Publisher

IJCISIJCIS

Journal International Journal of Computer and Information System (IJCIS)International Journal of Computer and Information System (IJCIS) Abstract

This paper explores the integration of Internet of Things (IoT) and Big Data Analytics for optimizing supply chain management. With increasing complexity in global supply chains, traditional information systems fall short in providing real-time visibility, accurate forecasting, and agile decision-making. IoT technology facilitates real-time data collection through sensors embedded in goods, vehicles, and production systems, while Big Data analytics processes these high-volume, real-time data streams to generate actionable insights. By integrating these technologies into a Management Information System (MIS), this paper proposes a framework that enhances supply chain visibility, forecasting accuracy, and responsiveness. The study examines how IoT-enabled sensors and big data analytics improve logistics, inventory management, and risk mitigation. Key challenges, including data security, interoperability, and infrastructure costs, are also addressed. The proposed MIS architecture offers a foundation for building smart, adaptive, and resilient supply chains, transforming decision-making from reactive to proactive. The findings suggest significant improvements in operational efficiency and supply chain agility. This paper concludes with implications for practitioners and calls for further empirical research to validate the proposed system in real-world settings.

Conclusion

The study demonstrates that integrating IoT and Big Data analytics significantly enhances supply chain performance.Specifically, the proposed MIS architecture leads to improved delivery lead times, enhanced demand forecasting, and better inventory management.These improvements contribute to increased operational efficiency and a more responsive supply chain, ultimately benefiting businesses by reducing costs and improving customer satisfaction.

Future Research

Future research should focus on validating the proposed MIS in real-world supply chain settings, particularly within developing economies, to assess its adaptability and effectiveness across diverse operational contexts. Furthermore, exploring the integration of advanced analytics techniques, such as artificial intelligence and machine learning, with IoT and Big Data could unlock even greater potential for predictive maintenance, anomaly detection, and proactive risk management within supply chains. Finally, investigating the synergistic benefits of combining IoT and Big Data with blockchain technology could enhance data security, transparency, and traceability, fostering greater trust and collaboration among supply chain partners, ultimately leading to more resilient and efficient supply chain networks.

Metadata

File size	299.24 KB
Pages	8
DMCA	Report