# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
###### Abstract
Existing studies often focus on the individual benefits of technologies like the Industrial Internet of Things (IIoT) and Additive Manufacturing (AM). For example, the IIoT has shown potential in optimizing resource efficiency and predictive maintenance, while AM significantly reduces material waste and the environmental footprint. However, the combined effects of these technologies on sustainability have not been adequately explored. Figure 1: The three main themes of Industry 4.0. [PERSON] and [PERSON] (2021) [4; 5; 6] emphasized that research on Industry 4.0 technologies is fragmented, with a focus on short-term outcomes such as energy savings and material reduction. Long-term sustainability impacts, particularly the social dimension, are often neglected. This study aims to address these gaps by employing the Fuzzy Integration-Rough Set Theory (FI-RST) model to evaluate the combined impact of Industry 4.0 technologies on resource efficiency, waste reduction, and social well-being in the manufacturing sector. Manufacturing Execution Systems (MES), IIoT, and AM, when integrated, can significantly improve decision making, production efficiency, and waste reduction. Studies have demonstrated that while each technology has clear benefits, their combined implementation could lead to more substantial sustainability outcomes [7; 8]. For example, combining MES with the IIoT enhances real-time visibility and predictive analytics, reducing operational downtime and energy consumption [9]. Similarly, AM contributes to precise production and minimizes emissions from logistics (Figure 2). In the Chinese context, [PERSON] et al. (2020) [10] highlighted the role of Industry 4.0 technologies in improving sustainability despite challenges such as high costs and a lack of skilled labor. These findings underscore the need for further research on the combined effects of these technologies in emerging markets. The FI-RST model employed in this study provides a comprehensive quantitative framework for evaluating the socio-economic and environmental impacts of Industry 4.0 technologies. By applying this model to 36 manufacturing firms in Fujian Province, China, this research addresses the limitations of previous studies, which often rely on quantitative metrics without accounting for subjective and uncertain sustainability factors. While existing studies have illustrated the benefits of individual technologies, they often fail to explore the synergies between them. For instance, the combination of MES, IIoT, and AM can magnify their collective impact, particularly when integrated with Information and Communication Technology (ICT) systems, which further improve data flow and decision making [10; 11]. Additionally, there is a noticeable lack of longitudinal studies that assess the long-term sustainability benefits of these technologies. Most research focuses on short-term impacts, such as initial energy savings and reduced material usage. Future studies should focus on periodic data collection to capture the full potential of Industry 4.0 technologies over time, particularly in terms of resource efficiency, carbon emission reductions, and social sustainability metrics. This holistic approach will provide a more comprehensive understanding of the technologies' role in driving sustainable manufacturing practices, especially in emerging economies (Figure 3). Figure 2: Industry 4.0 technology hierarchy. In conclusion, while the literature has advanced considerably, significant gaps remain. Most studies analyze technologies in isolation, and there is a lack of empirical research on their combined impact. Addressing these research gaps will provide a clearer understanding of how Industry 4.0 technologies can contribute to sustainable development in a real-world context.