Strategic Evaluation of Data Compression and Edge Computing Technologies for Enhancing Energy Sustainability and Smart Supply Chain Management

The rapid digital transformation of supply chains has led to a substantial increase in real-time data generated by interconnected sensors, devices, and analytics platforms. Although this growth enhances operational visibility and supports data-driven decision-making, it also introduces challenges related to energy consumption, bandwidth limitations, and rising communication overhead. As data volumes continue to expand, ensuring sustainable and efficient digital operations becomes increasingly difficult. In this context, data compression and edge computing serve as essential strategies for managing information directly at the source. By performing localized preprocessing and reducing unnecessary transmissions, these technologies help lower network load, decrease energy usage, and improve system responsiveness. This study provides a strategic evaluation of data compression and edge computing as complementary solutions for enhancing sustainability in smart supply chain environments. The proposed multilayer framework integrates adaptive compression with edge-level analytics to optimize communication, computational demand, and power utilization. Experimental results show consistent and moderate improvements of nearly ۱۵ percent in energy efficiency, transmission reduction, and latency. These findings confirm that combining edge intelligence with lightweight compression creates a practical and environmentally responsible foundation for scalable and resilient supply chain systems.

کلیدواژه ها:

Smart Supply Chain ، Digital Transformation ، Data Compression ، Edge Computing ، IoT ، Energy Sustainability