Each time a relevant proposal occurs, existing perspectives, concepts or fundamentals are confronted by emergent ones. The Industry 4.0 and its promoted production control systems based on Cyber-Physical Systems (CPS), made splash new potentials for the binomial human and technology (equipments and its settings). Several authors explore the envisaged required more physical and digital connection to announce interesting transformative changes, where selfconfigure and adaptive machines sustain the application of corrective decisions. This paper exposes a spectrum of existing CPS definitions and models and contributes with the fundamentals for an effective intelligent CPS (I-CPS), where a double loop learning process, allows its supporting software algorithms to be changed or reprogrammed. Instead, not only selfconfigure machines but its configuring software, too.

PurposeThe objective of this paper is to provide a deeper insight into the relationship of the issue “lean vs agile” in order to inform managers towards more coherent decisions especially in a dynamic, unpredictable, uncertain, non‐linear environment.Design/methodology/approachThe methodology is an exploratory study based on secondary data analysis.Findings“Lean” and “agile” are two exclusive concepts “in the limit” and “agile” has a higher potential for serving as an instrument for starting “a journey” towards a new sustainable organizational paradigm.Research limitations/implicationsFurther research in the context of the arguments presented is necessary, especially in the “field” and on primary data.Practical implicationsThere are clearly indicated contexts of primary applications of “lean” and “agile”, and especially along with the techniques, methodologies and system‐thinking informed by chaordic system thinking (CST), which should be of help for managers.Originality/valueThe novel contribution of the paper is the presentation of the argumentation on “lean” and “agile” as exclusive concepts and their analysis through the CST lenses, as well as the presentation of suggestions for development of new manufacturing systems paradigms.

This paper presents the findings of exploratory research on the potential of semiotics for manufacturing systems integration (MSI). The findings strongly suggest that semiotics might be the basis for a new paradigm for MSI. In the first part of the paper a number of needs for the new semiotic-based integration paradigm are presented. The second part of the paper introduces the basic notions of semiotics and provides a discussion on the use of semiotics in MSI. The third part presents a framework for the semiotics-based MSI, together with a model of the semioticsbased MSI, entitled 'generative integration' (GI). In the final part, some experimental set-ups, i.e. prototype demonstrators of the manufacturing systems, elements and systems, are presented as a platform for future research and development of the semiotics-based MSI.

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In many popular, as well scientific, discourses it is suggested that the "massive" use of Artificial Intelligence, including Machine Learning, and reaching the point of "singularity" through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race. Speaking in terms of manufacturing systems, it would mean that there will be achieved intelligent and total automation (once the humans will be excluded). The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, that the ML algorithms will be not able to became totally autonomous and, consequently, that the human role will be indispensable. In the context of the question, the authors of this paper introduce the notion of the manufacturing singularity and an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will be always human dependent, and that, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in I4.0. The methodology to support this argument is inductive, similarly to the methodology applied in a number of texts found in literature, and based on computational requirements of inductive inference based machine learning. The argumentation is supported by several experiments that demonstrate the role of human within the process of machine learning. Based on the exposed considerations, a generic architecture of intelligent CPS, with embedded ML functional modules in multiple learning loops, in order to evaluate way of use of ML functionality in the context of CPPS/CPS. Similarly to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology doesn't provide an absolute proof in favour of, or against, the hypothesis defined, the paper represents a kind of position paper. The paper is divided into two parts. In the first part a review of argumentation from literature, both in favor of and against the thesis on the human role in future, is presented. In this part a concept of the manufacturing singularity is introduced, as well as an intelligent machine