Cyber-physical systems (CPS) in supply chain management: from foundations to practical implementation

Abstract Since 2015 developments such as Industry 4.0 and cyber-physical production systems on the technology side, and approaches such as flexible and smart manufacturing systems hold great potential. These in turn give rise to special requirements that the production planning, control and monitoring, among others, needing a paradigm shift to exploit the full potential of these methods and techniques. Starting from foundations in Cyber Physical Systems (CPS), building upon definitions and findings reported by literature, a practical example of innovative Cyber Physical Supply Chain Planning System (CPS2) is provided. The paper clarifies the advantages of cyber-physical systems in the production planning, controlling and monitoring perspective with respect to manufacturing, logistics and related planning practices. A set of basic features of CPS2 systems are discussed and addressed by contextualizing service orientation architecture and microservices components with respect to supply chain management collaboration and cooperation practices. The identification of specific technologies behind those functions, within the developed research, provides some practical insight if the interesting CPS2 potential

Cyber-physical systems (CPS) in supply chain management: From foundations to practical implementation

Since 2015 developments such as Industry 4.0 and cyber-physical production systems on the technology side, and approaches such as flexible and smart manufacturing systems hold great potential. These in turn give rise to special requirements that the production planning, control and monitoring, among others, needing a paradigm shift to exploit the full potential of these methods and techniques. Starting from foundations in Cyber Physical Systems (CPS), building upon definitions and findings reported by literature, a practical example of innovative Cyber Physical Supply Chain Planning System (CPS2) is provided. The paper clarifies the advantages of cyber-physical systems in the production planning, controlling and monitoring perspective with respect to manufacturing, logistics and related planning practices. A set of basic features of CPS2 systems are discussed and addressed by contextualizing service orientation architecture and microservices components with respect to supply chain management collaboration and cooperation practices. The identification of specific technologies behind those functions, within the developed research, provides some practical insight if the interesting CPS2 potential

Special Session on Industry 4.0

No abstract available

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

A review of modular strategies and architecture within manufacturing operations

This paper reviews existing modularity and modularization literature within manufacturing operations. Its purpose is to examine the tools, techniques, and concepts relating to modular production, to draw together key issues currently dominating the literature, to assess managerial implications associated with the emerging modular paradigm, and to present an agenda for future research directions. The review is based on journal papers included in the ABI/Inform electronic database and other noteworthy research published as part of significant research programmes. The research methodology concerns reviewing existing literature to identify key modular concepts, to determine modular developments, and to present a review of significant contributions to the field. The findings indicate that the modular paradigm is being adopted in a number of manufacturing organizations. As a result a range of conceptual tools, techniques, and frameworks has emerged and the field of modular enquiry is in the process of codifying the modular lexicon and developing appropriate modular strategies commensurate with the needs of manufacturers. Modular strategies and modular architecture were identified as two key issues currently dominating the modular landscape. Based on this review, the present authors suggest that future research areas need to focus on the development and subsequent standardization of interface protocols, cross-brand module use, supply chain power, transparency, and trust. This is the first review of the modular landscape and as such provides insights into, first, the development of modularization and, second, issues relating to designing modular products and modular supply chains

Mechatronics & the cloud

Conventionally, the engineering design process has assumed that the design team is able to exercise control over all elements of the design, either directly or indirectly in the case of sub-systems through their specifications. The introduction of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) means that a design team’s ability to have control over all elements of a system is no longer the case, particularly as the actual system configuration may well be being dynamically reconfigured in real-time according to user (and vendor) context and need. Additionally, the integration of the Internet of Things with elements of Big Data means that information becomes a commodity to be autonomously traded by and between systems, again according to context and need, all of which has implications for the privacy of system users. The paper therefore considers the relationship between mechatronics and cloud-basedtechnologies in relation to issues such as the distribution of functionality and user privacy

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications