# Main Trend Topics on Industry 4.0 in the Manufacturing Sector: A Bibliometric Review
## 1 Introduction  
The manufacturing industry is crucial for economic development, innovation, and social progress, providing well-paying jobs and significantly contributing to GDP [1]. With Industry 4.0 (14.0 see Abbreviation section), also known as the Fourth Industrial Revolution, the sector is transforming through digital technologies, enhancing its impact. This transformation is driven by advancements such as cyber-physical systems, the Internet of Things (IoT), artificial intelligence, big data analytics, and digital twins, which collectively contribute to automated and interconnected manufacturing [2, 3, 4, 5]. Technologies like IoT, Big Data, and cloud computing enhance productivity and flexibility in manufacturing [3, 6, 7]. According to [7], Industry 4.0 technologies are predominantly applied in smart manufacturing focusing on production programming and control, indicating a trend towards smarter production and more efficient systems. Smart factories represent a significant shift from traditional manufacturing practices. They integrate physical and cyber technologies, using advanced sensors and IoT to improve performance, quality, control capability, management, and transparency of manufacturing processes [8]. They leverage digital technologies, AI, and automation to facilitate real-time communication between the factory and the market, supporting dynamic adaptationand maximizing efficiency [9]. Advanced manufacturing capabilities drive innovation, especially in high-wage regions [10]. Smart manufacturing systems require a shift from traditional manufacturing philosophies, incorporating intelligent system requirements to remain competitive in the future market [11]. This shift requires support and understanding from top management, and the performance of existing production systems influences perceived benefits and adoption intentions [12]. Benefits of smart factories include improved quality, safety, cost savings, increased productivity, and a competitive edge over traditional production systems [13]. The transition to Industry 4.0 presents challenges that require comprehensive understanding and systematic methodologies for effective implementation [3; 6; 14]. This complex process is fraught with challenges. According to [15; 16], implementing Industry 4.0 technologies is hindered by management and organizational adjustment challenges, including the need for strategic planning and the development of new competencies. Companies face difficulties integrating cyber-physical systems, big data analytics, and other technologies into existing manufacturing processes [15; 17]. Other challenges include infrastructure transformation and financial requirements [18]. The perception and impact of Industry 4.0 opportunities and challenges vary by company size, industrial sector, and whether the company is a provider or user of Industry 4.0 [16]. Additionally, refs. [19; 20] add that such transformation poses multi-level technological challenges to local production systems, especially SMEs, which may require integrated place-based industrial policies to leverage opportunities and mitigate threats. The current state of Industry 4.0 implementation varies across different industrial sectors, with significant advancements and strategic initiatives underway in countries like Germany and China [4]. SMEs are actively working to adopt Industry 4.0 principles, using frameworks and models to facilitate this transition [21]. In logistics, the potential for transformative change is recognized, although the concept has yet to be fully realized in practice [22]. Overall, Industry 4.0 is shaping the future of manufacturing and logistics, with ongoing efforts to overcome challenges and fully harness the benefits of this industrial evolution. The Fourth Industrial Revolution represents a paradigm shift in manufacturing, driven by the integration of digital technologies such as cyber-physical systems, the Internet of Things, artificial intelligence, and big data analytics.