Biologicalisation as the convergence of biology, engineering and information technology offers the prospect of dramatic step change scenarios for future innovative development. A large number of highly stimulating and potentially very valuable solutions, created over millions of years of evolution, are available in nature's solution space and waiting for application in technology. Transfer methods linking the biosphere and the technosphere are classified as functionalities which assume: bio-inspiration from nature, bio-integration combining biological and technological solutions, and bio-intelligence. The latter aims at achieving developments towards living systems based on the (reasonably) high level of appreciation of the environment, the system's capability and the specific task to be undertaken using decision making or self-reasoning. The use of large numbers of different sensors is involved together with sensor fusion strategies, self-healing and self-organising properties, along with functional integration. Moreover, to derive maximum benefit, the key enabling technologies will play a crucial role going forward. The impact on industry of unique and outstanding solutions will radically change the way manufacturing is performed today by building on new levels of latency, interconnectivity and communication. This paper aims at supporting the comprehension of these developments and revealing future trends, research needs and educational requirements in manufacturing science and technology as the biosphere and the technosphere converge to create the new levels of global sustainability.

Platform-based manufacturing is substantially changing the way production is conceived and performed. Companies do not know who is making their parts, part manufacturers do not necessarily own the machines, and knowledge and decisions cross the borders of firms. This revolutionary approach is already becoming a reality thanks to a wealth of innovations related to manufacturing science as well as information and communication technologies, and novel business models that meet together at the right moment of their evolution. Considering the existing literature and digging into the phenomenon by interviewing decision-makers, the paper reconstructs the roots of these developments, analyzes the challenges posed to the manufacturing sector, focuses on recent challenges and opportunities, and finally delineates visions for the future.

Artificial neural networks (ANNs) are successfully applied in different fields of manufacturing, mostly where multisensor integration, robustness, real-timeness, and learning abilities are needed. Since the higher levels of the control and the monitoring hierarchy require symbolic knowledge representation and processing techniques, the integrated use of the symbolic and subsymbolic approaches is straightforward. The paper describes and compares two hybrid AI solutions for supervision and control of manufacturing processes with different degrees of integration. The first experiences gained by their usage are outlined. Finally, further possible applications of these hybrid solutions in intelligent manufacturing environment are enumerated.

The potential in treating chronic and life-threatening diseases by stem cell therapies can greatly be exploited via the efficient automation of stem cell production. Working with living material though poses severe challenges to automation. Recently, production platforms has been developed and tested worldwide with the aim to increase the reproducibility, quality and throughput of the process, to minimize human errors, and to reduce costs of production. A distinctive feature of this domain is the symbiotic co-existence and co-evolution of the technical, information and communication, as well as biological ingredients in production structures. A challenging way to overcome the issues of automated production is the use of biologically inspired control algorithms. In the paper an approach is described which combines digital, agent-based simulation and reinforcement learning for this purpose. The modelling of the cell growth behaviour, which is an important prerequisite of the simulation, is also introduced, together with an appropriate model fitting procedure. The applicability of the proposed approach is demonstrated by the results of a comprehensive investigation.

The evolution of manufacturing systems, influenced by changes along four axes - products, technology, business strategies and production paradigms - is presented. Adoption of human-centric decision making in meshed collaboration with intelligent systems is examined. Implications and preparedness for the shift towards more responsive, intelligent adaptive systems are reviewed. Research and industrial use cases are presented. A vision for the new future Adaptive Cognitive Manufacturing System (ACMS) paradigm and its characteristics, drivers and enablers are articulated highlighting the digital and cognitive transformations. Perspectives and insights are offered for future research, education, and work to realize the evolution of manufacturing systems.

This paper reports on a highly ambitious international study undertaken in the period 2018–2020 on the topic of convergence between biology and advanced manufacturing systems. The international team (authors of this paper) worked together to analyse the status of this convergence through the assessment of concrete examples, referred to here as demonstrators, within advanced manufacturing systems. Four independent demonstrators from different sections of the manufacturing value chain and involving bio-inspiration, bio-integration and/or bio-intelligence were selected to test the following hypothesis: “That Future Manufacturing Systems will incorporate Components, Features, Characteristics and Capabilities that enable the convergence towards Living Systems”. Each of these four demonstrators have succeeded in supporting this hypothesis and in providing clear evidence to confirm that significant performance benefits may be derived through the “biologicalisation” of advanced manufacturing systems. This conclusion is of great significance for the next phases of development of manufacturing science and engineering globally. The evidence reported in this paper provides a robust basis for recommending that a deeper analysis of the implications of biologicalised manufacturing systems be undertaken. As a result of this early stage work, it is concluded that there is a high likelihood that this new convergence will lead to a major paradigm shift in advanced manufacturing. Outstanding opportunities exist for high levels of innovation in the next stages of development of advanced manufacturing processes and systems from the biological perspective. The relationship between the human and the physical manufacturing system will also change and the world of advanced manufacturing will be confronted with many new challenges including important ethical questions.

The potential in treating chronic and life-threatening diseases by stem cell therapies can greatly be exploited via the efficient automation of stem cell production. Working with living material though poses severe challenges to automation. Recently, production platforms has been developed and tested worldwide with the aim to increase the reproducibility, quality and throughput of the process, to minimize human errors, and to reduce costs of production. A distinctive feature of this domain is the symbiotic co-existence and co-evolution of the technical, information and communication, as well as biological ingredients in production structures. A challenging way to overcome the issues of automated production is the use of biologically inspired control algorithms. In the paper an approach is described which combines digital, agent-based simulation and reinforcement learning for this purpose. The modelling of the cell growth behaviour, which is an important prerequisite of the simulation, is also introduced, together with an appropriate model fitting procedure. The applicability of the proposed approach is demonstrated by the results of a comprehensive investigation.

The possible role of stem cells in medical treatments can hardly be overestimated. Today they are produced – almost without exemption – with significant human involvement using adaptive protocols that take the growth behavior of the biological material into account. Automated production platforms are being developed and tested in a number of research laboratories with the main goals of improving reproducibility, as well as increasing quality and throughput. However, automated stem cell production differs from the traditional manufacturing processes in (1) the inherent diversity of the products (stem cells), (2) their varying growth rates and process times, (3) the need for their regular observation and process adaptation, and, therefore, (4) for mixed-initiative production control. A distinctive feature of the domain is the symbiotic co-existence and co-evolution of the technical, ICT and biological ingredients in production structures. A challenging way to overcome these issues is the use of biologically-inspired control algorithms. In the paper the application of reinforcement learning is proposed for this purpose. As a first step, a digital simulation of the stem cell production was performed in order to generate patterns for the training process and to test the approach. In addition to the description of the concept, the paper also presents initial research results.

The impact of the forth industrial revolution on the social and natural environment is considered significant and far-reaching, even though the interactions of the human, natural and manufactured assets are less understood, extremely complex and unpredictable. The paper discusses how present days’ – so-called cyber-physical – manufacturing systems operate in the fabrics of society and the natural environment. It is underlined that they show and – to an increasing extent – will show features and capabilities reminiscent of living beings and organizations. This view helps to understand how manufacturing interacts with the ecosystem and suggests resolutions for the dilemma of competitive and sustainable manufacturing.

Manufacturing became one of the main targets of the coronavirus disease 2019 pandemic. Important questions such as how to deal with the drastic changes (up and down) in demand, and how to keep production ongoing with a decimated workforce and crippled supply chains arose. This technical note gives a short assessment of the praiseworthy reactions of the manufacturing industry, which prevented society from sinking into an even deeper crisis. The pandemic, at the same time, pointed out some weaknesses of present-day manufacturing. Possible long-term consequences of the lessons given and perhaps learned are summarized herein, with a focus on substantial changes and new paradigms that could provide an appropriate armory for the manufacturing industry when responding to similar or even more drastic situations in the future. However, it is yet undecided as to whether the ongoing changes in manufacturing will be accelerated or dismissed, causing manufacturers to fall back into the old groove. Some related questions and dilemmas are also exposed in the end of the paper.

The main focus of the research presented in this paper is to propose new methods for filtering and cleaning large-scale production log data by applying statistical learning models. Successful application of the methods in consideration of a production optimization and a simulation-based prediction framework for decision support is presented through an industrial case study. Key parameters analysed in the computational experiments are fluctuating reject rates that make capacity estimations on a shift basis difficult to cope with. The most relevant features of simulation-based workload estimation are extracted from the products’ final test log, which process has the greatest impact on the variance of workload parameters.

This is a milestone paper of the Coordinating Committee on Manufacturing and Logistics Systems (CC5) of the International Federation of Automatic Control (IFAC). Classical applications of control engineering and information and communication technology (ICT) in production and logistics are often done in a rigid, centralized and hierarchical way. These inflexible approaches are typically not able to cope with the complexities of the manufacturing environment, such as the instabilities, uncertainties and abrupt changes caused by internal and external disturbances, or a large number and variety of interacting, interdependent elements. A paradigm shift, e.g., novel organizing principles and methods, is needed for supporting the interoperability of dynamic alliances of agile and networked systems. Several solution proposals argue that the future of manufacturing and logistics lies in network-like, dynamic, open and reconfigurable systems of cooperative autonomous entities. The paper overviews various distributed approaches and technologies of control engineering and ICT that can support the realization of cooperative structures from the resource level to the level of networked enterprises. Standard results as well as recent advances from control theory, through cooperative game theory, distributed machine learning to holonic systems, cooperative enterprise modelling, system integration, and autonomous logistics processes are surveyed. A special emphasis is put on the theoretical developments and industrial applications of Robustly Feasible Model Predictive Control (RFMPC). Two case studies are discussed: i), a holonic, PROSA-based approach to generate short-term forecasts by means of a delegate multi-agent system (D-MAS) is presented; ii) an application of distributed RFMPC to a drinking water distribution system with benchmarks.

The Centre of Excellence in Production Informatics and Control (EPIC CoE) was established by an international consortium with the objective to be a leading, internationally acknowledged and sustainable focus point in its field, representing excellence in R&D&I related to Cyber-Physical Production. This 7-year project is funded by the European Horizon 2020 Teaming Programme. By the end of this period, the newly created EPIC CoE is expected to become fully self-sustainable in its regional operation that is planned to cover both advanced and emerging countries in Europe. Main objective of this paper is to describe the organisational development and transformation process of an Industry 4.0 oriented state-of-the-art organisation. These circumstances determine the approach how to address the issue of governance closely related to the business model and processes that will be implemented. However, this task has to be facilitated as part of the evolution process envisaged for the upcoming years. First the vision, mission and concrete targets of the EPIC CoE are analysed to build the strategic basis for the derivation of the management and organisational structures enabling the participating partners to harmonise their experiences, skills and competences. By these efforts, the EPIC CoE is conceived as a synergic combination of two entities, i.e. one existing, acting in basic and applied research while the other one is a new company focussing on knowledge transfer and dissemination. In this paper, the special aspects that led to the concrete representations of the governance structure for each of the planned three operational phases will be outlined. These phases are (1) the establishment and start-up of the EPIC CoE, (2) followed by the phase of the evolving and maturing operation (3) to be concluded by the self-sustained one. For the definition of these phases, it is indispensable to define the different levels of the management functions and responsibilities to ensure efficiency, reliability, transparency, clear accountability as well as high-quality services to EPIC CoE’s clients and partners. Being service oriented is specifically a key issue, therefore the processes that will be highlighted are grouped into: the core processes embodying the daily businesses, the management processes that ensure the effective flow of all processes that are aided by the supporting processes.

Offering product variety is crucial for satisfying diverse customer needs. Although product design, process and production planning related decisions are interdependent, they are conventionally made by different divisions, separately for each product, resulting in excess costs. This paper proposes a methodology for increasing investment efficiency by the joint optimization of product design, process and production planning for a family of products. Tolerance allocation, as a sub-problem of product design, and assembly resource configuration, regarding process planning, are solved jointly, with a foresight on long-term production planning. The efficiency of the method is demonstrated through an industrial case study.

A new emerging frontier in the evolution of the digitalisation and the 4th industrial revolution (Industry 4.0) is considered to be that of “Biologicalisation in Manufacturing”. This has been defined by the authors to be “The use and integration of biological and bio-inspired principles, materials, functions, structures and resources for intelligent and sustainable manufacturing technologies and systems with the aim of achieving their full potential.” In this White Paper, detailed consideration is given to the meaning and implications of “Biologicalisation” from the perspective of the design, function and operation of products, manufacturing processes, manufacturing systems, supply chains and organisations. The drivers and influencing factors are also reviewed in detail and in the context of significant developments in materials science and engineering. The paper attempts to test the hypothesis of this topic as a breaking new frontier and to provide a vision for the development of manufacturing science and technology from the perspective of incorporating inspiration from biological systems. Seven recommendations are delivered aimed at policy makers, at funding agencies, at the manufacturing research community and at those industries involved in the development of next generation manufacturing technology and systems. It is concluded that it is valid to argue that Biologicalisation in Manufacturing truly represents a new and breaking frontier of digitalisation and Industry 4.0 and that the market potential is very strong. It is evident that extensive research and development is required in order to maximise on the benefits of a biological transformation.

The accurate prediction of manufacturing lead times (LT) significantly influences the quality and efficiency of production planning and scheduling (PPS). Traditional planning and control methods mostly calculate average lead times, derived from historical data. This often results in the deficiency of PPS, as production planners cannot consider the variability of LT, affected by multiple criteria in today’s complex manufacturing environment. In case of semiconductor manufacturing, sophisticated LT prediction methods are needed, due to complex operations, mass production, multiple routings and demands to high process resource efficiency. To overcome these challenges, supervised machine learning (ML) approaches can be employed for LT prediction, relying on historical production data obtained from manufacturing execution systems (MES). The paper examines the use of state-of-the-art regression algorithms and their effect on increasing accuracy of LT prediction. Through a real industrial case study, a multi-criteria comparison of the methods is provided, and conclusions are drawn about the selection of features and applicability of the methods in the semiconductor industry.

Manufacturing lead time (LT) is often among the most important corporate performance indicators that companies wish to minimize in order to meet the customer expectations, by delivering the right products in the shortest possible time. Most production planning and scheduling methods rely on LTs, therefore, the efficiency of these methods is crucially affected by the accuracy of LT prediction. However, achieving high accuracy is often complicated, due to the complexity of the processes and high variety of products. In the paper, analytical and machine learning prediction techniques are analyzed and compared, focusing on a real flow-shop environment exposed to frequent changes and uncertainties resulted by the changing customer order stream. The digital data twin of the processes is applied to accurately predict the manufacturing LT of jobs, keeping the prediction models up-to-date via online connection with the manufacturing execution system, and frequent retraining of the models.