scholarly journals HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

2021 ◽  
Vol 1 ◽  
pp. 1755-1764
Author(s):  
Rongyan Zhou ◽  
Julie Stal-Le Cardinal
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Network Models ◽  
Global Supply Chain ◽  
Great Opportunity ◽  
Strategic Research ◽  
Complex Graph ◽  
The World ◽  
Tremendous Challenge

Abstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

scholarly journals Humanity–Organization–Technology in View of Industry 4.0 / Society 5.0

2021 ◽  
Vol 50 ◽  
pp. 1-11
Author(s):  
Andrzej Kiepas ◽  
Keyword(s):  
Industry 4.0 ◽  
The Other ◽  
Technological Determinism ◽  
Human Beings ◽  
Cultural Changes ◽  
Digital Society ◽  
Technological Factors ◽  
Loss Of Autonomy ◽  
The World

The article addresses selected problems related to the perspective on the development of Industry 4.0 and social and cultural changes that accompany this development and lead toward the so-called post-digital society. In the field of industry, the changes concern, among others, the functioning of various organizations, and in the perspective of post-digital society – human beings and their relations with the world of technology. These changes lead to an increase in the role of technological factors, hence the current revival of technological determinism, and this, in turn, has to do with questions regarding human subjectivity. In this context, questions regarding humans also revolve around the need to acknowledge their increasing capabilities and scope of freedom, and on the other hand, their loss of autonomy in relation to the world of technology.

scholarly journals THE ROLE OF BOARDING SYSTEM UNIVERSITY EDUCATION IN INCULCATING PROBLEM SOLVING SKILL IN THE 4IR ERA

2020 ◽  
Author(s):  
Ihwan Mahmudi ◽  
Ahmad Saifullah ◽  
Amal Fathullah Zarkasyi ◽  
Ahmad Danis
Keyword(s):  
Problem Solving ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Qualitative Approach ◽  
University Education ◽  
Data Presentation ◽  
Interactive Analysis ◽  
The World

Problem Solving is the first competency needed in the era of Industry 4.0”or the 4th Industrial Revolution (4IR) according to the World EconomicForum 2016. This study aimed: 1) to measure student’s problem solvingskill in University of Darussalam (UNIDA) Gontor, and 2) to explore howUniversity of Darussalam (UNIDA) Gontor with its boarding systemeducation inculcate problem solving skill to its students. This studyemployed a qualitative approach with case study as a research design. Thedata were collected by questionnaire, observation, interview, anddocumentary method. They were analysed using interactive analysis modelconsisting three components; data reduction, data presentation, andconclusion or data verification which is done together with the process ofcollecting data interactively by cycle process.The study found that: 1) moststudents of University of Darussalam (UNIDA) Gontor have considerabledegree of problem solving skill, and 2) the skill was inculcated throughlearning process in the classroom, various activities within studentorganization and a large number of assignments and tasks that students haveto fulfil.

Studying the Issues of Global Digitalization and its Impact on the Organizational Culture of the Enterprise

2021 ◽  
pp. 68-72
Author(s):  
A. Kalenova
Keyword(s):  
Organizational Culture ◽  
Theoretical Analysis ◽  
Industry 4.0 ◽  
New Technologies ◽  
Active Process ◽  
Organizational Processes ◽  
The World ◽  
The Impact

The article is devoted to the theoretical analysis of the concept of organizational culture of the organization. The influence of an active process in the world is the process of digitalization and its impact on the organizational culture of the organization. The analysis of the intensity of the transition of organizations to "Industry 4.0" at the present time is carried out. A key element in the process of transition to innovations has been identified, namely, personnel. A number of problems that enterprises face when implementing new technologies are highlighted. During the consideration of the problem, the provisions that need to be taken into account when switching to innovations are highlighted. The stages of implementing changes in the organizational culture, which will help to reduce the resistance to the introduction of changes, are considered. Specific examples of the impact of organizational culture on organizational processes are presented. In general, the role of organizational culture in the organization is indicated.

Machine learning techniques applied to industrial engineering: a multi criteria approach

2019 ◽  
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Giuseppina Piscitelli ◽  
Raffaele Cioffi ◽  
Antonella Petrillo
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Technological Development ◽  
Predictive Maintenance ◽  
Industrial Engineering ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The World ◽  
The Subject ◽  
To Receive

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

Impact Analysis of Machine Learning Techniques for COVID-19 Diagnosis: A Critical Review

2021 ◽  
Vol 12 (2) ◽  
pp. 510-516
Author(s):  
Anshul, Et. al.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Severe Acute Respiratory Syndrome ◽  
Impact Analysis ◽  
Machine Learning Techniques ◽  
X Ray ◽  
Learning Techniques ◽  
The World ◽  
Almost All

COVID-19 virus belongs to the severe acute respiratory syndrome (SARS) family raised a situation of health emergency in almost all the countries of the world. Numerous machine learning and deep learning based techniques are used to diagnose COVID positive patients using different image modalities like CT SCAN, X-RAY, or CBX, etc. This paper provides the works done in COVID-19 diagnosis, the role of ML and DL based methods to solve this problem, and presents limitations with respect to COVID-19 diagnosis.

scholarly journals Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

2021 ◽  
Vol 10 (6) ◽  
pp. 424
Author(s):  
Behrouz Pirouz ◽  
Aldo Pedro Ferrante ◽  
Behzad Pirouz ◽  
Patrizia Piro
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Bayesian Network ◽  
Negative Impact ◽  
Virus Infections ◽  
The Third ◽  
Complex Problems ◽  
Probabilistic Technique ◽  
The World

Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.

scholarly journals Global Supply Chain Management At Printko Ink Company

2011 ◽  
Vol 6 (4) ◽  
Author(s):  
Raida Abuizam
Keyword(s):  
Network Models ◽  
Global Supply Chain ◽  
Location Problems ◽  
Fixed Cost ◽  
Model Formulation ◽  
Single Product ◽  
Spreadsheet Model ◽  
Decision Variables ◽  
Warehouse Location ◽  
The World

The Printko Ink Company case illustrates how network models can be used as an aid in spreadsheet model formulation. It also enriches students’ knowledge how to use integer linear programming with binary (0-1) variables in dealing with fixed cost plant and warehouse location problems. Students completing the Printko Ink case will be able to develop a spreadsheet model that will solve for many logistic decision variables.  It will help students decide where or whether to manufacture Printko Ink single product and how to get it to its customers around the world in the most economical manner.

Industry 4.0 as the Last Industrial Revolution and Its Opportunities for Developing Countries

2021 ◽  
pp. 1113-1128
Author(s):  
Egemen Hopali ◽  
Özalp Vayvay
Keyword(s):  
Developing Countries ◽  
Competitive Advantage ◽  
Emerging Economies ◽  
Value Chain ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Business Partners ◽  
The World ◽  
New Perspective

In this chapter, better understanding of Industry 4.0 is presented by investigating the role of different technologies and business partners on success of Industry 4.0. Enablers for smart factory are discussed in detail, and how to match these enablers with value chain partners of Industry 4.0 are identified as a new perspective on Industry 4.0. Furthermore, the aim of this chapter is to present actions to be taken from the point of the emerging economies to sustain and increase competitive advantage by catching and implementing Industry 4.0. Consequently, Industry 4.0 can enable developing countries to get a bigger slice of the world manufacturing value chain.

scholarly journals Clustering: Finding Patterns in the Darkness

2021 ◽  
pp. 1-28
Author(s):  
Hector Menendez
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Industry 4.0 ◽  
Expert Knowledge ◽  
Large Datasets ◽  
Decision Making Process ◽  
Clustering Problem ◽  
Multiple Strategies ◽  
Data Similarity ◽  
The World

Machine learning is changing the world and fuelling Industry 4.0. These statistical methods focused on identifying patterns in data to provide an intelligent response to specific requests. Although understanding data tends to require expert knowledge to supervise the decision-making process, some techniques need no supervision. These unsupervised techniques can work blindly but they are based on data similarity. One of the most popular areas in this field is clustering. Clustering groups data to guarantee that the clusters’ elements have a strong similarity while the clusters are distinct among them. This field started with the K-means algorithm, one of the most popular algorithms in machine learning with extensive applications. Currently, there are multiple strategies to deal with the clustering problem. This review introduces some of the classical algorithms, focusing significantly on algorithms based on evolutionary computation, and explains some current applications of clustering to large datasets.

scholarly journals Emergence of Futuristic HRM in Perspective of Human - Cobot’s Collaborative Functionality

2021 ◽  
Vol 10 (5) ◽  
pp. 292-296
Author(s):  
Amit Saxena ◽  
Abhay Saxena ◽  
Rajeev Sharma ◽  
Mukesh Parashar
Keyword(s):  
Human Resource ◽  
Paradigm Shift ◽  
Industry 4.0 ◽  
Smart Manufacturing ◽  
Advanced Manufacturing ◽  
Cognitive Computing ◽  
Collaborative Robots ◽  
The World ◽  
Effective Role

Industry 4.0 buzzed out with a theme of “Smart Manufacturing for the Future”. With the advent of Industry 5.0, the world of technology is registering a paradigm shift from Customization to Personalization. Advanced Manufacturing, Cognitive Computing, AI, Robotics, Collaborative Robots, where all these technologies altogether introduces Industry 5.0 onto the stage. Cobot’s will be part of Human Resource Management. This Paper aims to visualize the Futuristic HRM and to understand the Collaborative Robots performance with Human HR’s. In this study, we had discussed the possible issues related to human-robot collective functionality from the organizational and HRM perspective. We had also suggested the effective role of HR and Cobot HR while dealing with the human and machine employees. We believe that the issues identified in this study will pave way for many upcoming organizational robotics research studies.

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