A Review Of Cloud Manufacturing: Issues And Opportunities

Cloud Manufacturing (CM) is the latest manufacturing paradigm that enables manufacturing to be looked upon as a service industry.The aim is to offer manufacturing as a service so that an individual or organization is willing to manufacture products and utilize this service without having to make capital investment.However,industry adoption of CM paradigm is still limited.This paper compared the current adoption of CM by the industry with the ideal CM environment.The gaps between the two were identified and related research topics were reviewed. This paper also outlined research areas to be pursued to facilitate CM adoption by the manufacturing industry.This will also improve manufacturing resource utilization efficiencies not only within an organization but globally.At the end,the cost benefits will be passed down to end customer

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

A Review of Cloud Manufacturing: Issues and Opportunities

Cloud Manufacturing (CM) is the latest manufacturing paradigm that enables manufacturing to be looked upon as a service industry. The aim is to offer manufacturing as a service so that an individual or organization is willing to manufacture products and utilize this service without having to make capital investment. However, industry adoption of CM paradigm is still limited.  This paper compared the current adoption of CM by the industry with the ideal CM environment.  The gaps between the two were identified and related research topics were reviewed.  This paper also outlined research areas to be pursued to facilitate CM adoption by the manufacturing industry.  This will also improve manufacturing resource utilization efficiencies not only within an organization but globally.  At the end, the cost benefits will be passed down to end customer

How Environmentally Sustainable Is the On-Going Industrial Digitalization? Global Trends and a Swedish Perspective

While industrial digitalization presents great opportunities to enhance the efficiency, flexibility, and reliability of production systems, the environmental implications of these improvements are not systematically considered. As digitalization is a relatively new field of research, there are no unified framework to guide its development towards achieving sustainability goals. To support researchers and practitioners towards such a framework, this study aims to formalize the relationship between industrial digitalization and environmental sustainability by reviewing published literature intersection of these two topics. The work was carried out in four steps: (1) Define and scope the problem around environmental considerations when adopting and exploiting digital technologies in manufacturing; (2) Design the literature analysis process to identify publications at the intersection of environmental sustainability and digitalization; (3) Categorise the literature based on established eco-efficiency principles; (4) Visualise and discuss the results about which principles are covered by current research and to what extent. The global trends in the literature collected and analysed are presented along with a more detailed content analysis for Swedish research. While the results confirm that digitalization has the potential to address eco-efficiency principles, relatively few studies explicitly mention the sustainability implications of the research and proposed technological solutions. The paper proposes an eco-efficient smart production model using eco-efficiency as guiding principles. The main argument put forward in this paper is that digital technologies should more systematically contribute to greener industrial systems through energy and material efficiency, pollution prevention, sustainable use of renewable sources, product quality and durability, value retention through remanufacturing, recycling and servitization

Virtual supply chain for networked business : perspective of collaborative bill-of-materials, scheduling and process monitoring for developing innovative product

This article presents a methodology applicable to the formation of collaborative bill-of-materials (C-BOM), coordinating production scheduling and monitoring operational processes. The concept of collaborative product development process is described in detail through an example network, where the participating organisations form a temporary virtual organisation (VO) and contribute throughout the entire product development processes. In this paper, the basic need for exchanging product information among the partners is carried out through IT-based software tools, where the partners are enabled to visualise and monitor the operational processes in a real-time environment. This research study concludes with the overall research outcomes and future research directions.fi=vertaisarvioitu|en=peerReviewed

Unveiling the Potential of Big Data Analytics for Transforming Higher Education in Bangladesh; Needs, Prospects, and Challenges

Big Data Analytics has gained tremendous momentum in many sectors worldwide. Big Data has substantial influence in the field of Learning Analytics that may allow academic institutions to better understand the learners needs and proactively address them. Hence, it is essential to understand Big Data and its application. With the capability of Big Data to find a broad understanding of the scientific decision making process, Big Data Analytics (BDA) can be a piece of the answer to accomplishing Bangladesh Higher Education (BHE) objectives. This paper reviews the capacity of BDA, considers possible applications in BHE, gives an insight into how to improve the quality of education or uncover additional values from the data generated by educational institutions, and lastly, identifies needs and difficulties, opportunities, and some frameworks to probable implications about the BDA in BHE sector. Keywords; Big Data Analytics, Learning Analytics, Quality of Education, Challenges, Higher Education, Banglades

Manufacturing Value Modelling, Flexibility, and Sustainability: from theoretical definition to empirical validation

The aim of this PhD thesis is to investigate the relevance of flexibility and sustainability within the smart manufacturing environment and understand if they could be adopted as emerging competitive dimensions and help firms to take decisions and delivering value

Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future

Given the exponential expansion of the internet, the possibilities of security attacks and cybercrimes have increased accordingly. However, poorly implemented security mechanisms in the Internet of Things (IoT) devices make them susceptible to cyberattacks, which can directly affect users. IoT forensics is thus needed for investigating and mitigating such attacks. While many works have examined IoT applications and challenges, only a few have focused on both the forensic and security issues in IoT. Therefore, this paper reviews forensic and security issues associated with IoT in different fields. Future prospects and challenges in IoT research and development are also highlighted. As demonstrated in the literature, most IoT devices are vulnerable to attacks due to a lack of standardized security measures. Unauthorized users could get access, compromise data, and even benefit from control of critical infrastructure. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system by designing a FLIP-based system that is highly scalable and adaptable. Utilizing a blockchain-based authentication mechanism with a multi-chain structure can provide additional security protection between different trust domains. Deep learning can be utilized to develop a network forensics framework with a high-performing system for detecting and tracking cyberattack incidents. Moreover, researchers should consider limiting the amount of data created and delivered when using big data to develop IoT-based smart systems. The findings of this review will stimulate academics to seek potential solutions for the identified issues, thereby advancing the IoT field.Comment: 77 pages, 5 figures, 5 table