Towards Innovative Production Model: Digital Transformation in Small and Medium Enterprises

This research addresses digital transformation as the strategic utilization of advanced technologies such as big data, artificial intelligence (AI), and the Internet of Things (IoT) investigating its importance in the Lebanese context with an aim of enhancing operational efficiency and innovation within the existing business models. The study employed a robust empirical methodology that combines data analysis and textual analysis of annual reports, exploring both the direct and mediated impact of digital transformation on healthcare organization performance. A key finding is the identification of a threshold effect, indicating that significant performance improvements are contingent upon reaching certain levels of digital maturity. The analysis further elucidates how digital transformation varies in impact based on institutional digital readiness, offering a granular understanding of its benefits. This insight prompts a call for policymakers and healthcare providers in Lebanon and the wider Arab region to develop digital capabilities and supportive policies that encourage an efficient and innovative healthcare environment. By contributing nuanced perspectives on digital transformation’s role in healthcare, the study not only advances academic discourse but also serves as a foundation for future research across varied geographic contexts. It emphasizes the strategic development recommendations for healthcare providers, aiming to bridge the gap between current practices and the potential of digital technologies to transform healthcare delivery and management. The findings underscore a positive correlation between digital transformation and enhanced performance in healthcare, with notable improvements in efficiency and cost management emerging as enterprises reach and surpass digital maturity thresholds. The study concludes that while digital transformation offers substantial benefits, its success is predicated on strategic implementation and the attainment of a critical level of digital sophistication. Ultimately, this research advocates for a targeted approach in digitalization efforts, emphasizing the need for healthcare organizations to adopt adaptive digital strategies that are in harmony with their developmental stage and resource capabilities.

Intelligent organizational systems or intelligent organizations are being created through processes of digital transformation (DT) that typically involves intelligent technologies such as robotic process automation (RPA), the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), blockchain, drones, virtual reality (VR), 3D printing, big data and big data analytics (BDA). However, there is the significant problem of high DT failure rates with severe financial losses. Thus, understanding the key constructs for DT success in terms of organizational performance is an essential knowledge requirement and was the research problem and knowledge gap addressed by the study. The study followed an integrative literature review method since the purpose was analysis and synthesis for research model development. The study exposed the key constructs for successful DT in terms of organizational performance, namely organizational performance, sustainability, information technology (IT)/digital capability, change management, dynamic capabilities and risk management. This knowledge enables DT management to effectively manage DT toward success. For DT and related scientists, the study establishes the foundations for measuring DT and the associated organizational performance and facilitates DT theory progression and new knowledge generation.

In the article, the authors reveal the directions of solving the problem of forming effective methodological support for the digital transformation management system of industrial enterprises. The main focus of the work is on the development of the enterprise’s management system and digital infrastructure based on Industry 4.0 technologies in a high-risk environment and uncertainty. The study presents a methodology for assessing the level of digital maturity of an industrial enterprise and its infrastructure built on the basis of Internet of Things (IoT) technologies. The developed author’s methodology includes a system of criteria for assessing the level of digital maturity, a mathematical model and a system of recommendation characteristics of the values obtained based on the evaluation results. Based on the results of the evaluation system, it is possible to conduct a comparative analysis of the level of digital maturity of the infrastructure and infrastructure of selected business entities. The model can also be used to analyze and make management decisions about the initiation of IoT projects and the allocation of resources between strategic projects of digital transformation of an industrial enterprise. The developed mathematical model is a point-based one. IoT projects that form the digital infrastructure of enterprises have been selected as objects of digital transformation research. The novelty of the research lies in the development of a methodology for assessing the maturity level of the IoT infrastructure in the digital transformation project management system to improve the efficiency of project management processes. The main purpose of this research is to develop tools to support the management decision-making process in the digital transformation project management system. The proposed developments can be applied to the management of projects for the digital transformation of industrial enterprises and the development of IoT infrastructure. When developing a methodology for assessing the level of digital maturity and infrastructure built on the basis of IoT technologies, system analysis methods such as analysis and synthesis, expert method and mathematical modeling were used. Their complex combination made it possible to work out all the elements of the methodology, as well as to systematize the composition of the evaluation criteria, taking into account the qualitative and quantitative reflection of the characteristics of the digital infrastructure.

The Industrial Revolution 4.0, marked by the accelerated adoption of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, and other technologies, has significantly impacted various business sectors. One sector that has been fundamentally affected is accounting, which has had to adapt to these changes to remain relevant and efficient in an increasingly complex and dynamic business environment. This study aims to understand and analyze how digital transformation in the Industrial Revolution 4.0 era has affected the field of accounting. The research method used is descriptive-qualitative research. This research reveals that digital transformation in accounting, supported by technologies such as IoT, artificial intelligence, big data, and others, brings operational efficiencies through automation, increases the accuracy of financial data, and drives deep data analysis. Collaboration and communication are also enhanced through a cloud-based platform. However, data protection and privacy are important issues in the digital era. In the Industrial Revolution, 4.0, technologies such as big data analytics, AI, IoT, cloud computing, and blockchain are bringing fundamental changes, enabling accounting to be more proactive, using data analysis for better predictions and customer service, and more intelligent and responsive decision making. Digital transformation continues to change the landscape of the accounting and financial industry, making careful use of the potential of the digital age.

Digital transformation in the field of oil and Gas industry is already a significant impact creator. It is actually act like catalyst through which the overall functionality of the oil and gas industry get enhanced and the overall output with the help of technologically-advanced mechanism, increased up to manifold. In the present scenario, the over-all quest is not just about the volume of the oil and petroleum, but it is also regarding the overall value generated throughout the process. And such enhanced level of value generation is taking place with great pace with the help of enhanced level of implementations of different types of technologies in different type of activities related to the oil and gas industry. In the present scenario, oil and gas industry’s business model is no longer depending upon just the inflated and narrow based value-chain mechanism. It is actually depending upon the almost all modernized and futuristic technologies. The modern technologies include big data analytics, 3D printing technology, cyber security, digital marketing, Artificial Intelligence, Internet of Things, drone technologies, database management system, etc. all these technologies are not only supports in handling the overall business capability of the oil and Gas Industries, but also eliminate the overall negative impact generating elements. With the help of technologies and digital transformation, the overall profitability of the oil and gas industry enhanced. Digital transformation is a prominent and significant impact creator which is not limited to the oil and gas industry, but also reaching up to the all-global level Businesses. It is transforming the overall business operations by enhancing the speed of innovation and making the use of practical knowledge base which ultimately enhance the overall power of operations and increase efficiencies. With the emergence of digital transformation technologies especially with the emergence of big data analytics, the Internet of Things and Artificial Intelligence have supports several types of innovative and new ways of developing and transforming the overall market as well as the customer satisfaction in significant manner. All such innovative technologies and digital transformations are contributing significantly in shaping the future of oil and gas industry

The study identifies and substantiates the periods of digital transformation and introduction of artificial intelligence in administrative services in Ukraine with a focus on regulatory and legal support. The periods of digital transformation in Ukraine are divided into informatization (1991-2000), digitalization (2000-2019), and digital transformation (2019-present). Informatization includes the creation of national information systems and telecommunications infrastructure. Digitalization is characterized by data integration and process automation, such as the launch of e-government, edemocracy, decentralization, and automation of public services. Digital transformation includes the strategic restructuring of social and business processes using technologies such as the Internet of Things, artificial intelligence, cloud computing, big data, and others. To formulate the periods of digital transformation, we considered global trends in the development of AI in the world. Several periodization’s were taken into account, including the ten-year periodization of AI development, which covers key stages: from its inception in the 1950s, the ups and downs of public interest in the 70s and 90s, to the modern era of large language models. Another periodization, based on technological and investment criteria, identifies such stages as the formation of machine intelligence, the heyday of expert systems, the “winter of AI,” the era of deep learning, and the “spring of AI.” This study highlights the complexity and multifactorial nature of the processes of AI formation and digital transformation. Particular attention is paid to the analysis of the legal and regulatory framework in these areas in Ukraine. This makes it possible not only to assess the achievements but also to identify potential vectors of development in the context of sustainable development.

The workplace dynamics have been significantly reshaped by the digital transformation, which has been driven by advancements in technologies such as the Internet of Things (IoT), machine learning, artificial intelligence (AI), and big data analytics. This transformation not only results in substantial job design alterations to enhance productivity but also poses challenges in achieving work–life balance and gender equality. This chapter centres on the pivotal role of inclusive leadership in effectively navigating these changes, specifically in its promotion of gender equality and sustainable practices within the framework of digital transformation. In light of the transition towards a more adaptable and project-based work setting, it becomes imperative for leaders to embrace approaches that champion long-term growth and inclusivity. This systematic review examines several fundamental inquiries: (1) How does inclusive leadership facilitate digital transformation while also promoting sustainable practices? (2) What are the implications of digital transformation on gender equality in the workplace? (3) How do inclusive leadership styles impact gender participation in digital initiatives? (4) Which organisational outcomes are impacted by inclusive leadership during digital transformation? The authors argue that inclusive leadership has a dual effect: it improves organisational outcomes and acts as a mediator in attaining gender equality and organisational justice. These factors are pivotal for ensuring sustainable development in the age of the digital industrial revolution. This chapter aims to analyse the theoretical frameworks, examine the existing literature, present empirical findings, and propose future directions for effectively incorporating inclusive leadership and gender equality into the narrative of digital transformation.

The pharmaceutical industry is undergoing a significant transformation driven by the integration of digital technologies, collectively known as Industry 4.0. This shift is redefining how drugs are developed, manufactured, and distributed. Key technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics are at the forefront of this change, enabling smart manufacturing, real-time process optimization, and enhanced supply chain management. IoT facilitates the creation of interconnected production environments where sensors and devices continuously monitor critical parameters, ensuring optimal conditions and predictive maintenance. AI accelerates drug discovery through predictive modeling, automates quality control processes, and employs predictive analytics to enhance maintenance and process improvement. Big data empowers data-driven decision-making, ensures regulatory compliance through comprehensive analysis, and supports the shift toward personalized medicine by enabling customized drug production. Despite the significant benefits, the adoption of these technologies poses challenges, including integration with existing systems, data security concerns, and navigating a complex regulatory landscape. This review explores these technologies' impact on pharmaceutical manufacturing, highlighting successful case studies and best practices. Additionally, it discusses the future directions, including the move towards fully autonomous systems and the importance of collaboration between tech companies, manufacturers, and regulators to drive innovation and ensure compliance. The continued evolution of digital technologies in pharma manufacturing promises to enhance efficiency, reduce costs, and deliver more personalized treatments.

With the evolution and upgrading of the fifth-generation mobile communication technology, the mobile network will support a larger mobile user group and more diverse business scenarios, greatly enhance the Internet service experience, and fully support IoT-aware applications. As the forefront of the Internet of Everything, the wireless access network plays an important role in the evolution of the network. The continuous innovation of a new generation of information technology, especially the application of technologies such as big data, cloud computing, artificial intelligence, and the Internet of Things, has triggered an upsurge in the digital transformation of enterprises. Digital transformation has had a profound impact on the production and operation activities of enterprises and has also changed the organization and management of enterprise innovation activities. From the perspective of the innovation ecosystem, the digital transformation of a single enterprise leads to changes in innovation activities, which will converge and emerge new characteristics at the system level. The purpose of this article is to study the full scenarios of 5G communication access in the digital transformation of medium and large enterprises. This article starts with the development trend of wireless access networks, analyzes network slicing technologies in detail, and points out that wireless access networks are moving towards 5G. Important challenges are faced in the development of the times. Based on the adaptive random access model proposed in this paper, a feedback adaptive optimization method is proposed. This method estimates the real access load through feedback and calculates the access threshold through the access load. Then, broadcast the threshold value to each user equipment through downlink broadcast. The results show that the normalized throughput rate of random access methods using AC-RACH and FC-RACH is higher than that of RA-RACH. Especially, when the arrival rate of user requests is getting higher and higher, the throughput rate of RA-RACH access will continue to decrease with the collision until it approaches zero. By comparing the key data of normalized throughput rate and access success rate, it is concluded that the feedback adaptive method has a significant performance improvement under heavy load conditions compared with the previous method.

ABSTRACTThis study investigates the potential driving forces and strategies for implementing supply chain management 4.0 (SCM 4.0) in pharmaceutical manufacturing industries (PMIs). Pertinent data were collected from 111 related companies using a mixed‐methods research approach. The study used IBM SPSS and AMOS version 21 for exploratory and confirmatory factor analysis, respectively. The driving forces include regulatory and compliance, market, technological, and economic drivers, while research, development, and innovation emerged as the first‐ranked strategy. With the manufacturing landscape in Tanzania transitioning towards digital transformation, implementing SCM 4.0 is essential. Digital transformation in PMIs can improve supply chain performance by enabling predictive analytics, real‐time tracking, and better resource optimisation. Incorporating digital technologies like the Internet of Things, artificial intelligence, blockchain technology, and big data analytics is crucial for PMIs to maintain competitiveness and resilience in a globalized market. The digital transformation can boost efficiency, precision, and regulatory compliance while mitigating SCM risks. The transformation in deploying advanced robotics and automating the production systems within the PMIs can assist in streamlining the manufacturing workflows, diminishing human errors, and ultimately increasing the PMI outputs. Likewise, collaboration between PMIs, academia, research institutions, and government agencies is essential for knowledge sharing and addressing common PMIs' challenges. PMIs should be customer‐focused and use SCM 4.0 technologies to improve competitiveness and satisfy changing customer demands. Likewise, to develop new technology and business models, it is essential to support innovation and entrepreneurship through funding programs, incubators, and hubs.

Due to the irruption of new technologies in cities such as mobile applications, geographic information systems, internet of things (IoT), Big Data, or artificial intelligence (AI), new approaches to citizen management are being developed. The primary goal is to adapt citizen services to this evolving technological environment, thereby enhancing the overall urban experience. These new services can enable city governments and businesses to offer their citizens a truly immersive experience that facilitates their day-to-day lives and ultimately improves their standard of living. In this arena, it is important to emphasize that all investments in infrastructure and technological developments in Smart Cities will be wasted if the citizens for whom they have been created eventually do not use them for whatever reason. To avoid these kinds of problems, the citizens’ level of adaptation to the technologies should be evaluated. However, although much has been studied about new technological developments, studies to validate the actual impact and user acceptance of these technological models are much more limited. This work endeavors to address this deficiency by presenting a new model of personalized recommendations based in the technology acceptance model (TAM). To achieve the goal, this research introduces an assessment system for tourists’ digital maturity level (DMT) that combines a fuzzy 2-tuple linguistic model and the analytic hierarchy process (AHP). This approach aims to prioritize and personalize the connection and communication between tourists and Smart Cities based on the digital maturity level of the tourist. The results have shown a significant correlation between technology usage and the potential for personalized experiences in the context of tourism and Smart Cities.

I. Abstract The implementation of Industry 4.0 technologies in the manufacturing sector has opened up new pathways for sustainability by improving resource efficiency and minimizing waste. The emergence of cyber-physical systems, the Internet of Things (IoT), artificial intelligence, big data analytics, and digital twins supports the development of intelligent and sustainable manufacturing practices. This research synthesizes key insights from existing literature to propose a comprehensive framework aimed at waste reduction and resource efficiency in manufacturing through the application of Industry 4.0. The literature suggests that digital transformation is instrumental in facilitating lean manufacturing, circular economy practices, and sustainable waste management. Hybrid multi-criteria decision-making methodologies, smart factory frameworks, and machine learning-based predictive models have demonstrated enhancements in productivity, reductions in waste, and improvements in environmental sustainability. Case studies spanning various industries, from micro, small, and medium enterprises (MSMEs) to large-scale manufacturing, highlight the role of Industry 4.0 in optimizing supply chains, conserving energy, and enhancing overall equipment effectiveness (OEE). Despite these advancements, challenges such as high implementation costs, lack of standardization, and environmental concerns regarding Industry 4.0 technologies persist. Addressing these challenges necessitates a structured roadmap that incorporates lean methodologies, circular economy principles, and sustainable innovation. The findings of this study provide practical recommendations for policymakers, industry professionals, and researchers to harness the potential of Industry 4.0 for sustainable manufacturing. Through the utilization of smart automation, data-driven decision-making, and collaborative digital ecosystems, the manufacturing sector can significantly contribute to achieving Sustainable Development Goals (SDGs) while maintaining competitiveness in the global market. Future research should focus on overcoming barriers to adoption and refining frameworks for the seamless integration of Industry 4.0 in sustainability-focused manufacturing. Keywords: Industry 4.0, sustainable manufacturing, resource efficiency, waste reduction, cyber-physical systems, Internet of Things (IoT), artificial intelligence, big data analytics, digital twins, lean manufacturing, circular economy, smart factory, predictive models, environmental sustainability, multi-criteria decision-making (MCDM), overall equipment effectiveness (OEE), Sustainable Development Goals (SDGs), data-driven decision making, manufacturing innovation, digital transformation

This paper explores the impact of digital transformation in the age of Industry 4.0 on business models and operations, with a specific focus on literature published between 2018 and 2024. The emergence of Industry 4.0 has brought about significant transformations in traditional business frameworks and operational processes. This is primarily due to the advancements in technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. This research explores various important topics, including the development of digital business models, the effects of new technologies on operational efficiency, and the long-term viability of digital transformation efforts. The findings indicate that in order to stay competitive and ensure long-term viability, businesses need to constantly innovate their models and strategically incorporate advanced technologies. The study highlights the significance of change management and organizational culture in enabling successful digital transformation. This paper offers a thorough examination of various sources, providing a well-rounded perspective on how businesses can effectively navigate the complexities of Industry 4.0. The insights gained from this research contribute to fostering a business environment that is flexible and capable of withstanding challenges.

​The OECD Science, Technology and Industry Scoreboard 2017 draws on the latest international comparative data to uncover the strength of the OECD and other large economies, and shows how the digital transformation is affecting science, innovation, the economy and the way people work and live. Mobility, cloud computing, the Internet of Things (IoT), artificial intelligence (AI) and big data analytics are among the most important technologies in the digital economy today, empowering businesses, consumers, and society as a whole. However, their development and use are distributed very unevenly. The headquarters of the top 2,000 R&D corporations worldwide are concentrated in just a few economies – notably the United States, Japan, and China – and about 70% of their total R&D spending is concentrated in the top 200 firms. Although the digital transformation is affecting all sectors of the economy, Telecommunications and IT services are consistently ahead in terms of digital intensity, while Agriculture, Mining, and Real estate are consistently ranked at the bottom. Significant differences remain in a majority of OECD countries, including between younger and older generations, between women and men, by educational background, urban and local locations, and firms of different size. This publication aims to help governments design more effective science, innovation, and industry policies in the digital era.

Purpose This paper aims to fill important research gaps by understanding how companies implement digital transformation (DT), what technologies are being applied, what practices are being used in the DT process and how they can add value to companies. Design/methodology/approach The research method utilized is a multiple-case study conducted with companies from the retail segment. This approach allows for an in-depth analysis of these companies’ strategies, technologies and practices in their DT processes. Findings The results indicate that the strategies and practices applied by the companies highlight the evolving nature of organizational functions. The standard digital technologies used by the three companies are digital platforms, mobile devices and communication tools. Only companies A and C stand out for using big data analytics, the Internet of things (IoT) and artificial intelligence (AI). Interviews revealed that companies are following the DT offensive but are also working internally to accelerate the DT journey, startup acquisition, new positions, innovation lab creation and agile concepts. Originality/value By addressing the research questions, we can systematically identify and categorize key strategies, practices, challenges and outcomes associated with DT in the retail sector. This can lead to developing a comprehensive framework that guides retail companies in implementing effective DT initiatives.