Industry 5.0

2022 ◽  
pp. 59-73
Author(s):  
Saurabh Tiwari ◽  
Prakash Chandra Bahuguna ◽  
Jason Walker
Keyword(s):  
Sustainable Development ◽  
Comparative Analysis ◽  
Customer Satisfaction ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Digital Manufacturing ◽  
Human Intervention ◽  
Computing Power ◽  
Use Of Data ◽  
Mass Personalization

There will be a revolution in industry and society as a result of Industry 5.0. Human-robot co-working, also known as cobots, is a key component of Industry 5.0. Industry 5.0 will overcome all the limitations of the previous industrial revolution. Humans and machines will work together in this revolution to increase the efficiency of processes by utilising human brainpower and creativity. To solve complex problems more efficiently and with less human intervention, Industry 5.0 provides a strong foundation for advanced digital manufacturing systems through interconnected networks, and it's designed to communicate with other systems, as well as powerful computing power. To enhance customer satisfaction, Industry 5.0 involves a shift from mass customization to mass personalization along with a shift from digital usage of data to intelligent use of data for sustainable development. On the basis of comparative analysis, this chapter outlines Industry 5.0's definition, its elements and components, and its application and future scope paradigm.

Critical Components of Industry 5.0 Towards a Successful Adoption in the Field of Manufacturing

2020 ◽  
Vol 05 (03) ◽  
pp. 327-348 ◽  
Author(s):  
Mohd Javaid ◽  
Abid Haleem
Keyword(s):  
Manufacturing Systems ◽  
Industrial Revolution ◽  
New Paradigm ◽  
Computing Power ◽  
New Era ◽  
Intelligent Machines ◽  
Customer Delight ◽  
Critical Components ◽  
Industrial Revolutions ◽  
Mass Personalization

The fifth industrial revolution is known as Industry 5.0 and is being evolved to focus on the personalized demand of customers. This industrial revolution is required to provide better interaction among humans and machines to achieve effective and faster outcomes. It provides a new era of personalization and solves complex problems. Digital technologies provide a new paradigm in manufacturing and eliminate repetitive jobs. It applies human intelligence to understand the requirement of a human operator. The data in manufacturing can be analyzed using machine learning and artificial intelligence (AI). This paper discusses the development of all industrial revolutions and differentiates between Industry 4.0 and Industry 5.0. Further, it identifies the significant elements and capabilities of Industry 5.0 in the manufacturing field. This paper finally identifies 17 critical components of Industry 5.0 and discusses them briefly. Intelligent machines used in this revolution are efficiently used to solve real problems. It provides higher accuracy and speeds up the industrial automation with the help of critical thinking of human resources. Industry 5.0 provides computing power to the industry, which is to facilitate the digital manufacturing systems that are built to communicate with other systems. Thus, with mass personalization, there is customer delight with higher value addition through Industry 5.0.

scholarly journals Towards demand-driven industry: supprot system proposal for Factories of Future (FoF)

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Sanja Vasin ◽  
Srdjan Živković
Keyword(s):  
Sustainable Development ◽  
Decision Support ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Technology Development ◽  
Basic Principles ◽  
Distant Future ◽  
Eu Member States ◽  
Engineering And Technology ◽  
Manufacturing Technologies

EU platform called Manufuture aims goal to support manufacturing engineering and technology development in nearer and distant future. Manufacturing technologies are key for sustainable development of EU member states. Serbia as native part of this european nations union, needs to follow these european trends. This article presents an importance of national Manufuture platform adoptation - basic principles of Factories of Future are shown, Cyber-physical manufacturing systems, as well as Customer-oriented decision support systems. All together creating elements of new industrial revolution, called „Industrija 4.0“

Intelligent machining methods for Ti6Al4V: A review

2020 ◽  
pp. 095440892097479
Author(s):  
Sílvia Ribeiro-Carvalho ◽  
RBD Pereira ◽  
Ana Horovistiz ◽  
J. Paulo Davim
Keyword(s):  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Failure Detection ◽  
Machining Process ◽  
Digital Manufacturing ◽  
Behavior Prediction ◽  
Modeling Techniques ◽  
The Future ◽  
Review Reports ◽  
Modelling And Optimization

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision-making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence-based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.

scholarly journals Towards the Development of Digital Manufacturing Ecosystems for Sustainable Performance: Learning from the Past Two Decades of Research

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2945
Author(s):  
Abdulrazak F. Shahatha Al-Mashhadani ◽  
Muhammad Imran Qureshi ◽  
Sanil S. Hishan ◽  
Mohd Shamsuri Md Saad ◽  
Yamunah Vaicondam ◽  
...  
Keyword(s):  
Systematic Review ◽  
Performance Management ◽  
Collaborative Research ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Developed Countries ◽  
Digital Transformation ◽  
Digital Manufacturing ◽  
Manufacturing Processes

Although the Internet of Things (IoT), advanced manufacturing technologies, and cloud manufacturing contribute to developing a digital manufacturing ecosystem that enhances energy efficiency and resource utilization, manufacturing processes are vulnerable to timely production and delivery. The digital manufacturing systems in Industrial Revolution 4.0 (IR 4.0) minimized the human-technology interactions to foster productivity and material flow. However, there is scarce research to gauge the efficiency of these digital technologies in the entire manufacturing process; also, little is known about the collaborative efforts among countries to achieve sustainable manufacturing performance through the digitalization of the production process. Thus, this systematic review aimed to highlight the effectiveness of the digital manufacturing systems for sustainable product development and the collaborative research on the subject. We selected 52 research articles for this review by following the 2015 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. The literature classifications were developed using text frequency algorithms in VOSviewer (Centre for Science and Technology Studies, Leiden University, The Netherlands) Results exposed literature from 2005 to 2020 can be categorized into four major research streams: digital transformation, digital manufacturing ecosystem, performance management, and sustainability. The study’s findings revealed that the manufacturing processes are moving towards the IoT, digital devices, and smart factories that are entirely dependent on digital technologies. The digital manufacturing ecosystem is dependent on the availability of digital technologies to all stakeholders. The study concluded that digital technologies are improving manufacturing efficiency and process effectiveness. However, this requires infrastructure that primarily available in developed countries; thus, the digital transformation in underdeveloped regions is deliberate and requires more collaborative research.

Scientific methodological approaches to the developmentof strategy of sustainable development of territorial communities

2019 ◽  
pp. 71-80
Author(s):  
Olexander Martynyuk ◽  
Inna Studennikova ◽  
Volodymyr Hromozdov ◽  
Victor Maliarevsky
Keyword(s):  
Sustainable Development ◽  
Comparative Analysis ◽  
Empirical Research ◽  
Working Group ◽  
Swot Analysis ◽  
Expert Evaluation ◽  
Analysis Methods ◽  
Methodological Approaches ◽  
Structured Approach ◽  
Developing Strategies

The purpose of the article is to propose methodological assistance to the selfgovernment system in developing strategies for sustainable development of territorialcommunities. Methodology. The research used the works of foreign and domesticspecialists involved in the development of sustainable development strategies.Authors applied methods of scientific comparative analysis, general scientificmethods of theoretical, empirical research and a method of expert evaluation. Scientificnovelty includes clear well-structured approach to identification of what is neededto develop a strategy of territorial communities’ development, important tasks fora working group, SWOT analysis methods, what should be analysed to achievestrategic goals and consistency of organizational details. Conclusions. Complianceto the proposed scientific methodological approaches to the development of thestrategies of territorial communities’ sustainable development enables the communityto obtain a strategic algorithm for its sustainable development.

Website Information Security and Privacy Concerns in 4IR: The Moderating Role of Trust in B2C e-Commerce

10.29007/jlq6 ◽  
2019 ◽  
Author(s):  
Thabang Mofokeng
Keyword(s):  
South Africa ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Success Factors ◽  
Industrial Revolution ◽  
Emerging Market ◽  
Security And Privacy ◽  
Future Research ◽  
Privacy And Security ◽  
Customer Trust

The technology devices introduced in recent years are not only vulnerable to Internet risks but are also unable to elevate the growth of B2C e-commerce. These concerns are particularly relevant today, as the world transitions into the Fourth Industrial Revolution. To date, existing research has largely focused on obstacles to customer loyalty. Studies have tested e-commerce models guided by the establishment of trusting, satisfied and loyal consumers in various international contexts. In South Africa, however, as an emerging market, there has been limited research on the success factors of online shopping.This study examines the influence of security and privacy on trust, seen as a moderator of customer satisfaction, which in turn, has an effect on loyalty towards websites. Based on an exhaustive review of literature, a conceptual model is proposed on the relationships between security and privacy on the one hand, and customer trust, satisfaction and loyalty on the other. A total of 250 structured, self-administered questionnaires was distributed to a purposively selected sample of respondents using face-to-face surveys in Johannesburg, South Africa. A multivariate data analysis technique was used to draw inferences from the data. With an 80.1% response rate, the findings showed that privacy and security do influence customer trust; security strongly influences customer trust and weakly influences satisfaction. In South Africa, customer loyalty towards websites is strongly determined by satisfaction and weakly determined by trust. Trust significantly moderates the effect of customer satisfaction on loyalty. The study implications and limitations are presented and future research directions are suggested.

Directions of Accounting Environmental Factors for Forecasting Formation of Agroecosystems

2020 ◽  
pp. 31-35
Author(s):  
L.Z. Khalishkhova ◽  
◽  
A. Kh. Temrokova ◽  
I.R. Guchapsheva ◽  
K.A. Bogаtyreva ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Comparative Analysis ◽  
Environmental Factors ◽  
Environmental Safety ◽  
Logical Analysis ◽  
Management Decisions ◽  
The Sustainable Development ◽  
The Impact

Ensuring the sustainable development of agroecosystems requires research into the justification of the impact of environmental factors on the formation of territorial agroecosystems and identifies ways to take them into account in order to justify management decisions and ensure environmental safety. The main goal of the research within the article is to identify the most significant environmental factors in predicting the formation of agroecosystems. Provisions are devoted to the study of the laws governing the functioning of agroecosystems in order to increase their stability. The methods of comparative analysis, generalization, abstraction, logical analysis are applied. A number of provisions are formulated regarding ways to account for the influence of factors on the formation of key elements of agroecosystems.

scholarly journals Sustainable AI: AI for sustainability and the sustainability of AI

AI and Ethics ◽  
2021 ◽  
Author(s):  
Aimee van Wynsberghe
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Ecological Integrity ◽  
Idea Generation ◽  
Computing Power ◽  
The Sustainable Development ◽  
Sociotechnical System ◽  
Societal Values ◽  
Development Goals ◽  
Definition Of

AbstractWhile there is a growing effort towards AI for Sustainability (e.g. towards the sustainable development goals) it is time to move beyond that and to address the sustainability of developing and using AI systems. In this paper I propose a definition of Sustainable AI; Sustainable AI is a movement to foster change in the entire lifecycle of AI products (i.e. idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice. As such, Sustainable AI is focused on more than AI applications; rather, it addresses the whole sociotechnical system of AI. I have suggested here that Sustainable AI is not about how to sustain the development of AI per say but it is about how to develop AI that is compatible with sustaining environmental resources for current and future generations; economic models for societies; and societal values that are fundamental to a given society. I have articulated that the phrase Sustainable AI be understood as having two branches; AI for sustainability and sustainability of AI (e.g. reduction of carbon emissions and computing power). I propose that Sustainable AI take sustainable development at the core of its definition with three accompanying tensions between AI innovation and equitable resource distribution; inter and intra-generational justice; and, between environment, society, and economy. This paper is not meant to engage with each of the three pillars of sustainability (i.e. social, economic, environment), and as such the pillars of sustainable AI. Rather, this paper is meant to inspire the reader, the policy maker, the AI ethicist, the AI developer to connect with the environment—to remember that there are environmental costs to AI. Further, to direct funding towards sustainable methods of AI.

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