Digital technologies represent an important factor in the achievement of the sustainability goals of the European Green Deal. Whether the digitalisation trend will contribute to the sustainable development of societies in the long term depends on how we shape and understand it. The aim of this paper is to create a concept for the management of the threats and opportunities in the digital transformation of SMEs. As part of a broader research project, a complex tool was developed to assess the digital maturity of SMEs, and our paper focuses on one aspect of this tool, which is related to the identification and management of the opportunities and threats that are associated with digital transformation trends. The methodology that was used to develop this paper is based on a pilot survey that involved 154 SMEs in the Czech Republic, 74 of which were used for the follow-up survey on the opportunities and threats. Sixteen key trends were identified, which were then reduced to five latent factors, with their names forming five dimensions. In the cluster analysis, the companies were divided into three clusters, which served as the basis for determining the final cluster centres. The result is a concept for the management of the threats and opportunities in the digital transformation of SMEs. Digital transformation is the broad topic. Therefore, the theoretical overlap of this study can be seen in the narrower focus on the identification and management of the opportunities and threats in digital transformation. In addition, this study focuses on SMEs because there are more than one million small and medium-sized enterprises in the Czech Republic, and they create 54% of the innovative solutions. The practical impact of this study can be understood in the fact that the proposed concept may enable SMEs to more easily manage threats and opportunities in their digital transformation.

Research background: Cognitive computing and robotic technologies, enterprise digital twin system modeling, and sensory perception algorithms optimize industrial big data exchange and production collaboration, production floor management, and smart device 3D simulation and visualization in the Industry 5.0 metaverse and virtual shop floor environments. Enterprise metaverse business operations, multi-granularity cognitive computing, and industrial big data fusion simulation integrate virtual and augmented reality technologies, collaborative robotic and industrial cyber-physical production systems, and artificial intelligence-enabled edge computing and Internet of Everything devices in mobile edge computing environments. Cloud-based production and digital twin Internet of Things networks, 3D immersive virtual reality and realistic 3D scene construction technologies, and cyber-physical production and business process management systems articulate smart production engineering and management, artificial intelligence-driven physics simulation, and Internet of Things-based robotic manufacturing in highly realistic industrial product representations and 3D virtual spaces with regard to big data-driven business decisions. Purpose of the article: We show that 3D immersive virtual reality and digital twin metaverse technologies, spatial scanning modeling, and autonomous robotic and virtual factory simulation systems are pivotal in immersive 3D process management, industrial manufacturing production value, and knowledge accumulation in synthetic simulated environments. 3D simulation-based industrial processes and immersive experiences can be attained through cognitive computing and robotic technologies, multi-modal information fusion, autonomous intelligence generation, and multiple production process management in immersive 3D metaverse environments. Immersive, multisensory, and augmented digital experiences can be attained through 3D factory simulation and immersive extended reality technologies, cognitive robotic process automation, autonomous robotic and industrial machine learning systems, and task allocation optimization in computer-generated 3D virtual environments. Methods: We analyzed and synthesized common operations for the first 60 companies in industrial metaverse on ensun (AI-based supplier sourcing tool’s) website in terms of key takeaway, working industry, type of company, and specialized areas, and identified three main topics. Findings & value added: The main value added derived from our research is that industrial metaverse 3D simulation and modeling, digital twin and remote fault diagnosis technologies, multiphysics simulation and predictive maintenance tools assist industrial big data monitoring and management, Internet of Things-based robotic manufacturing, and multiple processing tasks in 3D digital twin factories. Collaborative autonomous manufacturing operations, artificial intelligence-driven physics simulation, and smart industrial devices and processes necessitate industrial metaverse decentralized federated learning, cognitive computing and robotic technologies, and cognitive digital twins in virtual shop floor environments, generating economic value. 3D simulation and visualization technologies, business intelligence and digital twin-based cyber-physical production systems, and big data-driven forecasting and real-time collision detection algorithms can be harnessed in robotic automation processes, intelligent manufacturing upgrading, and sustainable industrial value creation across 3D digital twin factories and distributed computing environments.

The article presents a new approach to the generation of business models based on value streams. The originality of the study lies in the assumption that in every business model the generator of added value is the value chain which, if original enough, will maximize potential while minimizing costs. This approach will provide the respective enterprise with a high level competitiveness in the long term. The tested group of enterprises consisted of 354 SMEs, one hundred eightyseven of which were from the manufacturing and industry sector, with 167 from the services sector. The novelty of this approach also consists of the integrated approach to the business environment in which the value streams were analyzed. The results suggest that the analysis of value streams is suitable for the projection of business models. In addition, the methods of dimensional reduction and logit regression have been identified as appropriate analytical tools, with representative results found valid and utilizable in business practice. The authors of the article are aware that this is only the first step in addressing this issue, one which will play a critical role in the gradual process of the upcoming digital transformation of all business models, not only those of SMEs in the Czech Republic.

This paper presents a method of generating a business strategy using value chain analysis. There were 186 manufacturing and industrial enterprises from the Czech Republic. The analysis was carried out using mathematical–statistical methods (dimensional reduction, logit regression and its transformation in order to objectify the opinion level of the managers) and with a neural network in terms of validation of the results of the mathematical–statistical methods. The aim was to determine the significance of different parts of the value chain in terms of their impact on the profitability of an enterprise and to demonstrate its important role in the process of generating business strategy. The significance for the profitability of the enterprises was statistically proven in the area of scientific and technological development, input logistics and human resource management. These parts were identified by the authors as the golden triangle of manufacturing and industry. Purchasing and output logistics were identified as the parts with a negative impact to the profitability of the enterprises. Strong underestimation of scientific and technological development by the top managers of the manufacturing and industrial enterprises is seen as a very negative finding. Neural networks showed higher statistical sensitivity compared to the mathematical–statistical methods (dimensional reduction and logit regression). They defined the value sector chain with the following structure (ranked from the highest degree of positive impact on the profitability of the enterprise): human resource management, scientific and technological development, production, input logistics, purchasing, material management, output logistics, enterprise infrastructure, marketing and sales, service and other supporting services. In conclusion, it is stated that the sector value chain of manufacturing and industrial enterprises will be further decomposed into value chain models of specific industries of the production and industry, such as engineering and construction, intended for direct use in different business entities to generate their unique value chains and corporate strategies.

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

The world’s economy has been significantly affected by the ongoing pandemic crisis. Its logical reflection is a deteriorating condition of national and regional economies and their vehicles—enterprises. Negative effects of the crisis have influenced both the manufacturing sector and the services sector, where some segments have been hit fatally. In the corporate sphere in general, including in the services sector, there has been a growing need to change the approach to corporate strategy. The strategy should be generated based on management of value-creating processes with the objective to maximize satisfaction of customers while minimizing costs. This is also the topic of this paper. A representative sample of small and medium enterprises from the services sector have been surveyed about their size, using mathematical statistics and models that have been proposed to maximize profit probability. The objective was to propose models that maximize probability of profit and, at the same time, minimize costs for each combination of value chain activities (input logistics, provision of services, output logistics, marketing and sale, servicing, and other accompanying activities, purchasing material management, science-technical development, human resources management and company infrastructure). The main outcome of this paper is the fact that we have been able to prove the functionality of the tested model, which has demonstrated the growing importance of value stream management in relation to the size of enterprises. Such a universal and structured model provides a practical tool for the definition of economic-financial, investment and personnel policies in enterprises.