Industry 4.0, a revolution that requires technology and national strategies

Redefining fan manufacturing: Unveiling industry 5.0's human-centric evolution and digital twin revolution

Industry 5.0 has the capacity to surpass the technology -oriented efficiency of Industry 4.0 and advance sustainable development objectives such as prioritizing human needs, ensuring socio-environmental sustainability, and enhancing resilience. Digital twins and simulation technologies improve manufacturing, evaluate products and operations, and predict any potential adverse consequences. With digital twin technology, everything that exists in the physical world will eventually be duplicated in the digital realm. Within the context of Industry 5.0, this study aims to investigate the impact of digital twin technology on the fan manufacturing sector. The proposal for implementing the enabling industry 5.0 application was presented to the chief engineers of eight distinct fan manufacturers. Out of these, five responded positively and their feedback was subsequently followed up on. Three different avenues such as production, supply chain, and testing transparency were proposed for industry 5.0 implementation. The exploration of testing transparency is being undertaken based on a consensual decision. The Web of Things standard that enables digital twin generation in industry 4.0 is implemented to enable the testing transparency. This data was linked with the internal digital twin of fan motor created using ANSYS. This digital twin can predict the lifespan of the motor by analyzing the temperature of the motor housing surface. Toward sustainability and resilience with Industry 4.0 and Industry 5.0 may provide insights into this alignment.

Advancing nursing education through wearable electronic devices: A scoping review

AIM

To examine the incorporation of wearable electronic devices in the education of undergraduate nursing students.

BACKGROUND

The advancement of technology has influenced nursing education and will continue to do so in the future. Wearable technologies are electronic devices that can be worn as an accessory and expand the possibilities in nursing education with increased engagement in the learning process.

DESIGN

A scoping review was conducted following JBI and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines.

METHODS

The search was performed on August 25th, 2023, in the databases: MEDLINE via PubMed, ISI Web of Science, ERIC, EBSCOhost CINAHL, EBSCOhost Academic Search Premier, EBSCOhost Health Source Nursing, EMBASE, Scopus, BVShost LILACS and ProQuest. Literature that examined the application of wearable electronic devices in the education of undergraduate nursing students was included.

RESULTS

This scoping review study included a total of 21 records published between 2014 and 2023. The analysis showed that smart glasses are the most common wearable electronic device used in nursing education, followed by smartwatches. The devices provide an opportunity for competencies development, especially when applied in the simulation environment, such as physical examination and medication administration. Wearable technologies are potentially useful and feasible as learning tools in nursing education, increasing nursing students' motivation, confidence and satisfaction.

CONCLUSIONS

In the technological revolution, educators must consider the potential of innovative teaching strategies, such as wearable electronic devices, to advance nursing education. Wearables can contribute to developing competencies required for the professionalism of undergraduate nursing students.

End-to-End Solution for Analog Gauge Monitoring Using Computer Vision in an IoT Platform

The emergence of Industry 4.0 and 5.0 technologies has enabled the digital transformation of various processes and the integration of sensors with the internet. Despite these strides, many industrial sectors still rely on visual inspection of physical processes, especially those employing analog gauges. This method of monitoring introduces the risk of human errors and inefficiencies. Automating these processes has the potential, not only to boost productivity for companies, but also potentially reduce risks for workers. Therefore, this paper proposes an end-to-end solution to digitize analog gauges and monitor them using computer vision through integrating them into an IoT architecture, to tackle these problems. Our prototype device has been designed to capture images of gauges and transmit them to a remote server, where computer vision algorithms analyze the images and obtain gauge readings. These algorithms achieved adequate robustness and accuracy for industrial environments, with an average relative error of 0.95%. In addition, the gauge data were seamlessly integrated into an IoT platform leveraging computer vision and cloud computing technologies. This integration empowers users to create custom dashboards for real-time gauge monitoring, while also enabling them to set thresholds, alarms, and warnings, as needed. The proposed solution was tested and validated in a real-world industrial scenario, demonstrating the solution's potential to be implemented in a large-scale setting to serve workers, reduce costs, and increase productivity.

Digital inclusion to enhance energy sustainability: public participation and environmental governance in the new media era to achieve energy sustainable goals

Renewable energy not only helps to safeguard the environment and slow down climate change but also supports economic growth and energy security. The significance of renewable energy sources is expanding as more people throughout the globe understand how important it is to switch to clean energy sources. Therefore, empirics are in search of the factors that can promote renewable energy production. This analysis investigates some of the novel determinants of renewable energy production, such as digital inclusion, public participation, and environmental governance, which have not been examined previously in any study. For empirical analysis, the study employs the ARDL and QARDL estimation techniques using Chinese data from 1998Q1 to 2021Q4. The analysis findings confirm that digital financial inclusion, ICT, and GDP are vital in boosting both short and long-run renewable production. Green investment, environmental governance, and carbon emissions also significantly and favourably impact long-run renewable energy production. In the Quantile ARDL model, digital financial inclusion is positively linked to renewable energy production at most of its quantiles in the short and long run, while the ICT, GDP, environmental governance, and carbon emissions are positively linked to renewable energy in most quantiles in the long-run only. The Wald test confirms the asymmetric impact for all variables in the long run, which implies that policymakers should consider the positive and negative changes in these factors while devising policies for enhancing renewable energy production.

Impact of waste management among Industry 4.0 and sustainable development

The study is aimed at investigating the impact of waste management in the context of Industry 4.0 and sustainable development. Data were collected from 257 production managers in the industrial sector using a survey questionnaire and analyzed using SPSS and PLS-SEM. The findings indicated that Industry 4.0 and waste management significantly contribute to achieving sustainable development. The integration of Industry 4.0 technologies and effective waste management practices can help organizations implement sustainable development goals. Practical implications include assisting organizations in implementing Industry 4.0 technologies and waste management strategies based on the 3Rs principle. This can lead to reduced environmental impacts and improved resource efficiency, contributing to sustainable development. Policymakers can also benefit from the study's insights to address waste management challenges and promote sustainable development. The study's originality lies in its incorporation of the cyber-physical system and niche theory to explore how Industry 4.0 can facilitate sustainable waste management. It highlights the transformative potential of Industry 4.0 in the industrial sector, particularly in developing countries. Overall, this research offers a unique contribution to understanding waste management within the context of Industry 4.0 and sustainable development.

A systematic review of the application of immersive technologies for safety and health management in the construction sector

INTRODUCTION

The construction industry employs about 7% of global manpower and contributes about 6% to the global economy. However, statistics have depicted that the construction industry contributes significantly to workplace fatalities and injuries despite multiple interventions (including technological applications) implemented by governments and construction companies. Recently, immersive technologies as part of a suite of industry 4.0 technologies, have also strongly emerged as a viable pathway to help address poor construction occupational safety and health (OSH) performance.

METHOD

With the aim of gaining a broad view of different construction OSH issues addressed using immersive technologies, a review on the application of immersive technologies for construction OSH management is conducted using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) approach and bibliometric analysis of literature. This resulted in the evaluation of 117 relevant papers collected from three online databases (Scopus, Web of Science, and Engineering Village).

RESULTS

The review revealed that literature have focused on the application of various immersive technologies for hazard identification and visualization, safety training, design for safety, risk perception, and assessment in various construction works. The review identified several limitations regarding the use of immersive technologies, which include the low level of adoption of the developed immersive technologies for OSH management by the construction industry, very limited research on the application of immersive technologies for health hazards, and limited focus on the comparison of the effectiveness of various immersive technologies for construction OSH management.

CONCLUSIONS AND PRACTICAL APPLICATIONS

For future research, it is recommended to identify possible reasons for the low transition level from research to industry practice and proffer solutions to the identified issues. Another recommendation is the study of the effectiveness of the use of immersive technologies for addressing health hazards in comparison to the conventional methods.

Mechanical and Structural Properties of Polyhydroxybutyrate as Additive in Blend Material in Additive Manufacturing for Medical Applications

Today, additive manufacturing (AM) is considered one of the vital tenets of the industry 4.0 revolution due to its high productivity, decentralized production and rapid prototyping. This work aims to study the mechanical and structural properties of polyhydroxybutyrate as an additive in blend materials and its potential in medical applications. PHB/PUA blend resins were formulated with 0 wt.%, 6 wt.%, 12 wt.% and 18 wt.% of PHB concentration. Stereolithography or an SLA 3D printing technique were used to evaluate the printability of the PHB/PUA blend resins. Additionally, from FESEM analysis, a change was observed in PUA's microstructure, with an additional number of voids spotted. Furthermore, from XRD analysis, as PHB concentration increased, the crystallinity index (CI) also increased. This indicates the brittleness properties of the materials, which correlated to the weak performance of the tensile and impact properties. Next, the effect of PHB loading concentration within PHB/PUA blends and aging duration towards the mechanical performance of tensile and impact properties was also studied by using analysis of variance (ANOVA) with a two-way method. Finally, 12 wt.% of PHB/PUA was selected to 3D print the finger splint due to its characteristics, which are compatible to be used in finger bone fracture recovery.

A high-performance framework for personal protective equipment detection on the offshore drilling platform

In order for the offshore drilling platform to operate properly, workers need to perform regular maintenance on the platform equipment, but the complex working environment exposes workers to hazards. During inspection and maintenance, the use of personal protective equipment (PPE) such as helmets and workwear can effectively reduce the probability of worker injuries. Existing PPE detection methods are mostly for construction sites and only detect whether helmets are worn or not. This paper proposes a high-precision and high-speed PPE detection method for the offshore drilling platform based on object detection and classification. As a first step, we develop a modified YOLOv4 (named RFA-YOLO)-based object detection model for improving localization and recognition for people, helmets, and workwear. On the basis of the class and coordinates of the object detection output, this paper proposes a method for constructing position features based on the object bounding box to obtain feature vectors characterizing the relative offsets between objects. Then, the classifier is obtained by training a dataset consisting of position features through a random forest algorithm, with parameter optimization. As a final step, the PPE detection is achieved by analyzing the information output from the classifier through an inference mechanism. To evaluate the proposed method, we construct the offshore drilling platform dataset (ODPD) and conduct comparative experiments with other methods. The experimental results show that the method in this paper achieves 13 FPS as well as 93.1% accuracy. Compared to other state-of-the-art models, the proposed PPE detection method performs better on ODPD. The method in this paper can rapidly and accurately identify workers who are not wearing helmets or workwear on the offshore drilling platform, and an intelligent video surveillance system based on this model has been implemented.

Blockchain-Internet of Things Applications: Opportunities and Challenges for Industry 4.0 and Society 5.0

Today, blockchain is becoming more popular in academia and industry because it is a distributed, decentralised technology which is changing many industries in terms of security, building trust, etc. A few blockchain applications are banking, insurance, logistics, transportation, etc. Many insurance companies have been thinking about how blockchain could help them be more efficient. There is still a lot of hype about this immutable technology, even though it has not been utilised to its full potential. Insurers have to decide whether or not to use blockchain, just like many other businesses do. This technology keeps a distributed ledger on each blockchain node, making it more secure and transparent. The blockchain network can operate smart contracts and convince others to agree, so criminals cannot make mistakes. On another side, the Internet of Things (IoT) might make a real-time application work faster through its automation. With the integration of blockchain and IoT, there will always be a problem with technology regarding IoT devices and mining the blockchain. This paper gives a real-time view of blockchain-IoT-based applications for Industry 4.0 and Society 5.0. The last few sections discuss essential topics such as open issues, challenges, and research opportunities for future researchers to expand research in blockchain-IoT-based applications.

Revisiting the bullwhip effect: how can AI smoothen the bullwhip phenomenon?

Purpose

Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.

Design/methodology/approach

Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.

Findings

The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.

Originality/value

By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.

Effect of print parameters on additive manufacturing of metallic parts: performance and sustainability aspects

In this study, the effects of print parameters on the mechanical properties of additively manufactured metallic parts were investigated using a tensile test. The 17-4 PH stainless steel specimens with two print parameters, including infill density and pattern orientation, were fabricated by additive manufacturing (AM) using the bound metal deposition (BMD) technique. The mechanical properties considered in this study are the Young's modulus and ultimate tensile strength. The results demonstrate that the pattern orientations do not affect the Young's modulus of the infill specimen with the triangular pattern. In contrast, the ultimate strength significantly varies depending on the pattern orientations, where the samples with the pattern orientation of zero degrees yield the best ultimate strength. In fact, the mechanical properties of infill specimens increase with their infill density. However, when operating cost and time are considered, an index for estimating performance and sustainability is consequently established. The relationship between the normalized ultimate strength of an infill specimen and the relative density is defined as the weight efficiency. The index for assessing a sustainable product is characterized by the weight efficiency versus sustainable parameter(s). The index can help end users select an appropriate infill density for AM products by considering the operating cost and time. Different cost models, including material-only costs, direct costs, and total costs, can be included in the index model to assess a sustainable product in a particular cost context.

Contributions of Industry 4.0 to resilience achievement in the context of COVID-19 pandemic

In the totally unprecedented context of the COVID-19 health crisis, the widespread adoption of Industry 4.0 technologies, and the great interest in resilience, have been stronger than ever. Within this framework, the present paper outlines the involvement of technologies emerging from the fourth industrial revolution in the fight against the epidemic expansion, and the results of this implication in terms of strengthening and achieving resilience in diverse fields. In order to gain a fuller understanding of these points, fourteen resilience domains related to the COVID-19 pandemic are defined. On the other hand, the third section of this paper digs into the literature to expose a variety of Industry 4.0 solutions developed to cope with the sanitary crisis. Afterwards, a fuzzy cognitive map is elaborated, using mental modeler, in order to emphasize the causal links between Industry 4.0 technologies and resilience domains. Subsequently, a simulation of this model is performed to evaluate the contribution of an optimized joint use of Industry 4.0 core technologies in the achievement of resilience in its different dimensions during the COVID-19 pandemic, and to discuss how the identified gaps or weaknesses can be addressed.

Control System Development and Implementation of a CNC Laser Engraver for Environmental Use with Remote Imaging

This article is aimed at studying the features of the control systems development for a small-sized Computer Numerical Control (CNC) portative laser engraver. The CNC is implemented in mobile maintenance and repair platforms for remote sensing of the environment where the wild environment may not allow us to access the animals and places. The proposed work in this paper is based on recent research, which shows that applying the automated CNC speeds up the processes of repair, modernizes the equipment size, and significantly reduces the economic costs; accordingly, the authors developed a block diagram of a portable CNC laser engraver. The choice of the hardware was also made, taking into account the possibility of quick replacement in the field, which reduces the repair time and the cost of the developed layout. A control system based on the selected modules was synthesized, and a stability check was carried out using MatLab tools. To check the correctness of the developed control system, the authors developed and assembled an experimental layout to illustrate the results of engraving on such a layout. Finally, the stability and sensitivity of the proposed system have been obtained and proved that the system works in a comfortable zone of stability. The obtained results show that the proposed CNC laser engraver has achieved the expected improvements (high speed, small size, short production and repairing time, minimum human influence factor, and achieving a better outcome).

Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

Intelligently driven performance management: an enabler of real-time research forecasting for innovative commercial agriculture

Research in commercial agriculture is instrumental to achieve the targets set under the second Sustainable Development Goal (SDG), i.e., ‘Zero Hunger by 2030.’ Execution of research for the success of commercial agriculture becomes a tedious task and research organizations have been long struggling to assess their performance unequivocally in the face of COVID-19. Any evacuation plan in place to improve the performance, monitoring, and evaluation of a research institute must guarantee that the institute is on the right momentum and let it evades metrics-obsessed research drives during this pandemic. A survey was conducted through the participation of the topmost administrators attached to key research institutes working on agriculture in Sri Lanka to explore the current performance management practices deeply. The conclusions derived from a thematic analysis of the survey data were used to propose a set of solutions that facilitate a well-thought research agenda in a digitally transformed performance management system. The solutions imply that intelligently driven key performance measurements worked by artificial intelligence and big data could be used with policy innovations to support research integrity and assessment security within the coexistence of humans and machines for the well-being of research development in the commercial agriculture sector.

Industry 4.0 Wireless Networks and Cyber-Physical Smart Manufacturing Systems as Accelerators of Value-Added Growth in Slovak Exports

Industry 4.0 integrates smart and connected production systems that are pivotal in predicting and supporting production in real-time, leading to sustainable organizational performance. In manufacturing, it may increase productivity, sustainability, and energy efficiency, while optimizing competitiveness. The main purpose of this paper is to determine the impact of Industry 4.0 on the Slovak economy through a secondary data analysis in the automotive industry, which is the leading sector in the country. The paper aims to provide a comprehensive analysis of the various opportunities that are available in the value-added growth of car exports in Slovakia. It also explores the case study of PSA Group Slovakia, which highlights the importance of the Industry 4.0 concept in boosting the country’s export growth. The paper proposes a series of recommendations and steps to improve Slovakia’s innovation environment.

Knowledge Management in Society 5.0: A Sustainability Perspective

Organizations require the means to navigate Society 5.0. This is a knowledge-intensive society where a sustainable balance must be created for social good through a system that integrates cyberspace and physical space. With significant data, information and insight exchange based on knowledge in people and machines, organizations need to make sense of the notion that knowledge assets are the central structuring elements for sustainable development. By considering the key aspects of knowledge management (KM) in Society 5.0 as they relate to sustainable development, organizations may leverage their KM capability and learning agility to successfully address the unique requirements of the new society, environment and goals for sustainable development. In this research, automated content analysis was applied to identify key KM aspects using the Leximancer software. A total of 252 academic papers were analyzed, identifying 10 themes related to key KM concepts in Society 5.0 as they pertain to sustainability. The KM concepts identified were described and mapped to the sustainability triple bottom line. They comprised three primary and three intersecting dimensions, i.e., the environment (planet), society (people) and economic performance (profit) in the socio-economic, eco-efficiency and socio-environmental domains. The most significant themes included “knowledge”, “human”, “companies”, “information” and “system”. Secondary themes included “innovation”, “development”, “resources”, “social” and “change”.

Design and implementation of fault-tolerant control strategies for a real underactuated manipulator robot

This paper presents the design and implementation of four control strategies applied to a real underactuated manipulator robot with 3-DOF (Degrees of Freedom). Additionally, an original methodology for controlled oscillatory compensations is designed and implemented to mitigate the effect of a passive joint on the overall performance of this manipulator robot. The objective of this methodology is to create controlled oscillations that allow the faulty link and its (passive) joint to physically align with their adjacent previous link. The implemented control techniques are sinh–cosh, neural compensation, gain scheduling PID, and gain scheduling sinh–cosh. The real robot in which these four control strategies and oscillatory compensation methodology are implemented is a SCARA (Selective Compliant Assembly Robot Arm) robot. To assess controller performance—once the 3-DOF underactuated manipulator robot starts its trajectory—after t = 4.5 s, a fault is activated in its joint No. 2, converting it into a passive joint. The performance indicators IA (index of agreement), RMS (Root Mean Square), and RSD (Residual Standard Deviation) are used to analyze, compare, and evaluate the behavior of the four control strategies and the compensation methodology.

Multi-Unit Serial Polynomial Multiplier to Accelerate NTRU-Based Cryptographic Schemes in IoT Embedded Systems

Concern for the security of embedded systems that implement IoT devices has become a crucial issue, as these devices today support an increasing number of applications and services that store and exchange information whose integrity, privacy, and authenticity must be adequately guaranteed. Modern lattice-based cryptographic schemes have proven to be a good alternative, both to face the security threats that arise as a consequence of the development of quantum computing and to allow efficient implementations of cryptographic primitives in resource-limited embedded systems, such as those used in consumer and industrial applications of the IoT. This article describes the hardware implementation of parameterized multi-unit serial polynomial multipliers to speed up time-consuming operations in NTRU-based cryptographic schemes. The flexibility in selecting the design parameters and the interconnection protocol with a general-purpose processor allow them to be applied both to the standardized variants of NTRU and to the new proposals that are being considered in the post-quantum contest currently held by the National Institute of Standards and Technology, as well as to obtain an adequate cost/performance/security-level trade-off for a target application. The designs are provided as AXI4 bus-compliant intellectual property modules that can be easily incorporated into embedded systems developed with the Vivado design tools. The work provides an extensive set of implementation and characterization results in devices of the Xilinx Zynq-7000 and Zynq UltraScale+ families for the different sets of parameters defined in the NTRUEncrypt standard. It also includes details of their plug and play inclusion as hardware accelerators in the C implementation of this public-key encryption scheme codified in the LibNTRU library, showing that acceleration factors of up to 3.1 are achieved when compared to pure software implementations running on the processing systems included in the programmable devices.

Apache Spark and MLlib-Based Intrusion Detection System or How the Big Data Technologies Can Secure the Data

Since the turn of the millennium, the volume of data has increased significantly in both industries and scientific institutions. The processing of these volumes and variety of data we are dealing with are unlikely to be accomplished with conventional software solutions. Thus, new technologies belonging to the big data processing area, able to distribute and process data in a scalable way, are integrated into classical Business Intelligence (BI) systems or replace them. Furthermore, we can benefit from big data technologies to gain knowledge about security, which can be obtained from massive databases. The paper presents a security-relevant data analysis based on the big data analytics engine Apache Spark. A prototype intrusion detection system is developed aimed at detecting data anomalies through machine learning by using the k-means algorithm for clustering analysis implemented in Sparks MLlib. The extraction of features to detect anomalies is currently challenging because the problem of detecting anomalies is not actively and exhaustively monitored. The detection of abnormal data can be effectuated by using relevant data that are already in companies’ and scientific organizations’ possession. Their interpretation and further processing in a continuous manner can sufficiently contribute to anomaly and intrusion detection.

Storage assignment optimization for fishbone robotic mobile fulfillment systems

Robotic Mobile Fulfillment System (RMFS) affects the traditional scheduling problems heavily while operating a warehouse. This paper focuses on storage assignment optimization for Fishbone Robotic Mobile Fulfilment Systems (FRMFS). Based on analyzing operation characteristics of FRMFS, a storage assignment optimization model is proposed with the objectives of maximizing operation efficiency and balancing aisle workload. Adaptive Genetic Algorithm (AGA) is designed to solve the proposed model. To validate the effectiveness of AGA in terms of iteration and optimization rate, this paper designs a variety of scenarios with different task sizes and storage cells. AGA outperforms other four algorithm in terms of fitness value and convergence and has better convergence rate and stability. The experimental results also show the advancement of AGA in large size FRMFS. In conclusion, this paper proposes a storage assignment model for FRMFS to reduce goods movement and travel distance and improve the order picking efficiency.

Assessing the level of digital maturity of enterprises in the Central and Eastern European countries using the MCDM and Shannon's entropy methods

The process of global economic digitalization is a natural stage of evolutionary changes resulting from a dynamic development of information and communication technologies. Having appreciated the importance and advantages of digital transformation, individual countries began to strive to introduce it as soon as possible. In this context, it is important to study the level of digital maturity in Central and Eastern Europe, where the level of digitization is relatively low. This article assesses the level of digital readiness of enterprises in these countries based on 14 determinants characterizing the most important areas of the digitalization process. The research was carried out for 11 countries from the region, both for all and manufacturing enterprises. Multi-criteria analysis aimed at assessing the digital maturity of countries were performed using the Multi-Criteria Decision-Making methods (the TOPSIS, MOORA, VIKOR), and entropy methods for delineating the weights of the determinants. In order to obtain an unambiguous assessment of the determined digital maturity, the mean-rank method was applied. The method of multidimensional scaling allowed for the analysis of similarities between the countries in question. The results showed that the level of digital maturity in the Central and Eastern Europe countries varies greatly and is lower than in other European Union countries.

Critical Factors of Industry 4.0 Implementation in an Emerging Country: Empirical Study

The concept of Industry 4.0 is becoming more and more popular all over the world. The implementation of its assumptions in business practice changes the way companies operate. The enormous innovative potential of the Industry 4.0 concept and the intensive use of processes based on the implementation of advanced technologies in its assumptions have an impact on various industries in each country. The article attempts to analyze the critical factors for the implementation of Industry 4.0 in Kazakhstan. Primary and secondary data sources were used for this purpose. The majority of existing government initiatives and reports do not reflect the actual situation. Additionally, these materials do not always represent the experiences or positions of all the parties involved. Opinions of companies and organizations implementing Industry 4.0 solutions remain unexplored. The primary goal of the paper is to fill the cognitive gap by analyzing stakeholder responses and identifying the actual level of their awareness of the development of Industry 4.0. An additional intention of the authors was to empirically establish barriers that companies face while implementing the most desirable technologies, as well as to establish other critical factors, taking into account the specificity of the country under study. The results of the research can be used by policymakers, scientists and other stakeholders to develop forecasts and strategic plans, as well as to develop and conduct further research on the implementation processes of Industry 4.0 in Kazakhstan.