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In the last decade, cloud manufacturing (CMfg) has attracted considerable attention from academia and industry worldwide. It is widely accepted that the design and analysis of cloud manufacturing architecture (CMfg-A) are the basis for developing and applying CMfg systems. However, in existing studies, analysis of the status, development process and internal characteristics of CMfg-A is lacking, hindering an understanding of the research hotspots and development trends of CMfg-A. Meanwhile, effective guidance is lacking on the construction of superior CMfg-As. The purpose of this paper is to review the relevant research on CMfg-A via identification of the main layers, elements, relationships, structure and functions of CMfg-A to provide valuable information to scholars and practitioners for further research on key CMfg-A technologies and the construction of CMfg systems with superior performance.

This study systematically reviews the relevant research on CMfg-A across transformation process to internal characteristics by integrating quantitative and qualitative methods. First, the split and reorganization method is used to recognize the main layers of CMfg-A. Then, the transformation process of six main layers is analysed through retrospective analysis, and the similarities and differences in CMfg-A are obtained. Subsequently, based on systematic theory, the elements, relationships, structure and functions of CMfg-A are inductively studied. A 3D printing architecture design case is conducted to discuss the weakness of the previous architecture and demonstrate how to improve it. Finally, the primary current trends and future opportunities are presented.

By analyzing the transformation process of CMfg-A, this study finds that CMfg-A resources are developing from tangible resources into intangible resources and intelligent resources. CMfg-A technology is developing from traditional cloud computing-based technology towards advanced manufacturing technology, and CMfg-A application scope is gradually expanding from traditional manufacturing industry to emerging manufacturing industry. In addition, by analyzing the elements, relationships, structure and functions of CMfg-A, this study finds that CMfg-A is undergoing a new generation of transformation, with trends of integrated development, intelligent development, innovative development and green development. Case study shows that the analysis of the development trend and internal characteristics of the architecture facilitates the design of a more effective architecture.

This paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. The reason for considering Chinese articles is that CMfg was proposed by the Chinese and a lot of Chinese CMfg-A articles have been published in recent years. CMfg is suitable for the development of China’s manufacturing industry because of China’s intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg-A.

Prior studies ignore the identification and analysis of development process and internal characteristics for the current development of CMfg-A, including the main layers identification of different CMfg-As and the transformation process analysis of these main layers, and in-depth analysis of the inner essence of CMfg-A (such as its elements, relationships, structure and functions). This study addresses these limitations and provides a comprehensive literature review.

The purpose of this paper is to review and explore the evolution, advances and future trends of cloud manufacturing, placing the focus on the quality of services. Moreover, moving toward the new trend of cyber-physical systems (CPS), a cloud-based cyber-physical system (CBCPS) is proposed combining the key enabling techniques of this decade, namely Internet of Things (IoT), cloud computing, Big Data analytics and CPS.

First, an extensive review is made on cloud computing and its applications in manufacturing sectors, namely product development, manufacturing processes and manufacturing systems management. Second, a conceptual CBCPS which combines key enabling techniques including cloud computing, CPS and IoT is proposed. Finally, a review on the quality of the services (QoS) presented in the second step, along with the main security issues of cloud manufacturing, is conducted.

The findings of this review indicate that the combination of the key enabling techniques presented in the CBCPS will lead to a new manufacturing paradigm capable of facing the new challenges and trends. The opportunities, as well as the challenges and barriers of the proposed framework are presented, concluding that the transition into this whole new era of networked computing and manufacturing has a valuable impact, but also generates several security and quality issues.

The paper is the first to specifically study the QoS as a factor in the proposed manufacturing paradigm.

This study aims to identify the critical factors (barriers and drivers) influencing the adoption of cloud computing (ACC) in the manufacturing sector in India.

In this study, a mixed methodology approach is used. Interviews are conducted to investigate factors (drivers and barriers) influencing the ACC, which are further categorized as controllable determinants (weaknesses and strengths) and uncontrollable determinants (threats and opportunities) using a SWOT analysis. Fuzzy analytic hierarchy process (FAHP) has been utilized to highlight the most critical drivers as well as barriers. Finally, decision-making trial and evaluation laboratory (DEMATEL) has been used to find the cause-effect relationships among factors and their influence on the decision of adoption.

The manufacturing sector is in the digital and value change transformation phase with Industry 4.0, that is, the next industrial revolution. The 24 critical factors influencing ACC are subdivided into strengths, weaknesses, opportunities and threats. The FAHP analysis ranked time to market, competitive advantage, business agility, data confidentiality and lack of government policy standards as the most critical factors. The cause-effect relationships highlight that time to market is the most significant causal factor, and resistance to technology is the least significant effect factor. The results of the study elucidate that the strengths of ACC are appreciably more than its weaknesses.

This study couples the technology acceptance model (TAM) with technology-organization-environment (TOE) framework and adds an economic perspective to examine the significant influences of ACC in the Indian manufacturing sector. Further, it contributes to the knowledge of ACC in general and provides valuable insights into interrelationships among factors influencing the decision and strategies of adoption in particular.

This is the first scholarly work in the Indian manufacturing sector that uses the analysis from SWOT and FAHP approach as a base for identifying cause-effect relationships between the critical factors influencing ACC. Further, based on the extant literature and analysis of this work, an adoption framework has been proposed that justifies that ACC is not just a technological challenge but is also an environmental, economic and organizational challenge that includes organizational issues, costs and need for adequate government policies.

Cloud manufacturing (CMfg) is a networked manufacturing mode that promotes the agile, service-oriented, green and intelligent development of the manufacturing industry. Although some scholars have reviewed related studies of CMfg from multiple perspectives, these reviews are not fully systematic or well justified and fail to fully reveal the key characteristics in the development process of CMfg. The purpose of this paper is to systematically review the relevant research on CMfg via identification of key characteristics of definition, architecture, supporting technology and application of CMfg to provide critical information in decision support for the innovation and development of CMfg.

This study systematically reviews the relevant research on CMfg across theoretical methods to technical applications by integrating quantitative and qualitative methods. Word cloud method is used to quantitatively analyse the structure and feature of different definitions of CMfg. The principle of System Science is used to explore the basic components and functions of various CMfg architectures and their common and differing characteristics. A multi-level technology framework is developed to explore the development status of CMfg supporting technologies. A multi-stage application classification is proposed to reveal the application status of CMfg.

Through literature review, this study found that CMfg architecture is currently dominated by general architectures and lacks architectures that fit the actual enterprise characteristics; CMfg supporting technology is mature in the traditional cloud computing-based technology, but it is still weak in the development of virtualization and servitization technology, service scheduling technology; CMfg application is still in the initial stage and still lacks a relatively complete system application. By analysing the development status of CMfg, this study also identified potential research directions of CMfg in information management, service composition and evaluation, system application and sustainable development and other aspects.

This paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. Chinese articles account for more than half of the total. The reason is that CMfg was proposed by the Chinese and CMfg is suitable for the development of China's manufacturing industry because of China's intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg.

Prior literature reviews ignore the identification and analysis of key feature identification for the current development of CMfg, including common and unique feature identification of different CMfg architectures and functions, multi-layer analysis and interpretation of CMfg technology and different stage analysis of CMfg applications. This study addresses these limitations and provides a comprehensive literature review.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

The increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.

Intelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R², T-Value and GOF.

The study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.

Study limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.

The research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

Cloud Manufacturing (CM) is the manufacturing version of Cloud Computing and aims to increase flexibility in the provision of manufacturing services. On-demand manufacturing services can be requested by users to the cloud and this enables the concept of Manufacturing-as-a-Service (MaaS). Given the considerable number of prototypes and proofs of concept addressed in literature, this work seeks real CM platforms to study them from a business perspective, in order to discover what MaaS concretely means today and how these platforms are operating.

Since the number of real applications of this paradigm is very limited (if the authors exclude prototypes), the research approach is qualitative. The paper presents a multiple-case analysis of 6 different platforms operating in the manufacturing field today. It is based on empirical data and inductively researches differences among them (e.g. stakeholders, operational flows, capabilities offered and scalability level).

MaaS has come true in some contexts, and today it is following two different deployment models: open or closed to the provider side. The open architecture is inspired by a truly open platform which allows any company to be part of the pool of service providers, while the closed architecture is limited to a single service provider of the manufacturing services, as it happens in most cloud computing services.

The research shoots a picture of what MaaS offers today in term of capabilities, what are the deployment models and finally suggests a framework to assess different levels of development of MaaS platforms.

Cloud manufacturing (CMfg) has emerged as a service-oriented paradigm that enables modularization and on-demand servitization of resources in the context of manufacturing. The plethora of studies on CMfg has led the authors to investigate its implementation, as most of the literature is theoretical or simulation-based. Therefore, the purpose of this study is to investigate the reality of the CMfg concept in terms of adoption.

A tri-theoretic model is developed using the technology adoption model, diffusion of innovation and technology-organization-environment for hypotheses development. Data are collected from 218 US manufacturers. The data analysis approaches are partial least squares structural equation modeling, while data visualization is done to further analysis.

The study shows that most of the US manufacturers are reluctant to adopt the CMfg. Further, the statistical findings imply that competitive pressure, top management support, compatibility and trialability play a vital role in its adoption. The success of the CMfg adoption relies on the implementation of the pre-installation stage and the top management decisions.

For practitioners, the study provides insight on how to supervise the CMfg platform implementation to improve the adoption process. For researchers and academicians, the significance of trialability provides a wide range of research topics on developing the CMfg trials and models.

This paper highlights the concerns of manufacturers about the pros and cons of the CMfg adoption, as this topic has not been given due attention in the literature. This will help to align future research directions according to market concerns and mitigating the factors that are hindering its adoption.

This paper aims to provide a literature review on the cloud-based platforms for the education sectors. The several aspects of cloud computing adoption in education, remote/distance learning and the application of cloud-based design and manufacturing (CBDM) have been studied and theorised.

A four-step methodology was adopted to analyse and categorise the papers obtained through various search engines. Out of 429 research articles, 72 papers were shortlisted for the detailed analysis.

Many factors that influence cloud computing technology adoption in the education sector have been identified in this paper. The research findings on several research items have been tabulated and discussed. Based on the theoretical research done on cloud computing for education, cloud computing for remote/distance learning and CBDM, cloud computing could enhance the educational systems in mainly developing countries and improve the scope for remote/distance learning.

This study is limited to papers published only in the past decade from 2011 to 2020. Besides, this review was unable to include journal articles published in different languages. Nevertheless, for the effective teaching and learning process, this paper could help understand the importance and improve the process of adopting cloud computing concepts in educational universities and platforms.

This study is a novel one as a research review constituting cloud computing applications in education and extended for remote/distance learning and CBDM, which have not been studied in the existing knowledge base.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.