## 5 Discussion
* RPM sensors (RPM-1000) in spinning and weaving machines contributed to a 10% increase in machinery lifespan by optimizing rotation speeds, reducing wear and tear as the speed consumes energy, hence indirectly contributing to energy savings as well. * IP cameras resulted in a 25% decrease in theft incidents by enhancing surveillance, safeguarding assets, and minimizing extra costs. IP camera access was extended to managers via their mobile phones, enabling them to monitor the environment from anywhere at any time. * Speech recognition technology (SR-1000) integrated into control systems led to an improvement in communication efficiency, streamlining control processes. Overall, the integration of these sensors yielded significant improvements in productivity, quality control, safety, and resource management in Pakistan's textile industry. By employing real-time monitoring, predictive maintenance, and process optimization methodologies, our study aims to propagate sustainable manufacturing principles, diminish environmental footprints, and stimulate a trajectory toward comprehensive and sustainable industrialization. In setting up the monitoring framework for the textile industry, the authors established a table for standardized before values and measured after values for each parameter that translates directly to a reduction value. This decision was driven by the need for clarity, consistency, and ease of comparison to clearly indicate the improvement achieved. This approach facilitated benchmarking performance and effectively communicating the impact of the interventions to all stakeholders involved. A detailed breakdown of each of these parameters and the required change is mentioned in Table 3.  
In percentage change calculations, a negative value indicates a decrease or reduction in the parameter being measured, signifying improvement or efficiency gains. Conversely, a positive value denotes an increase, reflecting a rise or enhancement in the measured parameter. In the case of operational metrics like breakdowns, spoilage, corrosion, unauthorized access, energy consumption, water usage, and theft incidents, negative changes generally represent improvements or reductions, which are positive outcomes for the textile industry's efficiency, sustainability, and security. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** & **Parameter** & **Before Value** & **After Value** & **Percentage Change** \\ \hline Force Sensitive Resistor (FS-500) & Machinery Breakdowns & 200 & 160 & \(-\)20\% \\ Temperature Sensors (LM35) & Fabric Spoilage & 200 & 170 & \(-\)15\% \\ Humidity Sensors (DHT122) & Equipment Corrosion & 80 & 78 & \(-\)2.5\% \\ PIR Motion Sensors (HC-SR501) & Unauthorized Access Incidents & 200 & 140 & \(-\)30\% \\ Light Intensity Sensors (PV-200) & Energy Consumption & 300 & 219 & \(-\)27\% \\ GPS Modules (NEO-6M) & Delivery Efficiency & 160 & 184 & 15\% \\ Tank Water Level Sensors & Water Usage & 150 & 120 & \(-\)20\% \\ (UltrasonicSensor\_v3.1) & & & & \\ RPM Sensors (RPM-1000) & Machinery Lifespan & 120 & 132 & 10\% \\ IP Cameras (IP Camera) & Theft Incidents & 120 & 90 & \(-\)25\% \\ Speech Recognition Technology & Communication Efficiency & 80 & 96 & 20\% \\ (SR-1000) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The cumulative impact of sensors on operational parameters in the textile industry with percentage improvements across diverse performance metrics. The following table, Table 4, outlines how each application contributes distinctly to these overarching sustainability imperatives in the Pakistani textile industry and how it stands to experience transformative improvements across multiple fronts. By harnessing these technologies, manufacturers are poised to streamline operations, mitigate expenses, diminish waste streams, and deliver premium products in a severely competitive market landscape.