Management Information System Based on IoT and Big Data Technology for Optimization of Supply Chain

Muqorobin Muqorobin; Farid Fitriyadi

doi:10.29040/ijcis.v6i4.261

Management Information System Based on IoT and Big Data Technology for Optimization of Supply Chain

Muqorobin Muqorobin, Farid Fitriyadi

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.

Full Text:

PDF

References

Aryal, A., et al. "The emerging big data analytics and IoT in supply chain." International Journal of Logistics Research and Applications, vol. 25, no. 2, 2020, pp. 141-162.

Nozari, H., Fallah, M., Kazemipoor, H., Najafi, S.E. "Big data analysis of IoT-based supply chain management considering FMCG industries." Business Informatics, vol. 15, no. 1, 2021, pp. 78-96.

Zhang, Q., Ullah, A., Ashraf, S., Abdullah, M. "Synergistic Impact of Internet of Things and Big-Data-Driven Supply Chain on Sustainable Firm Performance." Sustainability, vol. 16, no. 13, 2024, Article 5717.

Usanto, S. "Integrasi IoT dan Big Data untuk Optimasi Logistik dan Rantai Pasokan." Jurnal RIS, vol. ..., 2024.

Zhou, L. "The Convergence of IoT and Big Data in International Logistics: Enhancing Supply Chain Efficiency." Law & Economy, vol. 2, no. 9, Sept. 2023.

V. Pande, C. Marlecha, and S. Kayte, “A review-fog computing and its role in the internet of things,” International Journal of Engineering Research and Applications, vol. 6, no. 10, pp. 2248–96227, 2016.

A. V. Dastjerdi and R. Buyya, “Fog computing: helping the internet of things realize its potential,” Computer, vol. 49, no. 8, pp. 112–116, 2016.

H. Hamidi and M. Jahanshahifard, “-e role of the internet of things in the improvement and expansion of business,” Journal of Organizational and End User Computing, vol. 30, no. 3, pp. 24–44, 2018. C. Chang, S. N. Srirama, and R. Buyya, “Mobile cloud business process management system for the internet of things: a survey,” ACM Computing Surveys, vol. 49, no. 4, pp. 1–42, 2016.

G. Diega, “Clouds of things. data protection and consumer law at the intersection of cloud computing and the internet of things in the United Kingdom,” Journal of Law & Economic Regulation, vol. 9, no. 1, pp. 69–93, 2016.

B. H. Dobkin, “A rehabilitation-internet-of-things in the home to augment motor skills and exercise training,” Neurorehabilitation and Neural Repair, vol. 31, no. 3, pp. 217–227, 2017.

H. Sun, X. Wang, R. Buyya, and J. Su, “CloudEyes: cloudbased malware detection with reversible sketch for resourceconstrained internet of things (IoT) devices,” Software: Practice and Experience, vol. 47, no. 3, pp. 421–441, 2017.

R. Visvanathan, D. C. Ranasinghe, A. Wilson et al., “Effectiveness of an ambient intelligent geriatric management system (AmbIGeM) to prevent falls in older people in hospitals: protocol for the AmbIGeM stepped wedge pragmatic trial,” Injury Prevention Journal of the International Society for Child & Adolescent Injury Prevention, vol. 25, no. 3, pp. 157–165, 2017.

M. Zaharia, R. S. Xin, P. Wendell et al., “Apache spark: a unified engine for big data processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.

Z. Obermeyer and E. J. Emanuel, “Predicting the future-big data, machine learning, and clinical medicine,” New England Journal of Medicine, vol. 375, no. 13, pp. 1216–1219, 2016.

D. Specht, “-e data revolution. big data, open data, data infrastructures and their consequences,” Media Culture & Society, vol. 37, no. 7, pp. 1110-1111, 2016.

J. Qin, H. Li, H. Li, X. Chen, and W. Feng, “Explore and practice of China’s intelligent “new engineering”-based on the grounded theory,” International Journal of Information and Education Technology, vol. 10, no. 8, pp. 632–640, 2020.

C. Kolias, A. Stavrou, J. Voas, I. Bojanova, and R. Kuhn, “Learning internet-of-things security “hands-on”,” IEEE Security & Privacy, vol. 14, no. 1, pp. 37–46, 2016.

J. Chen, Z. Lv, and H. Song, “Design of personnel big data management system based on blockchain,” Future Generation Computer Systems, vol. 101, pp. 1122–1129, 2019.

R. Parada, J. Melia-Segu ` ´ı, and R. Pous, “Anomaly detection using RFID-based information management in an IOT context,” Journal of Organizational and End User Computing, vol. 30, no. 3, pp. 1–23, 2018.

DOI: https://doi.org/10.29040/ijcis.v6i4.261