# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
## 4 Discussion  
### Discussion: The Importance of Empirical Research in Quantifying Industry 4.0's Impact  
This study emphasizes the need for more empirical research that can provide concrete evidence of the impact of Industry 4.0 technologies on sustainable development performance, particularly in emerging economies. Although some theoretical and qualitative studies exist, there is a critical shortage of data that capture the long-term effects of technologies such as MES, IIoT, and AM on metrics like carbon emissions, energy efficiency, and social sustainability. Emerging economies, which are often at the forefront of industrial growth, stand to benefit the most from such research.  
The FI-RST model used in this study is a step toward filling this gap by providing a quantitative framework for evaluating the sustainability performance of different technologies. However, further longitudinal studies are necessary to explore the scalability and adaptability of these technologies in diverse operational contexts. For instance, manufacturing firms in developing nations require empirical studies that evaluate how Industry 4.0 technologies can be integrated into local industries while considering factors such as limited infrastructure, regulatory environments, and resource constraints. The facial meeting of MES, IIoT, and AM as the main factors of sustainability not only adds weight to the theoretical predictions but, at the same time, reinforces them with the industrial implementation. Apart from that, the study outcome indicates that in case of the fully integrated utilization of these technologies, positive impacts may be observed along the profit generation, environmental protection, and social aspects of manufacturing, respectively, which will give a roadmap on how to achieve sustainable development in the whole sector (Table 7, Figures 13 and 14).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Impact Category**} & **Before** & **After** & \multirow{2}{*}{**Net Change (\%)**} & **Projected 5-Year** \\  & **Implementation** & & & **Impact** \\ \hline Production Cost (\$/unit) & 50 & 42 & \(-\)16\% & \(-\)25\% \\ \hline Energy Consumption (kWh/unit) & 12 & 9 & \(-\)25\% & \(-\)35\% \\ \hline Material Waste (\%) & 10 & 6 & \(-\)40\% & \(-\)50\% \\ \hline Carbon Footprint (tons CO\({}_{2}\)/unit) & 1.2 & 0.9 & \(-\)25\% & \(-\)33\% \\ \hline Revenue Growth (\%) & 5 & 12 & 140\% & 200\% \\ \hline Workforce Efficiency (\%) & 70 & 85 & 21.40\% & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Economic and environmental impact metrics before and after Industry 4.0 implementation.  
Description: this study demonstrates the impact of the integration of different Industry 4.0 technologies on the reduction in carbon emissions over a three-year time span. The X-axis of the line graph represents the years, the Y-axis carries the percentage of decline in the level of carbon emissions, and the various lines demonstrate the trends of the various technologies (e.g., MES, IIoT, AM, etc.) that have the potential to make this happen.