Fourth Industrial Revolution

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Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is “Smart production”. Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment. This article discusses and generalizes the experience and outlines future research directions.

Industry 4.0 paradigm: The viewpoint of the small and medium enterprises

The pervasive diffusion of Information and Communication technologies (ICT) and automation technologies are the prerequisite for the preconized fourth industrial revolution: the Industry 4.0 (I4.0). Despite the economical efforts of several governments all over the world, still there are few companies, especially small and medium enterprises (SMEs), that adopt or intend to adopt in the near future I4.0 solutions. This work focus on key issues for implementing the I4.0 solutions in SMEs by using a specific case example as a test bench of an Italian small manufacturing company. Requirements and constraints derived from the field experience are generalised to provide a clear view of the profound potentialities and difficulties of the first industrial revolution announced instead of being historically recognised. A preliminary classification is then provided in view to start conceiving a library of Industry 4.0 formal patterns to identify the maturity of a SME for deploying Industry 4.0 concepts and technologies.

Smart Manufacturing Systems based on Cyber-physical Manufacturing Services (CPMS)

Future manufacturing is becoming “smart” – capable of agilely adapting to a wide variety of changing conditions. This requires production plants, supply chains and logistic systems to be flexible in design and reconfigurable “on the fly” to respond quickly to customer needs, production uncertainties, and market changes. Service-Oriented Architecture (SOA) provides a promising platform to achieve such manufacturing agility. It has proven effective for business process adaptation. When combined with the emerging Internet of Things (IoT) technology and the concept of cyber-physical production systems, it is expected to similarly revolutionize real-time manufacturing systems. This paper proposes a new concept of cyber-physical manufacturing services (CPMS) for service-oriented smart manufacturing systems. In addition, we propose a modeling framework that provides appropriate conceptual models for developing and describing CPMS and enabling their composition. Specifically, the modeling framework separates service provision models from service request models and proposes the use of standardized functional taxonomies and a reference ontology to facilitate the mediation between service requests and service consumptions. A 3D-printing use case serves as an example implementation of an SOA-based smart manufacturing system based on our proposed modeling framework.

The Framework of Business Model in the Context of Industrial Internet of Things

The purpose of this article is an attempt to develop the concept of a business model dedicated to companies implementing technologies of the Industrial Internet of Things. The proposed concept has been developed to support traditional companies in the transition to the digital market. The study was based on the available literature on the impact the Industrial Internet of Things has on the economy and business models.

The industrial internet of things (IIoT): An analysis framework

Historically, Industrial Automation and Control Systems (IACS) were largely isolated from conventional digital networks such as enterprise ICT environments. Where connectivity was required, a zoned architecture was adopted, with firewalls and/or demilitarized zones used to protect the core control system components. The adoption and deployment of Internet of Things (IoT) technologies is leading to architectural changes to IACS, including greater connectivity to industrial systems. This paper reviews what is meant by Industrial IoT (IIoT) and relationships to concepts such as cyber-physical systems and Industry 4.0. The paper develops a definition of IIoT and analyses related partial IoT taxonomies. It develops an analysis framework for IIoT that can be used to enumerate and characterise IIoT devices when studying system architectures and analysing security threats and vulnerabilities. The paper concludes by identifying some gaps in the literature.

Enterprise Thinking for Self-aware Systems

The paper aims to provide high-level guidance for architects of cyber-physical enterprises such that the nature of interactions within it as a system can be largely self-determined based on system self awareness and dynamic self-configuration, and a set of foundational guiding principles, rather than being pre-defined by an external designer or architect. The paper investigates the suitability of typical development life cycles and architectural challenges in the context of dynamic cyber-physical systems intending to utilize the power of the Internet of Things, and then goes on to define desired attributes of such systems, which need to guide suitable core architectural choices. Application of the findings is exemplified through a case study, followed by synthesis of issues and implications for further research.