslo (Norway), ^(b)Mälardalen University (Sweden)

Track Description: The increase in global competitiveness challenges the manufacturing market to integrate design, manufacturing, and product in order to improve the quality of both the product and the process. In today’s market, even large companies need flexibility and also need to increase the level of sustainability of their systems and processes. Thus, in recent years, the focus on smart manufacturing systems is pushing companies toward a new variety of highly specific technical solutions. In the area of quality management is Zero Defect Manufacturing (ZDM) that combines all the best features of traditional quality management methods but also incorporates all the new digital technologies that Industry 4.0 and 5.0 can offer. These solutions are characterized by an integrated approach to manufacturing termed “digital manufacturing”. In fact, digital manufacturing systems often incorporate optimization capabilities to reduce time, cost, and improve the efficiency of most processes. Despite the recognition of importance for digital manufacturing, most organizations feel they lack the necessary capabilities. The digital revolution is now our “present” is not the future. There are many different tooling processes that digital manufacturing utilizes such as artificial intelligence, automation and robotics, additive technology, and human-machine interaction, IoT, etc. The metaverse has recently been added to these tools becoming an increasingly popular form of collaboration within virtual worlds. The tools characterizing digitization are destined to evolve and increase. In any case, these tools are unleashing innovations that will change the nature of manufacturing itself. Industry and academic leaders agree that digital manufacturing technologies will transform every link in the manufacturing value chain, from research and development, supply chain, and factory operations to marketing, sales, and service. This transformation is known as fourth industrial revolution, also referred to as Industry 4.0 but the real new trend refers to Industry 5.0, the future, already penetrating trend, of change processes directing towards closer cooperation between man and machine, and systematic prevention of waste and wasting including industrial upcycling. The aim of the track session is to host a selection of papers from researchers, academics, as well as practitioners providing significant insights in the context of Industry 4.0 towards Industry 5.0 to solve complex problems in the field of manufacturing planning, management, and control.

Please be sure to select “​Z27H19” as main topic during the submission.

Chair: Abbas Dashtimanesh

Affiliation: KTH Royal Institute of Technology (Sweden)

Track Description: The transition towards digitalization in the maritime industry is expected to reflect the increasing societal demands to decrease the environmental impact of shipping. Digitalization and Decarbonization are currently the most transformative forces in shipping, and the two topics are entwined – with digitalization enabling the decarbonization of shipping in several important ways including ship design and operation. Moreover, it has been highlighted that a life-cycle approach that accounts for environmental sustainability requires digitalization from smart design to smart operation. Accordingly, the main objective of this track is to highlight the ongoing research studies concerning Intelligent Ship Design and Operation in line with Industry 4.0. Industry 4.0 is urging various stakeholders to shift their traditional design philosophy and research studies to smart and digital solutions. It is expected that application of Artificial Intelligence in ship design will reduce computational efforts and complexity level of design space for obtaining optimal solutions because of demanding environmental standards. In the next decade, digitalization of ship design and operation through implementation of Artificial Intelligence and development of technologies such as digital twin will revolutionize the maritime sector. The authors addressing this demanding field of research are encouraged to submit their original papers to this track.

Please be sure to select “​238JG8” as main topic during the submission.

Co-Chairs: ^(a)Giovanna Rotella, ^(b)Maria Rosaria Saffioti

Affiliation: ^(a)LUM University (Italy), ^(b)University of Calabria (Italy)

Track Description: In order to fill the gap between engineering and medicine, combining the design and problem-solving skills of engineering with medical biological sciences, it is necessary to collect technical and industrial know-how to manufacture biomedical products featured with effective surface modifications improving the overall product performance. Surface characteristics determine components’ performance and functionalities by various processes able to locally modify the surfaces giving properties that the bulk material does not naturally possess. Surface characteristics of biomedical implants, determined experimentally or by computer simulations, help to understand relationships between structure, function, process parameters, properties changes and degradation of the devices. This track is endorsed by the Italian Manufacturing Association – AITeM.

Please be sure to select “​52I2RE” as main topic during the submission.

Co-Chairs: ^(a)Vincenzo Corvello, ^(b)Gabriele Zangara

Affiliation: ^(a)University of Messina (Italy), ^(b)University of Calabria (Italy)

Track Description: Social sustainability in the supply chain implies striving to ensure the well-being of the whole linked community, from the end customer up to the last supplier. It requires that everybody, in the supply chain, pays particular attention to the choice of its supplier or customer, considering also social and ethical issues. The economic and environmental aspects of sustainability are driven by specific regulations, unlike the social issue which is more or less voluntary and related to the initiatives of managers and entrepreneurs. However, the need to become socially sustainable is increasingly felt by many different stakeholders and companies are aware of the need to pay more attention to the well being of people, designing strategies inspired by ethics. The track is an opportunity to discuss the future of social sustainability in terms of impact, strategies, awareness, people, to “meet the needs of the present without compromising the ability of future generations to meet their own needs”, to increase industry and community wellbeing. Topics include, but are not limited, to: Social sustainability; Ethics; Supply chain management; Green supply chain; Supply chain strategies; Ethical strategies; Human wellbeing; Social responsibility; Corporate social responsibility CSR); Social Sustainability and Innovation; Environmental and Social Governance (ESG); Entrepreneurship and social sustainability in supply chains.

Please be sure to select “​S8918T” as main topic during the submission.

Co-Chairs: ^(a)Florian Bachinger, ^(b)Jan Zenisek

Affiliation: ^(a)(b)University of Applied Sciences Upper Austria (Austria)

Track Description: The increasing application of machine learning (ML) models in modern industrial settings raises more and more challenges along the lifecycle of a model: from training to deployment, execution, and adaptation. Due to the nature of fully automatized, just-in-time production lines, machine learning models not only need to be accurate, but also quick to serve predictions. Additionally, the long service life of industrial plants and associated high costs require dependable, trustworthy models that are easy to maintain. Changing parameters, e.g., production schedules, machine tooling, or material degradation, demand continuous monitoring of model performance and respective adaption. Such issues need to be addressed by the software components of a comprehensive industrial machine learning solution. In this track we invite researchers to present software design aspects of new solutions to these challenges and lessons learned from real-world applications to avoid common pitfalls. For contributions to this track, topics of interest include, but are not limited to: • Developments in ML pipelines, and workflows to address open issues in industrial application scenarios. • Distributed processing for large-scale industrial machine learning applications (parallelization, containerization, orchestration). • Application of established software engineering concepts for ML (CI/CD, unit testing). • Validation of a model’s functional safety, prior to deployment (suitable execution environment, trustworthiness, interpretability). • Software concepts for the interaction of simulation models and ML models in industry.

Please be sure to select “​BL834S” as main topic during the submission.

Co-Chairs: ^(a)Erwin Rauch, ^(b)Susanne Vernim, ^(c)Matthias Wolf

Affiliation: ^(a)Free University of Bozen-Bolzano (Italy), ^(b)Technical University Munich (Germany), ^(c)Graz University of Technology (Austria)

Track Description: The last few years have been marked by the challenge of greater resilience in value chains and by a shift in values in society toward the long-term sustainable and green production and distribution of goods. This track collects contributions that present methods, technologies or concepts on how to make our factories and production processes more sustainable and resilient. Contributions regarding sustainability can be focused on the ecological dimension of sustainability as well as on human-centric approaches to enhance socially sustainable manufacturing.

Co-Chairs: Abdulrahman Nahhas, Andrey Kharitonov, Matthias Pohl, Klaus Turowski

Affiliation: Otto von Guericke University Magdeburg (Germany)

Track Description: The concepts of the smart industry represent the future form of industrial network, in which the physical elements of manufacturing environments are coupled with IT services to achieve a cyber-representation of the real manufacturing environments. Although achieving a cyber-physical system promises a manufacturing environment with plenty of competitive advantages from a monitoring point of view, extracting knowledge from the massive amount of collected data is an extremely difficult and costly task. Therefore, the proposed new technologies in the context of industry 4.0 challenge the current operational practices in different manufacturing and service sectors. In addition, the conventional IT techniques that are employed for addressing industrial problems are usually problem-specific, lack flexibility, and highly standardized. However, cutting-edge technological advances are increasingly being based on the cloud after the introduction and establishment of the cloud computing model. Industrial environments exhibit naturally high structural complexity since an enormous number of factors related to market changes, heterogeneity of customer demands, distributed supply chain, and internal operating procedures are highly dynamic. To comprehensively size the potential of interconnected manufacturing and service environments, the adoption of new cloud IT technologies such as optimization, Machine Learning, Big Data solution, and hybrid techniques is required. From a research perspective, these techniques require extensive research efforts to achieve the required level of maturity and a low computational effort to be used in different contexts for addressing manufacturing problems in real time. This track welcomes research and application efforts that address the adoption of cloud computing technologies and their role in enabling industry 4.0 concepts.

Please be sure to select “​F72XE7” as main topic during the submission.

Co-Chairs: ^(a)Eleonora Bottani, ^(b)Barbara Bigliardi, ^(c)Vincenzo Corvello

Affiliation: ^(a)(b)University of Parma (Italy), ^(c)University of Messina (Italy)

Track Description: In recent years, complexity and requirements in the manufacturing industry have gradually increased. Among others, factors like demand for highly individualized products, growing international competition, and shortened product life cycles challenge the survival of companies. At the same time, the technological progress characterizing the last years has opened up a range of new business potentials and opportunities. Concepts like digitalization, internet of things and cyber-physical systems are gaining momentum not only in the manufacturing context but also in other business environments. In 2011, Germany launched the so-called “Industrie 4.0” initiative as part of its technological strategy, presenting the idea of a digitally integrated industry. Industry 4.0 encompasses numerous technologies and associated terms, such as Cyber Physical Systems (CPS), Radio Frequency Identification (RFID), Enterprise Resource Planning (ERP), Internet of Things (IoT), cloud-based manufacturing, and social product development. The term Industry 4.0 has become a key concept in many contexts, ranging from the academic one to modern manufacturing, but still, there are a lot of misinterpretations about its meaning. In particular, as stressed by various scholars , the exponential growth of digital technologies has caused significant improvements in many business processes, playing a significant role also in the supply chain management (SCM). The introduction of Industry 4.0 into manufacturing has many impacts on the whole supply chain. Collaboration between suppliers, manufacturers and customers is crucial to increase the transparency of all the steps from when the order is dispatched until the end-of-life of the product. Furthermore, due to the introduction of digitalization and automation of processes, the whole supply chain structure changes. However, digitization has also lead to disruptive changes that affect significantly supply chains and will continue triggering changes in the future. In order to understand the opportunities and possibly threats from the introduction of these new technologies, it is therefore important to analyze the impact of Industry 4.0 on the supply chain as a whole, highlighting the challenges, risks and opportunities in SCM as a consequence of digitization, as well as how Industry 4.0 impact on SCM and viceversa. Based on these premises, the aim of this track session is to host a selection of papers from researchers, academics, as well as practitioners providing significant insights in the context of SCM 4.0.

Topics may address, but are not limited to, the following key areas:

• Advanced tracking and tracing technologies and applications in the supply chain context
• Analysis of diffusion and digital transformation in supply chains
• Concepts of data security for supply chain
• Effects of Digitization & automation on efficiency, effectiveness, flexibility in supply chains
• Effects of product and service virtualization on supply chain management
• Impacts of autonomous transportation technologies on supply chains
• Impacts of digitization on SC decision making, leadership practices, management principles
• Innovative smart services for supply chain management
• Innovative supply chain models based on big data analytics
• Internet of things (IoT) and its impact on supply chain management
• Maturity models for digital transformation of supply chain management

Please be sure to select “​5G6B28” as main topic during the submission.

Chair: ^(a)Renxi Qiu, ^(b)Francesco Longo, ^(b)Vittorio Solina, ^(c)Kandarp Amin, ^(d)Sebastian Andraos, ^(e)Andreas Gavrielides

Affiliation: ^(a)University of Bedfordshire (UK), ^(b)Cal-Tek S.r.l. (Italy), ^(c)TWI Ltd (UK), ^(d)HAL Robotics (UK), ^(e)eBOS Technologies Ltd (Cyprus)

Track Description: Manufacturing as a Service (MaaS) is a distributed system of production in which resources are offered as services. It is an essential step towards the circular economy and industrial 5.0 for more distributed, diverse, inclusive, and resilient manufacturing processes. By sharing data, software, and services, manufacturers could access distributed providers to implement their personalized manufacturing processes. Meanwhile, 6G is envisioned to unleash the potential of smart connectivity for secure, resilient and sustainable development of future distributed systems. We believe MaaS will be the frontier of the vision that will be empowered by 6G. By combining the 6G and MaaS, a new framework to deliver the Green Deal and Industry 5.0 will be enabled from innovative business ecosystems, value chains, and supply chains. The session is designed to discuss and promote digital skills, use cases and the ecosystems of 6G enabled MaaS by steering digital transitions through human-centred technologies and innovations. Furthermore, the session will include experiences learned from various use cases applied across cyber physical systems and the required physical and virtualized infrastructure that enhance sharing, reusing, re-manufacturing, re-sale, and recycling for products based on the potential. Finally, the session will also discuss how to scale 6G technology into MaaS seamlessly by integrating Cloud computing and communication concepts into native manufacturing processes and how various communities work together to develop these novel solutions. This special session is endorsed by H2020 5G enhanced robot autonomy research project.

Please be sure to select “​W7151U” as main topic during the submission.

Chair: ^(a)Francesco Longo, ^(a)Antonio Padovano, ^(b)David Romero, ^(c)Johan Stahre, ^(d)Thorsten Wuest

Affiliation: ^(a)DIMEG, University of Calabria (Italy), ^(b)Tecnológico de Monterrey (Mexico), ^(c)Chalmers University of Technology (Sweden), ^(d)West Virginia University (USA)

Track Description: While the Industry 4.0 is idolizing the potential of an artificial intelligence embedded into “things”, it is neglecting the role of the human component which is still indispensable in different manufacturing activities. The 4th Industrial Revolution is inevitably changing not only what the human operators do and how they do it, but also who they are. After generations of operators that keep pace with the first three industrial revolutions, the Operator 4.0 (O4.0) came up as a new concept in the Industry 4.0 framework. Since debate around the O4.0 is still emerging, the definitions and applications that can be found in the recent literature are neither yet completely comprehensive nor exhaustive but represent good starting points for discussion. This track focuses on human-technology integration and collaboration aiming at demonstrating new ways factory workers and robots, automation, and artificial intelligence can operate in harmony to increase productivity, quality, and performance on the shop floor as well as work satisfaction and safety in the workforce.

Please be sure to select “​A7YR51” as main topic during the submission.