Symbiotic cyber-physical Kanban 4.0: an Approach for SMEs

Stadnicka, Dorota; Bonci, Andrea; Lorenzoni, Emanuele; Dec, Grzegorz; Pirani, Massimiliano

doi:10.1109/etfa46521.2020.9212073

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…It is also noticed that the most common application domain is manufacturing (e.g., [23,24,26,27,30,33]), where collective intelligence solutions are adopted to deal with the current trends for highly customized, flexible, and reconfigurable processes to meet fast-changing market conditions. However, some works propose solutions for the energy domain (e.g., see [25,32,36]) and IoT-based smart applications (e.g., see [31,34]).…”

Section: Stadnicka Et Al 2020 [27]mentioning

confidence: 99%

Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems

2022

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Cyber-physical systems (CPS) play an important role in the implementation of new Industry 4.0 solutions, acting as the backbone infrastructure to host distributed intelligence capabilities and promote the collective intelligence that emerges from the interactions among individuals. This collective intelligence concept provides an alternative way to design complex systems with several benefits, such as modularity, flexibility, robustness, and reconfigurability to condition changes, but it also presents several challenges to be managed (e.g., non-linearity, self-organization, and myopia). With this in mind, this paper discusses the factors that characterize collective intelligence, particularly that associated with industrial CPS, analyzing the enabling concepts, technologies, and application sectors, and providing an illustrative example of its application in an automotive assembly line. The main contribution of the paper focuses on a comprehensive review and analysis of the main aspects, challenges, and research opportunities to be considered for implementing collective intelligence in industrial CPS. The identified challenges are clustered according to five different categories, namely decentralization, emergency, intelligent machines and products, infrastructures and methods, and human integration and ethics. Although the research indicates some potential benefits of using collective intelligence to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, such approaches are still in the early stages, with perspectives to increase in the coming years. Based on that, they need to be further developed considering some main aspects, for example, related to balancing the distribution of intelligence by the vertical and horizontal dimensions and controlling the nervousness in self-organized systems.

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Section: Stadnicka Et Al 2020 [27]mentioning

confidence: 99%

Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems

2022

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“…When analyzing the concepts of Industry 4.0, the literature distinguishes the following categories of technologies on which the concept is based: cyber-physical systems (e.g., [ 7 ]), Internet of Things, big data analytics, cloud computing, fog and edge computing (EC), Augmented Reality (AR) and Virtual Reality, robotics, cyber security, Semantic Web technologies, and additive manufacturing [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Possible Applications of Edge Computing in the Manufacturing Industry—Systematic Literature Review

Kubiak

Dec

Stadnicka

2022

Sensors

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This article presents the results of research with the main goal of identifying possible applications of edge computing (EC) in industry. This study used the methodology of systematic literature review and text mining analysis. The main findings showed that the primary goal of EC is to reduce the time required to transfer large amounts of data. With the ability to analyze data at the edge, it is possible to obtain immediate feedback and use it in the decision-making process. However, the implementation of EC requires investments not only in infrastructure, but also in the development of employee knowledge related to modern computing methods based on artificial intelligence. As the results of the analyses showed, great importance is also attached to energy consumption, both in ongoing production processes and for the purposes of data transmission and analysis. This paper also highlights problems related to quality management. Based on the analyses, we indicate further research directions for the application of edge computing and associated technologies that are required in the area of intelligent resource scheduling (for flexible production systems and autonomous systems), anomaly detection and resulting decision making, data analysis and transfer, knowledge management (for smart designing), and simulations (for autonomous systems).

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