Smart factory
211432316
Smart factory performance and Industry 4.0
Abstract Existing literature on the Industry 4.0 concept does not empirically verify if, how, and for which types of firms, a greater openness to enabling technologies of Industry 4.0 provides further opportunities. This study analyzes the causal relationship between this degree of openness and performance, with an empirical analysis based on a sample representing local manufacturing units. Performance is measured by the extent of opportunities businesses obtain. The degree of openness is investigated using two indicators: breadth, or the number of technologies used; and depth, or the number of value chain stages involved. The regression models demonstrate that: (1) breadth and (2) depth of Industry 4.0 allow greater opportunities, and (3) micro level local units achieve best performances. Verifying the opportunities for companies with Industry 4.0 is extremely relevant, as investments in Industry 4.0 are high in terms of costs, the acquisition of new skills, and the risks of obsolescence to enable better strategic decisions. This work also provides a scope for future analyses of this topic conducted on panel data. Despite the limited application of Industry 4.0, this study's results can encourage managers and policy makers to implement a wider range of enabling technologies in the various stages of the value chain.
Giacomo Buchi Monica Cugno Rebecca Castagnoli
203173126
The smart factory as a key construct of industry 4.0: A systematic literature review
Industry 4.0 is a ubiquitous term throughout general newspapers, on company websites or in scientific journals. One of its key constructs is the smart factory, envisioned as a future state of a fully connected manufacturing system, mainly operating without human force by generating, transferring, receiving and processing necessary data to conduct all required tasks for producing all kinds of goods. Although the understanding of smart factory concepts has been sharpened in the last years, it is still difficult for industrial companies to establish a concrete strategy roadmap within the jungle of different terminologies, ideas and concepts. To generate further clarity and to consolidate the previous findings around smart factory for researchers as well as for practitioner, we conducted a systematic literature review. For this purpose, we chose a five steps approach: Scope definition, topic conceptualisation, literature search, literature analysis and synthesis, and synthesis of future research questions. During our review, we found that research within each perspective is fragmented and unequally advanced. Most publications treat single use cases with low generalizability, often rely upon machine data, typically have a technical nature and seldom incorporate impact estimations. In this paper, our key academic and practical contribution lies in the categorisation of the selected publications into eight thematic distinct perspectives within the sphere of smart factory: Decision making, cyber physical systems, data handling, IT infrastructure, digital transformation, human machine interaction, IoT, and cloud manufacturing and services. These are further developed into the smart factory research model, stating a foundation for future research endeavors.
Philipp Osterrieder Lukas Budde Thomas Friedli
70330823
A study on smart factory based ambient intelligence context aware intrusion detection system using machine learning
Digital transformation increasingly gains broad attentions from all the world and particularly studies on artificial intelligence, big data, cloud, and mobile are currently conducted. In addition, research based on ambient intelligence are also performed. Everything including condition information of all objects are shared on real time in AMI environment and all locations and objects are equipped with sensors. It acts intelligently such as decision making. As sensors are equipped in locations and objects and connected with high performance computer networks, users can receive information at any time and anywhere. In particular, the adoption of smart factory that turns all phases into automation and intellectualization based on cyber physical system technology is proliferating. However, unexpected problems are likely to take place due to high complexity and uncertainty of smart factory. Thus, it is very likely to end manufacturing process, trigger malfunction, and leak important information. Although the necessity of analyzing threats to smart factory and systematic management is emphasized, there is insufficient research. In this paper, machine learning and context aware intrusion detection system was built. The established system was effective to detection rate of anomaly signs and possibility of process achievement compared to the previous system.
J. Ambient Intell. Humaniz. Comput.
Seong-Taek Park GuoZhong Li Jae-Chang Hong
5429882
Implementing Smart Factory of Industrie 4.0: An Outlook
With the application of Internet of Things and services to manufacturing, the fourth stage of industrialization, referred to as Industrie 4.0, is believed to be approaching. For Industrie 4.0 to come true, it is essential to implement the horizontal integration of inter corporation value network, the end to end integration of engineering value chain, and the vertical integration of factory inside. In this paper, we focus on the vertical integration to implement flexible and reconfigurable smart factory. We first propose a brief framework that incorporates industrial wireless networks, cloud, and fixed or mobile terminals with smart artifacts such as machines, products, and conveyors. Then, we elaborate the operational mechanism from the perspective of control engineering, that is, the smart artifacts form a self organized system which is assisted with the feedback and coordination blocks that are implemented on the cloud and based on the big data analytics. In addition, we outline the main technical features and beneficial outcomes and present a detailed design scheme. We conclude that the smart factory of Industrie 4.0 is achievable by extensively applying the existing enabling technologies while actively coping with the technical challenges.
Int. J. Distributed Sens. Networks
Shiyong Wang Jiafu Wan Di Li Chunhua Zhang
125250150
Data Driven Analysis for RFID Enabled Smart Factory: A Case Study
The emergence of Internet of Things (IoT) and new manufacturing paradigms have brought greater complexity of massive datasets. Radio frequency identification (RFID) as one of the key IoT technologies, has been used to collect real time production data to support the manufacturing decision making in smart factories. The adoption of these technologies results in a large amount of data collection. To extract useful information from this data, this paper utilizes a big data approach to figure out useful insights from RFID enabled data regarding possible bottlenecks or inefficiencies on the shop floor so as to improve the quality management. Time and quality are the main metrics measured in this paper, where the longest process times, part accuracy percentage, and failure rate are determined for each of the workers (UserIDs) and process types (ProcCodes) Key findings and observations are significant to make advanced decisions in the smart factory by making full use of the RFID captured data. Note to Practitioners This paper was motivated by a real life company which has used RFID technology for over ten years. Great myriad of data has been captured from frontline production shop floors where machines, workers, materials, and manufacturing jobs were logically associated. Processing time and quality data are studied in this paper so as to investigate the production performance in the RFID enabled smart factory.
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Jiqiang Feng Feipeng Li Chen Xu Ray Y Zhong
213263345
Transformation towards smart factory system: Examining new job profiles and competencies
Andrej Jerman Mirjana Pejic Bach Ana Aleksic
3785868
Towards smart factory for industry 4.0: a self organized multi agent system with big data based feedback and coordination
The proliferation of cyber physical systems introduces the fourth stage of industrialization, commonly known as Industry 4.0. The vertical integration of various components inside a factory to implement a flexible and reconfigurable manufacturing system, i.e. smart factory, is one of the key features of Industry 4.0. In this paper, we present a smart factory framework that incorporates industrial network, cloud, and supervisory control terminals with smart shop floor objects such as machines, conveyers, and products. Then, we provide a classification of the smart objects into various types of agents and define a coordinator in the cloud. The autonomous decision and distributed cooperation between agents lead to high flexibility. Moreover, this kind of self organized system leverages the feedback and coordination by the central coordinator in order to achieve high efficiency. Thus, the smart factory is characterized by a self organized multi agent system assisted with big data based feedback and coordination. Based on this model, we propose an intelligent negotiation mechanism for agents to cooperate with each other. Furthermore, the study illustrates that complementary strategies can be designed to prevent deadlocks by improving the agents' decision making and the coordinator's behavior. The simulation results assess the effectiveness of the proposed negotiation mechanism and deadlock prevention strategies.
Comput. Networks
Shiyong Wang Jiafu Wan Daqiang Zhang Di Li Chunhua Zhang
86850570
A Blockchain Based Solution for Enhancing Security and Privacy in Smart Factory
Through the Industrial Internet of Things (IIoT) a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development.
IEEE Transactions on Industrial Informatics
Jiafu Wan Jiapeng Li Muhammad Imran Di Li
212727165
The framework design of smart factory in discrete manufacturing industry based on cyber physical system
ABSTRACT A cyber physical system (CPS) is a new trend in smart manufacturing related research, where the physical system acts as a data access role with sensors and communication systems to collect real world information and communicate to computation modules (i.e. cyber layer) which further analyse and notify the findings to the corresponding physical systems through multiple feedback loops. This paper develops a conceptual framework for a new paradigm called smart factory based on CPS by applying virtual real mapping and fusion, digital twin, big data driven, virtualisation, and edge to cloud service technology to the manufacturing system. The definitions, characteristics, architectures and previous case studies on smart factory based on CPS are explained in detail. In the paradigm, the digital twin merges the physical system modelling and cyberspace simulation to provide an intelligence solution simulation analysis ability. This generates a variety of digital designs of production processes, quality, cost, efficiency and environmental impact analysis about smart factory business mode in the preproduction phase based on big data which can be analysed, and provide authoritative solutions for actual physical space. The proposed system is applied and validated in a real case study from the circuit breaker production industry.
Int. J. Comput. Integr. Manuf.
Gaige Chen Pei Wang Bo Feng Yihui Li Dekun Liu
57753725
Reconfigurable Smart Factory for Drug Packing in Healthcare Industry 4.0
Industry 4.0, which exploits cyber physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low level machine resources. To further functionality reconfiguration and low level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.
IEEE Transactions on Industrial Informatics
Jiafu Wan Shenglong Tang Di Li Muhammad Imran Chunhua Zhang Chengliang Liu Zhibo Pang