Technological advancements in the information technology domain such as cloud computing, industrial internet of things (IIoT), machine to machine (M2M) communication, artificial intelligence (AI), etc. have started to profoundly impact and challenge not only the ISA95 compliant traditional (ISA95-CTS) but also the smart manufacturing systems (SMMS). Our literature survey pinpoints that systems scalability, interoperability, information security, and data quality domains are among those where many challenges occur. Blockchain technology (BCT) is a new breed of technology characterized by decentralized verifiability, transparency, data privacy, integrity, high availability, and data protection properties. Although many researchers leveraged BCT to empower various aspects of industrial manufacturing systems, there is no study dedicated to addressing the challenges impacting the manufacturing systems compliant with the ISA95 standard. Thereby, our study aims to fill the identified research gap systematically. This paper thoroughly analyzes the challenges hampering the ISA95-CTS and SMMS and methodically addresses them with corresponding BCT capabilities. Furthermore, this paper also discusses various aspects, including the weaknesses, of BCT convergence to ISA95-CTS and SMMS.

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

The variability in the market conditions is growing in terms of its frequency of change and range of diversity. In response to this new industrial panorama, research on production systems is aiming to achieve highly reconfigurable shop floors. Frequent changes in such systems require also frequent re-planning with updated information. In this regard the Continuous Precise Workload Control method, is a recent approach aiming at precise control of workload in shop floor with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. The study in this paper is two folded; (1) the presented workload approach is defined in terms of eight dimensions of the workload control concept in order to highlight its distinctive characteristics and (2) the impact of idleness penalty factor is analyzed by an experiment design in order to investigate its effect on the job release decision. The results show that the idleness penalty factor decreases the idleness of the resources up to a point where the adverse effect is initiated.

Modern machineries are often cyber-physical system-of-systems controlled by intelligent controllersfor collaborative operations on the productions of complex products. To assure theefficiency and effectiveness, a consolidation of concerns across managerial levels, product lifecyclestages, and product lines or families becomes necessary. This calls for a common informationinfrastructure in terms of ontology, models, methods and tools. For industrial manufacturerssubjected to increased cost pressure and market volatility, the availability of such an informationinfrastructure would promote their abilities of making optimized and proactive decisions andthereby their competitiveness and survivability. This paper presents a virtual environment thatconstitutes an information infrastructure for the management and development of evolvableproduction systems (EPS) in manufacturing. It adopts mature modeling frameworks throughEAST-ADL for an effective model-based approach. The contribution is centered on a meta-modelthat offers a common data specification and semantic basis for information management acrossproduct lifecycle, models and tools, both for resource planning and for anomaly treatment. Aprototype tool implementation of this virtual environment for validation is also presented.

Recent developments in the area of Cyber-Physical Production Systems prove that high technology readiness level is already achieved and industrialization of such technologies is not far from today. Although these technologies seem to be convenient in providing solutions to environmental uncertainties, their application provides adaptability only at shop floor level. Needless to say, an enterprise cannot reach true adaptability without ensuring adaptation skills at every level in its hierarchy. Commonly used production planning and control approaches in industry today inherit from planning solutions which are developed in response to historical market characteristics. However, market tendency in recent years is towards making personalized products a norm. The emerging complexity out of this trend obliges planning systems to a transition from non-recurring, static planning into continuous re-planning and re-configuration of systems. Therefore, there is a need of responsive planning solutions which are integrated to highly adaptable production system characteristics.

In this dissertation, Demand Responsive Planning, DRP, is presented which is a planning framework aiming to respond to planning needs of shifting trends in both production system technologies and market conditions. The DRP is based on three main constructs such as dynamicity, responsiveness and use of precise data. These features set up the foundation of accomplishing a high degree of adaptability in planning activities. By this means, problems from an extensive scope can be handled with a responsive behavior (i.e. frequent re-planning) by the use of precise data. The use of precise data implies to execute planning activities subject to actual demand information and real-time shop floor data. Within the context of the DRP, both a continuous workload control method and a dynamic capacity adjustment approach are developed. A test-bed is coded in order to simulate proposed method based on a system emulation reflecting the characteristics of cyber-physical production systems at shop floor level.

Continuous Precise Workload Control, CPWLC, method is a novel approach aiming at precise control of workload levels with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. As a result, improved shop floor performances are achieved through controlling workload levels precisely by the release of appropriate job types at the right time.

Presented dynamic capacity adjustment approach utilizes rapid re-configuration capability of cyber-physical systems in achieving more frequent capacity adjustments. Its implementation architecture is integrated to the CPWLC structure. By this means, a holistic approach is realized whereby improved due date performance is accomplished with minimized shop floor congestion. Hence, sensitivity to changing demand patterns and urgent job completions is improved.

Product design has a huge and widespread impact on the eventual design of the related production processes, such as procurement, manufacturing, assembly, maintenance and recycling, amongst others. Understanding the full the nature of such a complex relationship is a cornerstone in the professional development of any production engineering student and practitioner. Acquiring sophisticated concepts is a long process consisting of acquiring the necessary notions and mentally structuring them through different semantic links in a consistent body of knowledge. This generates a large set of intermediate states between the novice and the expert. Phenomenography focuses on identifying and classifying these perceptions with the aim of identifying the related pattern for good learning. In particular, this phenomenographic analysis focuses on investigating the students' perception of the articulated link between the design of a product and that of the related assembly process. The study is based on courses that exploit the principles of Design for Assembly (DFA) methods to present and detail such a domain. In the first section of the paper, the aforementioned focal issue is fully characterized as a 'Threshold Concept'. The central part of the paper describes five generic levels of understanding of such a matter: from a simple mechanical use of DFA to a more sophisticated correct holistic understanding of all the implications of such a tool. The classification has been inferred through a series of informal, semi-structured interviews with the students. The characterization introduced is finally discussed with the aim of disclosing the pattern of good learning that, in turn, could provide the base for studies aimed at disclosing useful hints for the effective development of the related teaching activities.

Sustainability is currently one of the biggest challenges and drivers of manufacturing industry. With traditional automation approaches becoming evermore inadequate to support sustainable mass customized production, the research focus is moving towards agile systems that enact companies with the ability to quickly reconfigure their shop-floors by seamlessly deploying or removing modules. Such systems are envisioned as key for attaining a profitable and sustainable industrial development. In this sense, this paper attempts to characterize an innovative approach that relies on bio-inspired concepts as the main control mechanism, in order to foster sustainability by attaining the necessary shop-floor agility. Furthermore an experimental setup is presented and the results are analysed, in order to understand the influence and impact of the main properties of the approach towards the system performance.

The variability in the market conditions is growing in terms of its frequency of change and range of diversity. In response to this new industrial panorama, research on production systems is aiming to achieve truly reconfigurable shop floors. Frequent changes in such systems require also frequent re-planning with updated information. In this regard the Continuous Precise Workload Control method, is a recent approach aiming at precise control of workload in the shop floor with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. The study in this paper is two folded; (1) in order to highlight its distinctive characteristics, the presented workload approach is defined in terms of eight dimensions of the workload control concept and (2) the penalty of idleness which affects the decision of release is analyzed by an experiment design in order to investigate its correlation with two critical parameters, norm value and assessment range. The results show that the idleness penalty factor decreases the idleness of the resources up to a point where the adverse effect is initiated. Besides there are strong indications towards the correlation of idleness penalty factor with the norm value.

The diversity of requirements and the frequency of change in the market can only be competed with dynamicity and responsiveness in both production and planning systems. In this sense, working principles of a novel workload control method, called continuous precise workload control are presented in this paper. The implementation of the method is based on a multi-agent based architecture. The presented approach generates dynamic non periodic release decisions exploiting real time shop floor information. The performance of the system and correlation of norm value against the assessment range are investigated through an experimented test case.

The rise of global competition and the demands for mass customization observed in recent years are the main shaping forces of the manufacturing domain. Current approaches to industrial production automation are not suitable to cope with the resulting increasing requirement in term of system agility and sustainability. While a quite large amount of innovative and sound technical solutions for automation address such an issue, it is not clear how the new generation of automatic production system will be economically connoted. This work proposes a first step towards an economical characterization of the Evolvable Paradigm: one among the most promising aforementioned innovative industrial automation technologies. A basic description of the state-of-the-art of the related Evolvable Assembly/Production System allows inferring a cost model able to account for such an installation. This, in turn, enable a quantitative description of how the focal innovative approach enables a more effective and rational use of industrial automation.