Co-Chairs: ^(a)Paola Serena Ginestra, ^(b)Antonio Piccininni, ^(c)Ali Gokhan Demir

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

Track Description: Biomanufacturing 4.0 is expected to replace traditional biomanufacturing production models with a new approach derived from Industry 4.0. This involves integrating the physical and digital in biomanufacturing, where all the systems and equipment in the biomanufacturing process are connected digitally, and technologies such as artificial intelligence (AI) or machine learning help to improve the critical process parameter during biomanufacturing. The digital transformation of bioprocess development is also known as Bioprocessing 4.0. Using the internet of things, digital biomanufacturing creates a network of data sources, materials, equipment, and industry representatives. This creates a competitive advantage, increases productivity, and boosts the value of biomanufacturing.

Please be sure to select “Biomanufacturing 4.0” as main topic during the submission.

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

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

Track Description:CyberPhysical 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 “CPS and IIoT for enabling Smart Factories” 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 “Data mining role in Industry 4.0” as main topic during the submission.

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

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 “Design and Management of Cobot Systems for Smart Manufacturing” 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 “Digital Manufacturing: towards Industry 5.0 – The future is already here” as main topic during the submission.

Co-Chairs: Fei Tao

Affiliation: Beihang University (China)

Track Description: With the rapid development of the I4.0 enabling technologies (Internet of Things, Cloud Computing, Big Data Analytics, and Artificial Intelligence, the Smart Manufacturing era has arrived. New trends and challenges are being traced thanks to the cyber-physical fusion of production technologies and software tools heading towards smarter, reliable and efficient manufacturing systems. In this context, the Digital Twin is defined as a high-fidelity digital mirror model of manufacturing resources that – combined with new technologies – allows to better accomplish complex tasks. This track collects review papers and application papers examining the background, latest research, and application models for digital twin technology in manufacturing, and aims at showing how the digital twin can be central to a smart manufacturing process. Topics of interest include, but are not limited to:

• Digital Twin Applications
• Digital Twin Driven Prognostics and Health Management
• Cyber-Physical Fusion in Digital Twin Shop Floor
• Digital Twin and Cloud, Fog, Edge Computing
• Digital Twin and Big Data
• Digital Twin and Virtual/Augmented/Mixed Reality
• IoT in Digital Twin-Based Cyber-Physical Systems
• Service-oriented Smart Manufacturing
• Manufacturing Service Management and Optimization
• Digital Twin Driven Product Design/Manufacturing/Service

Please be sure to select “​Digital Twin-Driven Smart Manufacturing” as main topic during the submission.

Chair: Giovanni Berselli

Affiliation: University of Genoa (Italy)

Track Description: Within the Industry 4.0 framework, Factories of the Future will have to be smart and green:

• SMART: the fierce competition within modern globalized market requires high performance, reconfigurable, adaptive and evolving factories based on robotic technologies;
• GREEN: it is necessary to reduce factories ecological footprint achieving a more efficient use, at factory level, of material and energy resources.

Industrial Robotics is then the strategic technology needed to achieve the performance necessary to keep the European Manufacturing at a leading edge. Unfortunately, industrial robotics in its current state of the art is intrinsically energy intensive, and its massive adoption compromises factories sustainability, not only in terms of ecological footprint but also in terms of economic costs, associated to the high, and always increasing, energy prices. The Un-sustainability of industrial robotics is the major cause for missing important and strategic opportunities in many sectors of the whole EU manufacturing. While it is easy to understand and well explained how Industrial Robotics can add competitive advantage and manufacturing performance improvements, it is often omitted and underestimated why the adoption of Industrial Robotics technology is not as widespread as it should be. Within this scenario, the aim of this Open Track is to provide an overview of recent advances in Engineering Methods and Tools for the Eco-Design and the Eco-Programming of robotized / automated production plants. Areas include:

1. New hardware technologies to minimize power consumption and to improve the use of renewable energy sources at factory level;
2. eco-efficient design and eco-efficient programming and scheduling of robotic production systems;
3. sustainability optimization of production processes;
4. lifecycle assessment of both environmental and economic costs linked with co-evolving products, processes and robotic production systems.

Please be sure to select “​Eco-Design Methods and Tools for Factory 4.0” as main topic during the submission.

Chair: Foivos Psarommatis

Affiliation: University of Oslo (Norway)

Track Description: Zero-defect manufacturing (ZDM) is gaining a vast amount of attention and interest from both research communities and industry, and is considered by both researchers and the industry as a viable replacement for the traditional QI methods such as Lean Production and Six Sigma. This is because ZDM utilizes all the different disruptive Industry 4.0 digital technologies, which can provide almost infinite capabilities. The advancements in Industry 4.0 technologies made the achievement of Zero-defect manufacturing possible. ZDM is not one method but rather a toolbox for decreasing and mitigating failures within manufacturing processes and “to do things right the first time”. ZDM covers both product and process quality. This concept had only partially been implemented so far due to many technological and economic limitations that restricted its rollout. For instance, the equipment required for data recording used to be very expensive and companies did not invest in it. However, the landscape has changed. Nowadays, increased computing power and data storage, significantly reduced sensor prices, combined with new digital technologies have made the implementation of the concept of ZDM easier than ever before. On one hand, the evolution of Industry 4.0 digital and automation technologies, such as intelligent machines, IIoT, digital and cognitive twins, AI, etc. has allowed responses to unexpected events and disruptions to become smarter and faster. On the other hand, the availability of the large volumes of data needed for the development of machine learning-based quality control strategies has allowed “industrialized” AI to work properly within factories and across global value chains. This special session addresses the technologies, methods, paradigms and their applications in factories of Industry 4.0.

Please be sure to select “​Emerging technologies and paradigms for achieving Zero Defect Manufacturing” as main topic during the submission.

Co-Chairs: ^(a)Erwin Rauch, ^(b)Manuel Woschank, ^(c)Corina Pacher, ^(d)Monica Ciolacu

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

Track Description: Industry 4.0 and the Fourth Industrial Revolution have greatly changed factories and increased the adoption of new emerging technologies. This change has far-reaching consequences that are not limited to the production environment. One example is the education sector, which is under enormous pressure to produce the profiles of technicians and engineers required by the labor market. It is necessary to develop new forms, structures and methods for a modern and future-oriented education and training around Industry 4.0 competencies. The specialists of the future must be qualified in new technologies and prepared for their introduction and implementation in the companies. This track invites scientists as well as practitioners to develop innovative concepts and share their experiences from best practices and thus contribute towards Engineering Education 4.0.

Please be sure to select “​Engineering Education 4.0” as main topic during the submission.

Chair: Steven Umbrello

Affiliation: Delft University of Technology (The Netherlands)

Track Description: 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 domains. Industry and engineering are no less effected, and in many cases serve as the vanguard of these changes given their privileged place as 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. This Open Track aims to address this lacuna by exploring research on how ethical theories 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

Please be sure to select “​Ethics and Ethical Engineering in Industry 4.0” as main topic during the submission.

Chair: Florian Sobieczky, Manuela Geiß, Salma Mahmoud, Anna-Christina Glock

Affiliation: Software Competence Center Hagenberg (SCCH) (Austria)

Track Description: Explainable artificial intelligence is concerned with the problem of the loss of model interpretability under an increase of model sophistication. Folklore wisdom claims that not being able to assign geometric characterizations to model parameters is the inevitable cost for any increase in accuracy. This hypothesis primarily rests on the number of parameters of increasingly deep learning models’ parameters becoming unpractically large. However, the spell can be broken by choosing model architectures which — however large — will have parts (e.g. layers …) fulfilling dedicated purposes. Not leaving it all to the self-organisation of the neural net during training, but employing third hand knowledge (physical, or statistical) leaves less matter to be explained by the black box. In manufacturing, the high demand for accountability of the machine learning predictions naturally pushes practitioners to using AI sparingly – only as much is necessary to complete the picture beyond the theoretically known process. The session is therefore dedicated to advanced AI machine learning models specifically applied to the interely unexplained, ‘magic’ part of the given data while going hand-in-hand with a conventional, classic ‘base’ model for the other (explainable) part.

Please be sure to select “Explainable Artificial Intelligence in Industry” as main topic during the submission.

Chair: Michele Fiorentino

Affiliation: 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 “Extended Reality for Industry 4.0” as main topic during the submission.

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

Affiliation: ^(a)(b)National University of Ireland Galway (Ireland), ^(c)Dublin City University (Ireland), ^(d)National University of Ireland Maynooth (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 “Federated Learning for Industry 4.0 and Smart Manufacturing” 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: Recent experiences allow to overcome the traditional development models based on the richness and growth pushed by the industry. 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. 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

Please be sure to select “​Harmonic industry and transition paradigms for circular innovation” as main topic during the submission.

Chair: Tim Jeske

Affiliation: Institute of Applied Industrial Engineering and Ergonomics (Germany)

Track Description: Human factors are crucial for a successful development of enterprises. Today, this development is strongly influenced by the opportunities and growing spread of digitalization. Digitalization facilitates existing forms of work and collaboration and enables innovative approaches. Examples for both are the increased proportion of employees working from home during the corona pandemic as well as the human-robot collaboration which combines their individual strengths. Successful digitalization requires changes not only in technological aspects but also in organizational circumstances and personal aspects like development of qualifications and skills. Human Factors in Digitalized Value Creation focuses on innovative design of human work and socio-technical systems by using digitalization.

Please be sure to select “​Human Factors in Digitalized Value Creation ” as main topic during the submission.

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

Affiliation: ^(a)School of Engineering – University of Basilicata (Italy), ^(b)(c)Department of Engineering for Innovation, University of Salento (Italy), ^(b)Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology

Track Description: The technology driven progress of Industry 4.0 has emphasised the social dimension of the production. Digital technologies, in a service oriented approach, are adapting to worker’s need. New models for individualised human-machine interaction systems are developing for facing with new chalenges. 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 realise the highest level of adatability with low investement 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 approches. Traditional ergonomics methods for designing and evaluting 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 “​Human-machine interactions towards a sustainable Industry 4.0 production environment” 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 “​Human-Robot Collaboration in Operations Management: From Industry 4.0 to Industry 5.0” as main topic during the submission.

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

Affiliation: ^(a)(c)(d)University of “Tuscia” (Italy), ^(b)University of Naples “Parthenope” (Italy), ^(e)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). This present revolution takes advantage of recent developments in internet connectivity and the proliferation of new devices such as mobile smartphones and virtual reality goggles. Industry 4.0 incorporates Smart Factory practices that provide workers, managers and executives with greater visibility and more flexibility and control over their manufacturing processes. With the ability to deliver real-time data collection, cloud-based analytical capabilities and the implementation of the Industrial Internet of Things (IIoT), manufacturers are able to extract even more value from well-known manufacturing improvement processes and methods such as Lean Manufacturing. With Industry 4.0, Connected Workers and connected machinery share a cyber-physical space where processes are integrated and decisions are made both in real-time and over time as unprecedented amounts of newly available data are turned into insights. As a consequence, the track aims at stimulating scholars from different research fields to propose contributions aimed at highlighting and analyzing the benefits of using these technologies in operations management. Topics of interest include:

• Machine Learning
• Real-time Data Collection
• Artificial Intelligence
• Internet of Things
• Cyber Physical System
• Information And Communications Technology
• Big Data
• Strategic Decision-Making Processes
• Lean Manufacturing
• Maintenance 4.0
• Smart Maintenance

Please be sure to select “​Industry 4.0 applications for operations management and maintenance” as main topic during the submission.

Co-Chairs: Giovanni Mirabelli, Vittorio Solina

Affiliation: DIMEG, University of Calabria (Italy)

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 “​Innovative solutions for smart and sustainable agri-food supply chains” as main topic during the submission.

Co-Chairs: ^(a)Andreas Beham, ^(b)Stefan Wagner

Affiliation: ^(a)(b)Heuristics and Evolutionary Algorithms Laboratory (HEAL), School of Informatics, Communications and Media, 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 “Integrated and dynamic planning for efficient smart manufacturing: Recent advances in management and science” as main topic during the submission.

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

Affiliation: University of Perugia (Italy)

Track Description: The idea of Lean and Sustainability (L&S) has become very popular in the last five years. Up to now, studies concern mainly with the possibility of integrating the two areas. None explicitly consider the fact that lean manufacturing is per se an improvement of factory operations management. Companies can be more reactive to external solicitations. In this sense, lean can react better to sustainable innovations. Papers investigating the topic of lean and sustainable manufacturing are welcome.

Please be sure to select “​Lean and Sustainability: a way for the operations management excellence” as main topic during the submission.

Chair: Tim Jeske

Affiliation: Institute of Applied Industrial Engineering and Ergonomics (Germany)

Track Description: Productivity is crucial for companies to compete on international markets. The management of productivity requires a holistic view on all processes of a company to be successful. This is still applicable when digitalization is introduced and companies develop towards Smart Manufacturing/Industry 4.0. But digitalization offers many new opportunities for management as well as for measures improving productivity. More data and information get available and help improving processes, work systems and work places. Examples are assistance systems providing information about the next task to do for assembly workers or human-robot-collaboration which combines the specific strengths of human and robot. Furthermore, rather monotonous tasks can get automated while human get more time for executing creative tasks.

Please be sure to select “​Management of Productivity in Smart Manufacturing/Industry 4.0” as main topic during the submission.

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

Affiliation: ^(a)(b)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, 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.

Sub-topics:

• Advanced manufacturing,
• Hybrid manufacturing,
• Sustainable manufacturing,
• Intelligent control system,
• Open innovation in manufacturing,
• Application of I4.0 paradigm,
• Super Smart Society.

Please be sure to select “Manufacturing of the future” as main topic during the submission.

Co-Chairs: ^(a)Michael Affenzeller, ^(b)Lukas Fischer, ^(c)Katharina Rafetseder, ^(a)Kaifeng Yang

Affiliation: ^(a)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 “​Prescriptive Analytics in Production” as main topic during the submission.

Co-Chairs: ^(a)Josef Wolfartsberger, ^(b)Norbert Wild, ^(c)Jan Zenisek

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

Track Description: The topic of smart production has attracted many researchers and industry experts over the past years, leading to innovative methodological approaches and promising implementations. Driven by the pursuit of innovation and the goals determined by public research funding institutions, scientific publications as well as technical magazines are prominently covering these success stories. Less rewarding discourses considering cost-benefit analysis or the problems and failures down the road to success are granted comparably little space. This bias in conservative and popular scientific dissemination entails unrealistically high expectations amongst all stakeholders on the verge of a smart production mission. This track aims to collect and persist the hard lessons learned, which may eventually help others to pave the way for a successful transition from smart production concept to implementation. Therefore, researchers are invited to scientifically recapitulate experiences and results from recent projects linked to smart production with focus on what did not quite went as intended, challenges to be overcome, tradeoffs to be made, viable off-text book solutions found, open problems etc. Topics of interest include any kind with link to smart production. Contributions may cover:

• Augmented, Mixed and Virtual Reality: Usefulness of XR solutions in industry context
• Additive Manufacturing: Overcomplex geometries, unsuitable raw material
• Predictive Maintenance: breakdown prediction horizon and reactivity vs. prediction confidence and accurracy; maintenance operator scheduling
• AI & Machine Learning in Industry: challenging data quality and quantity, data preprocessing effort, domain expert acceptance and trust

Please be sure to select “Smart Production Revisited: Challenges, Failures and Learnings” 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.

Please be sure to select “Sustainable and Resilient Manufacturing” as main topic during the submission.

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

Affiliation: Otto von Guericke University Magdeburg (Germany)

Track Description: The concepts of 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 the 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. Topics of interest include, but not limited to:

• Application of optimization techniques for advancing manufacturing environments.
• Adoption of Machine learning approach for improving production quality and prediction of anomalies in production and supply chain.
• Introduction or application of hybrid techniques that combine the use of (Heuristic, Metaheuristics, or Machine learning) for addressing manufacturing problems.
• Big data applications for Industry 4.0
• Cloud Computing concepts for Industry 4.0
• Data Engineering concepts for Industry 4.0

Please be sure to select “The adoption of cloud computing technologies for enabling Industry 4.0 visions” 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 “​The digitalization of supply chain: challenges and opportunities from a managerial perspective” as main topic during the submission.

Chair: ^(a)Francesco Longo, ^(a)Antonio Padovano, ^(b)David Romero, ^(c)Johan Stahre, ^(c)Åsa Fasth Berglund, ^(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 “The Industrial Operator 4.0: Human-Technology Integration and Collaboration” as main topic during the submission.