Digital Transformation Of Industry Research Articles

Effective Data Utilization in the Context of Industry 4.0 Technology Integration

We are part of the digital transformation of society and industry. The digital transformation of industry is based on new technologies brought about by the fourth industrial revolution. The Internet of Things (IoT), Cloud Computing, Cyber–Physical Systems (CPS) and Big Data provide the digital link between machines and individuals in processes. This completely new system is based on efficient data collection, data analysis and immediate interventions in organizational processes based on the results of the analysis. Smart organizations are driven by data and not by models. By working efficiently with the vast amounts of data available, the smart organizations of the future can ensure business sustainability, increase competitiveness through process optimization and reduce costs. In general, the aim of this paper was to identify the means to achieve a paradigm shift from traditional organizations to smart organizations through the use of data in the context of integrating Industry 4.0 technologies. The aim of the research was to determine the extent to which different Industry 4.0 technologies are applied in the effective use of data from specific activities/processes in industrial organizations to bring about a paradigm shift from traditional organizations to smart organizations. The first part of the paper describes the theoretical background of the transition from traditional to smart organizations using selected Industry 4.0 technologies. The second part of the paper characterizes the research objective, the methods used in the paper and the basic statistics used to determine the research questions and hypotheses. The next section evaluates the research questions and hypotheses that were used to meet the research objective. The last part of the paper is a summary of the obtained results, based on which we conclude that the primary challenge for organizations in the Slovak Republic is to learn how to work with the collected data, the need for their appropriate structuring and subsequent archiving, which is manifested by the need for training and application of data analysts in a broader context.

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode.

With the dynamic sharing of global resources shifted from the independent production of a single enterprise to and the development of collaborative manufacturing, today's manufacturing industry has collaborative production among multiple enterprises. This paper extends the optimization method of the multienterprise dynamic equipment collaborative scheduling system with the dynamic manufacturing network theory approach, combining the neural network system and model predictive control theory to continuously and dynamically diagnose the changes of demand and capacity in the actual production process and design the corresponding multilayer game collaborative optimization robust model according to the uncertainty relationship. The study considers the production characteristics and management needs of multiple enterprises within the manufacturing industry, as well as the optimization of the dynamic equipment collaborative scheduling system, uncertain demand, dynamic equipment matching effectiveness, and equipment failure under the shared manufacturing model, deduces the market capacity demand for manufacturing enterprises by inverse prediction of dynamic market demand through the simple recurrent neural network (Elman network), and then maximizes the idle equipment utilization and load capacity under the shared manufacturing model in model rolling optimization. This paper gives model solutions based on big data analysis and combines improved population intelligence algorithms to determine the dynamic matching of manufacturing resources, product supply and demand paths, and transportation flows. The simulation results show that the application of model predictive control theory to the model of Elman neural network system diagnosis can effectively reduce the adverse effects of the two-way uncertainty environment on the profitability of enterprises. Meanwhile, considering the maximum performance matching of dynamic equipment scheduling and combining with robust optimization methods will reduce the cost loss of manufacturing enterprises in the worst case, and the cost loss reduction can reach 24.73%. The analysis results verify that the method is of great practical significance in the direction of intelligent production collaboration and industrial digital transformation.

Towards the Next Decade of Industrie 4.0 – Current State in Research and Adoption and Promising Development Paths from a German Perspective

Considering the first ten years of Industrie 4.0 in Germany—the digital transformation of industry towards the goal of increased manufacturing productivity and mass customization—significant progress has been achieved. However, future efforts are required. This review first evaluates the status quo of implementation and research in Germany and finds that large-scale companies have proceeded faster than small- and middle-sized enterprises. Currently, regardless of their size, companies have in common a shortage of qualified specialists, coupled with a lack of adequate base technologies for Industrie 4.0 and an insufficient digital mindset. The creation of platform-based digital business models is particularly lagging behind, despite high research interest. This review subsequently identifies three research-driven fields of action that are particularly important for the future of Industrie 4.0: (1) resilience of value networks in the strategic area of sovereignty, (2) Open-Source as a driver for the strategic area of interoperability, and (3) the strategic combination of digitalization and sustainability as a basis for sustainable business models in the strategic area of sustainability.

Technology Acceptance, Adoption and Workforce on Australian Cotton Farms

The future of work is influenced by the digital transformation of industries, including agriculture. The current study aimed to understand the social drivers of automated technology acceptance and adoption in Australian cotton farms. The study employed a mixed-methods approach to compare those who were (a) currently using automated technology, (b) not currently using automated technology but considering adoption, and (c) not currently using automated technology and no intention to adopt. The research found that social factors and workforce considerations influence growers’ motivation to adopt automated technology on farms. Furthermore, differences on appraisals of perceived usefulness were observed when comparing growers with no intention to adopt automated technology with those considering adoption or who have adopted automated technology. Both perceived usefulness and ease of use barriers are challenges for those considering adoption of automated technology. Support that improves ease of use for those who have adopted automated technology is important for continued appraisals of perceived usefulness of automated technology. Further research to understand antecedents to appraisals of perceived usefulness and ease of use, and how these interact to influence acceptance and automated technology, is required to inform strategic workforce interventions that support the digital transformation of cotton farms.

A Digital Twin Model of the Smart City Communication Infrastructure

Digital transformation in Industry 4.0 today is associated with the transition to cyber-physical systems through the use of digital twin technologies, the industrial Internet, Big Data, Artificial Intelligence, Machine Learning, etc. In this work, an attempt of strict theory developing was made that would allow to determine, “what Digital Twin is” and to determine the place of the Digital Twin among other models. As a result, a cyber-physical Cross-Domain Model of Communication Service Provider and the model of the Digital Twin for the digital service provider was suggested. The models aim to meet new problems in communications management.

Probabilistic Message-Passing Control

There is insufficient current understanding of how to apply fully decentralized control to networks of sparsely coupled nonlinear dynamical subsystems subject to noise to track a desired state. As exemplars, this class of problem is motivated by practical requirements of creating decentralized power grids robust to cascade failures, the digital transformation of Industry 4.0 managing IoT connectivity reliably, and controlling transport flow in smart cities by computing at the edge. We demonstrate that an approach utilizing probability theory to characterize and exploit the uncertainty in locally received information, and locally optimized messages passed between neighboring subsystems is sufficient to implicitly infer global knowledge. Thus, control of a global state could be realized through decentralized control signals applied only to local subsystems using only local information without any reference to a global current state. Given a global system that can be decomposed into a set of locally coupled subsystems, we develop a theoretical method of probabilistic message passing and probabilistic control signals all interacting only at the subsystem level, but which promotes a system-wide convergence to a desired state. Our theoretical results are corroborated using computational experiments on a network of a 10-node partially coupled system decomposed into four separated subsystems with control inputs applied and determined at the subsystem level. Comparing the results with a centralized control method utilizing information from all the nodes to achieve global state convergence validates our hypothesis that local decentralized probabilistic control can be affected by the mechanism of local probabilistic message passing without needing access to global centralized information. We also provide a set of numerical experiments increasing the network size showing that the decentralized algorithm is independent of the global system size.

Service innovation in the restaurant sector during COVID-19: digital technologies to reduce customers' risk perception

PurposeThis paper investigates the extent to which the COVID-19 pandemic has accelerated service innovation in the restaurant sector. It explores the use of digital technologies (DT) as a safety-empowerment and resilient strategy in the food-service industry during the pandemic. It also investigates the impact of DT on customers' risk perception (CRP) and customers' intention to go to restaurants (CIR) in Italy.Design/methodology/approachBased on the theory of planned behaviour and perceived risk theory, this study investigates a sample of customers residing in Italy. Multiple regression and mediation analyses are conducted to test the research hypotheses, adapting the logic model developed. Using the bootstrapping technique, this study also explores whether the pandemic has moderated the relationship among several variables adapted from the literature. Robustness tests are also performed to corroborate the analysis.FindingsThe pandemic has accelerated the food-service industry's digital transformation, forcing restauranteurs to implement DT to survive. Findings show that DT support restauranteurs in implementing innovative services that reduce interactions and empower cleanliness among workers and customers, reducing CRP and preserving CIR. Thus, managing risk perception is helping the restaurant sector to recover.Practical implicationsPractical implications are presented for policymakers to catalyse the digital transformation in small- and medium-sized restaurants. The results may also be beneficial for entrepreneurs who can implement innovative service practices in order to reduce interaction and empower cleanliness levels. Moreover, academics can use these results to conduct similar research in other geographical contexts.Originality/valueThe present research represents the first study investigating the relationship between the use of digital technologies and the intentions of customers to go out for dinner during the ongoing pandemic in Italy.

Innovative effects of digital transformation of industrial enterprises

Subject. This article discusses the problems of implementation of Industry 4.0 technology. Objectives. The article aims to assess the level of digitalization of industrial enterprises in Russia. Methods. For the study, I used the methods of theoretical generalization, systems, logical, structural, and comparative analyses, and statistical methods. Results. The article proposes methods for assessing the innovative effects of the digital transformation of industry through indicators of patent activity and digitalization cost. The article also presents the systematized effects of the introduction of the most popular digital technologies used in industry. Conclusions. It is necessary to develop methodological tools for assessing the effects of digitalization of industrial enterprises in Russia.

Measuring industry digital transformation with a composite indicator: A case study of the utility industry

Climate change, urban sprawl, a global pandemic, and the general trend of digitizing economies are driving the need for digital transformation of utilities. Policymakers of industry digital transformation need some aggregated metric that captures the essence of this multidimensional concept, identifies the pros and cons of current policies, and guides future directions based on cross-country benchmarking. This study develops the Industry Digital Transformation Index for the utility industry (IDTIu) as a composite indicator. IDTIu provides an aggregate score for the digital transformation of the national utility industry based on 31 indicators grouped into 8 sub-indices. IDTIu scores were calculated for 34 European countries based on 2020 data. To avoid the methodological challenges of equal weighting and expert assessments, we applied the original Benefit-of-the-Doubt (BoD) model and its two extensions for the proportion and priority of sub- indices. Our results show that (i) the full flexibility of the original BoD model leads to the expected deficiencies in ranking leaders and assigning zeros to insignificant sub-indices; (ii) BoD model with ordinal sub-index share restrictions does not allow ranking of laggards when IoT, AI, and BDA are ranked as top 3 priorities. Therefore, at the current stage of the digital transformation of utilities, we recommend the BoD model with proportional sub-index share restrictions.

Digital transformation of the maritime industry: A cybersecurity systemic approach

Information system solutions are increasingly being applied to the maritime industry, and eventually, all aspects of maritime operations will be aided by the digital transformation of the industry. The maritime industry is a sector driven by compliance and historically deals with security and safety matters at an international level. It has been recognized that safety and security in maritime heavily depend on cyber systems and cybersecurity implementation requirements have started to get integrated into maritime's regulatory context. This paper focuses on cybersecurity in the maritime industry, presenting an inside view of maritime cybersecurity aspects and offering a detailed case study analysis based on a real-world company's approach. The main objective of the paper is, therefore, to connect research with practice, presenting a maritime company's cybersecurity systemic approach with references to procedures and policies. The findings of the case study and the cyber security vessel audit survey that is performed show that the company was able to assess its current status, collect evidence and objectively determine security gaps, and achieve cyber risks mitigation. The gained knowledge will be used in the future to continuously improve the company's systems and move to a more predictive and proactive maturity level.

The Present Situation and Future Prospect of Online Fitness in the Post-Epidemic Era

The Present Situation and Future Prospect of Online Fitness in the Post-Epidemic Era

The Fundamental Strategies that will Drive Higher Educational Sector Towards Digital Transformation in Industry 4.0.

The Fundamental Strategies that will Drive Higher Educational Sector Towards Digital Transformation in Industry 4.0.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ РОБОТИ ПІДПРИЄМСТВ МОРСЬКОГО БІЗНЕСУ ЗА РАХУНОК ВПРОВАДЖЕННЯ ЦИФРОВІЗАЦІЇ

Digital transformation is the introduction of modern information technologies into the business processes of an enterprise.Digitization of business processes is relevant not only at the level of individual enterprises. Whole industries choose this path of development as the only way to meet the ever-changing environmental conditions. Thanks to this, the digital transformation of industry, retail trade, the public sector and other areas is already changing the life of every person and every company today.Digital transformation of business processes is aimed at companies making quick decisions, quickly adapting their work to today's requirements and meeting the needs of their customers. The process of digitization did not bypass the port activity. Digitization of business processes in this field is quite active and effective.The article analyzes the main theoretical aspects of the implementation of digitalization of business processes in port activities. The main modern trends of digital transformation, which are relevant for the port industry today, have been identified. The authors reflect the main prospective directions of the introduction of digital technologies in the activities of port enterprises.In the course of the research, the general transformational processes taking place in port activity due to the introduction of digital innovations were analyzed.An analysis of the main effective IT-solutions used in the field of port cargo handling accounting was carried out. The authors also determined how digital technologies affect the safety of transportation and the optimization of the work of the personnel of port enterprises. As a result of the study, it was established that the implementation of digitalization of business processes significantly affects the overall efficiency of the sea ports. It was determined that the use of modern digital technologies makes it possible to improve the quality of ship service, increases the throughput capacity of the sea ports, and also increases the level of security during cargo transportation.Keywords: digital technologies, digital transformation, business models, digitization of business processes.

Assessment of digital transformation in the education system of Kazakhstan

Today, the issue of e-learning is very relevant. The concept of "informatics" has entered all spheres of our life, gradually transforming many processes of activity from mechanized to automated, with the help of information systems. In the modern world, we can no longer imagine many life cycles without informatization or digitalization. Digital transformation is the introduction of modern technologies into the business processes of an enterprise. This approach implies not only the installation of modern equipment or software, but also fundamental changes in approaches to management, corporate culture, and external communications. Digitalization of processes is relevant not only at the level of individual enterprises, but entire industries choose this path of development for themselves as the only opportunity to meet the rapidly changing conditions of the surrounding world. Thanks to this, the digital transformation of industry, trade, public sector, education, and other spheres is already changing the life of every person and every company. The article examines the current state of digital transformation in the education system based on the implementation of e-learning in the Republic of Kazakhstan, identifies the main problems in the implementation of state programs for digitalization of the national education system as a whole. The purpose of the article is to outline measures for the development of the education system in the direction of digitalization of processes based on the assessment of the implementation of e-learning.

5G, AI, and the Digital Transformation of Industry [Chapters

5G, AI, and the Digital Transformation of Industry [Chapters

IWeek experience: the innovation challenges of digital transformation in industry

Information Technology, communication, and innovation require specific, essential competencies that employers look for in engineers. Responding to this, Tecnologico de Monterrey has been implementing the Tec21 Educational model to foster students' competencies by involving them in challenge-based learning (CBL). The iWeek is one of the first implementations of this model, where students experience immersive learning for a whole week. This study presents the iWeek Innovation Challenge to improve students' innovation and information technology and communication skills through a CBL didactic technique. During this iWeek, a group of students developed efficient solutions with Microsoft Power Apps to solve real challenges confronting a global company. The results proved that students could quickly learn and apply knowledge and develop practical, innovative solutions to real problems in Industry. It was a revelation to the stakeholders to notice how fast students can become familiar with new information technology tools to propose solutions that positively impact the company. Strong partnerships between academia and industry are crucial to developing student disciplinary and transversal competencies by challenging them to solve real-life problems in real-world environments. This work presents a roadmap for planning and designing a CBL iWeek with an educational partner from Industry. It includes the implementation details, assessment instruments, and results analysis. Finally, we also highlight the significant contributions of iWeek to explain the value of this immersive experience in the teaching–learning process.

Industrial Digitalization in China: Literature Review and Research Prospects

Facing the new development stage of the "14th Five-year Plan", high-quality development has become the theme of China's economic and social development, putting forward new requirements for industrial digitalization. However, the depth and systematicness of the existing literature on China's industrial digitalization research is insufficient. In view of this, this paper systematically combs the research results and context of China's industrial digitalization, and combines specific economic practice, in order to promote the deepening and systematization of research in this field, and provide reference for promoting China's industrial digital transformation. Firstly, the connotation and denotation of industrial digitalization are defined based on relevant domestic researches, and its characteristics are clarified. Secondly, combined with the changes of industrial structure, this paper summarizes the three driving modes of China's industrial digitalization: the backward-forcing mode dominated by social motivation, the integration mode dominated by technology motivation and the value-added service mode dominated by innovation motivation. Thirdly, the formation mechanism of the industry chain being reshaped by digitalization is summarized and interpreted; Finally, the future research is prospected to provide feasible directions and ideas for further expanding and deepening the research of industrial digitalization in China.

Big Data as a Service and Application for Indian Banking Sector

The financial world, money markets, and the growth of an economy are all powered by banks. In India's financial sector, where cashless and paperless transactions are expanding quickly, bank fraud is also rising quickly, and fraudsters are now turning to cutting-edge techniques. Fighting financial crimes has always been a priority for the banking industry. Banks’ fraud losses will diminish when they are able use analytics and emerging technologies for better fraud prevention. Data has evolved into more than simply an IT resource; it is a vital feature of the banking industry's digital transformation in the present world. The majority of contemporary risk monitoring systems, which are based on data science discipline such as big data, allow for surveillance and offer Early Warning Signals (EWS) about any kind of worsening. The study of this paper signifies that how big data as a service and application proven as a strong and innovative instrument that assists Indian banking sector not only in identifying security issues and fraudulent behavior, but also in their early prevention. The experimental results of bibliometric study conducted on Scopus and Web of Science databases using in Vos Viewer software, clearly discovered big data technology is a crucial approach to deploy in Indian banking system. Findings of this paper will contribute to knowledge and practice of researchers working in this filed by increasing their understanding related to numerous big data technologies and techniques deployment for handling banking financial risks and its role in operational banking workflow management.

Стратегические приоритеты, технологии и инструменты неоиндустриализации России и Украины: отраслевой и пространственный аспекты

Abstract. In modern conditions, taking into account the development priorities of both Russia and Ukraine, one of the urgent tasks is to carry out neo-industrialization, which is especially important for ensuring their national security and leveling geopolitical threats. The purpose of the article is to substantiate the strategic priorities of the neo-industrialization of Russia and Ukraine in the context of sectoral and spatial aspects, as well as to systematize a set of technologies and tools for their implementation. The research methodology was based on the general and general scientific methods of cognition, as well as the concrete historical method, the comparative historical method, the method of the unity of the historical and the logical, the method of comparative analysis, statistical methods. As a result of the calculations (development coefficient), ratings of the priorities for the neoindustrialization of Russia and Ukraine by type of economic activity (in accordance with the Classification of Economic Activities) were compiled. Based on the study of foreign experience and comparison of the practice of neo-industrialization in Russia and in Ukraine, the general strategic priorities of the digital transformation of industry and the instrumental support of this process are formulated. The scientific novelty of the research lies in the development of methodological tools for substantiating the strategic priorities of neoindustrialization and systematization of classical, alternative instruments for its provision, including technological, financial, credit and institutional. The general strategic objectives of the neo-industrialization of Russia and Ukraine are formulated. It is substantiated that the formation of network integration structures in the most developed industries should be one of the priorities of its neo-industrialization. The practical significance of the research results is that within the framework of testing the developed scientific and methodological approaches, the diagnostics of the competitive advantages and weaknesses of the industry was carried out on the example of the two countries on the basis of a comparative analysis and strategic priorities were established, taking into account the sectorial and spatial aspects, which in the future may be accounting for the justification and implementation of strategic and operational decisions. Directions for further research will focus on quantifying the expected results of neo-industrialization in industry at the expense of the «digital» factor using econometric methods.

Deep Neural Network-Based Sorghum Adulteration Detection in Baijiu Brewing

In Baijiu brewing process, suppliers may adulterate glutinous sorghum with commercially inferior japonica sorghum, which can affect the yield and quality of the final Baijiu production. Currently, sorghum adulteration detection in Baijiu brewing process in China is carried out manually by sampling and observation, which strongly depends on the experiences of workers. In this paper, we proposed a method that uses sorghum images as input and combines image processing and deep neural networks to identify grain varieties and calculate the adulteration ratio. Two derivative networks of CNN, i.e., ResNet and SqueezeNet are used to implement the deep neural networks for sorghum grain identification and adulteration ratio calculation. The classification accuracy of the ResNet and SqueezeNet based models reached 93.34% and 87.98% on test set, respectively. The root mean squared error (RSME) for adulteration ratio estimation is 4.95% and 7.73%, respectively. The mean absolute error (MAE) is 4.20% and 6.29% accordingly. The proposed pipeline is capable of realizing rapid and non-destructive adulteration detection of raw materials in industrial production, thus conducing to the industrial digital transformation and efficiency improvement.