# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
## 3 Results
### Performance Metrics  
The performance metrics used to assess the sustainability of the manufacturing enterprises included economic efficiency, environmental impact, and social outcomes. The application of the FI-RST model provided quantitative insights into how each technology influenced these metrics (Table 6, Figure 12).  
Figure 11: Relationship diagram of influencing factors in manufacturing sustainable development.  
Figure 10: Correlation between Industry 4.0 technology integration and sustainability outcomes.  
This portrayal of Industry 4.0 technology by giving the sustainability rating of companies (A, B, C, D, E) through altered approaches is different. Stacked bar types display the scores of each applicant, with the bars expressing three steps that include economic, social, and environmental candidates. Such graphs simply show these dimensions and quantify the impact on the overall sustainability ratings of the analyzed companies. Economic efficiency: technologies such as MES and IIoT have contributed enormously to saving costs by reducing energy consumption and optimizing production schedules, thereby increasing operational efficiency by about 15-20%.  
Environmental impact: the application of AM and IIoT technologies was instrumental in reducing waste materials by between 25 and 30% and energy consumption by around 20%. Moreover, they were considered the main culprits for the reduction in greenhouse gas emissions, leading us to a more environmentally sustainable way of manufacturing [30].  
Social outcomes: by using AR and MR technologies in companies, there was up to a 40% decrease in workplace-related accidents, added to the other collateral benefits. Indeed, ICT improved work-related communication and coordination, which explains the higher social equity in the currently technologically savvy workforce. These findings underscore the array of advanced possibilities of 4.0 technologies in their way to building sustainable manufacturing. The FI-RST model, which is the data-driven information, is  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Enterprise** & \begin{tabular}{c} **MES** \\ **Adoption** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **IIoT** \\ **Adoption** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **AM** \\ **Adoption** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **Revenue** \\ **Growth (\%)** \\ \end{tabular} & \begin{tabular}{c} **Waste** \\ **Reduction** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Energy** \\ **Savings (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Sustainability** \\ **Score** \\ \end{tabular} \\ \hline Enterprise A & High & Medium & Low & 10\% & 25\% & 20\% & 87 \\ \hline Enterprise B & Medium & High & Medium & 12\% & 30\% & 25\% & 85 \\ \hline Enterprise C & Low & Medium & High & 8\% & 35\% & 15\% & 80 \\ \hline Enterprise D & Medium & Low & Medium & 6\% & 20\% & 18\% & 75 \\ \hline Enterprise E & High & High & High & 15\% & 40\% & 30\% & 90 \\ \hline \hline \end{tabular} Note: Adoption levels are categorized as low, medium, or high based on the extent of technology integration.  
\end{table}
Table 6: Comparative analysis of sustainability metrics across different manufacturing enterprises.  
Figure 12: Comparative analysis of sustainability metrics across different enterprises.  
very essential in terms of decision making. Managers of production firms and authoritative bodies can know the effect of technology use on the productivity of their firm to meet sustainability goals.