1
Docente de la carrera de Administración de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
(ua.marceloyancha@uniandes.edu.ec)
2
Docente de la carrera de Administración de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
(ui.oswaldotorres@uniandes.edu.ec)
3
Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador
(us.diegopalma@uniandes.edu.ec)
4
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(sktowfek@jcsis.org)
Abstract :
This study delves into optimizing sustainable inventory management practices through the integration of advanced data analytics methodologies. In response to the complex dynamics of modern supply chains, where inventory control significantly impacts sustainability goals, this research aims to address the intricate interplay between decentralized decision-making, government policies, and strategic choices within supply chain networks. Employing models such as Game Theory and Gated Recurrent Unit (GRU), alongside statistical analyses, our investigation explores the transformative potential of informed decision-making frameworks. Through a comprehensive evaluation of inventory data, including statistical analyses, visual representations, and model evaluations, we illuminate the nuanced relationships and dependencies prevalent within inventory control strategies. Our findings underscore the significance of data-driven decision-making in optimizing inventory practices, mitigating risks, and fostering sustainability within supply chains. The insights gleaned from this study advocate for the continued application of advanced data analytics to pave the way for resilient, environmentally conscious, and economically viable supply chain practices.
Keywords :
Sustainability; supply chain management; inventory optimization; inventory control systems;Sustainable logistics solutions; data analytics.
References :
[1] Wang, Gang, Angappa Gunasekaran, Eric W T Ngai, and Thanos Papadopoulos. 2016. “Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications.” International Journal of Production Economics 176: 98–110.
[2] Zhou, Kaile, Chao Fu, and Shanlin Yang. 2016. “Big Data Driven Smart Energy Management: From Big Data to Big Insights.” Renewable and Sustainable Energy Reviews 56: 215–25.
[3] Choi, Tsan-Ming, Stein W Wallace, and Yulan Wang. 2018. “Big Data Analytics in Operations Management.”
Production and Operations Management 27 (10): 1868–83.
[4] Tiwari, Sunil, Hui-Ming Wee, and Yosef Daryanto. 2018. “Big Data Analytics in Supply Chain Management
between 2010 and 2016: Insights to Industries.” Computers \& Industrial Engineering 115: 319–30.
[5] Ren, Shan, Yingfeng Zhang, Yang Liu, Tomohiko Sakao, Donald Huisingh, and Cecilia M V B Almeida. 2019. “A Comprehensive Review of Big Data Analytics throughout Product Lifecycle to Support Sustainable Smart Manufacturing: A Framework, Challenges and Future Research Directions.” Journal of Cleaner Production 210: 1343–65.
[6] Nguyen, Truong, ZHOU Li, Virginia Spiegler, Petros Ieromonachou, and Yong Lin. 2018. “Big Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review.” Computers \& Operations Research 98: 254– 64.
[7] Zhang, Yingfeng, Shan Ren, Yang Liu, and Shubin Si. 2017. “A Big Data Analytics Architecture for Cleaner Manufacturing and Maintenance Processes of Complex Products.” Journal of Cleaner Production 142: 626–41.
[8] Bibri, Simon Elias. 2018. “The IoT for Smart Sustainable Cities of the Future: An Analytical Framework for Sensor-Based Big Data Applications for Environmental Sustainability.” Sustainable Cities and Society 38: 230– 53.
[9] Zhong, Ray Y, Chen Xu, Chao Chen, and George Q Huang. 2017. “Big Data Analytics for Physical Internet- Based Intelligent Manufacturing Shop Floors.” International Journal of Production Research 55 (9): 2610–21.
[10] Civelek, Ismail. 2017. “Sustainability in Inventory Management.” In Intelligence, Sustainability, and Strategic Issues in Management, 43–56. Routledge.
[11] You, Fengqi, Ling Tao, Diane J Graziano, and Seth W Snyder. 2012. “Optimal Design of Sustainable Cellulosic Biofuel Supply Chains: Multiobjective Optimization Coupled with Life Cycle Assessment and Input--Output Analysis.” AIChE Journal 58 (4): 1157–80.
[12] Ban, Gah-Yi, and Cynthia Rudin. 2019. “The Big Data Newsvendor: Practical Insights from Machine Learning.” Operations Research 67 (1): 90–108.
[13] Babiceanu, Radu F, and Remzi Seker. 2016. “Big Data and Virtualization for Manufacturing Cyber-Physical Systems: A Survey of the Current Status and Future Outlook.” Computers in Industry 81: 128–37.
[14] Li, Jingran, Fei Tao, Ying Cheng, and Liangjin Zhao. 2015. “Big Data in Product Lifecycle Management.” The International Journal of Advanced Manufacturing Technology 81: 667–84.
[15] Zhang, Hao, Qiang Liu, Xin Chen, Ding Zhang, and Jiewu Leng. 2017. “A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line.” Ieee Access 5: 26901–11.
[16] Tao, Fei, Jiangfeng Cheng, Qinglin Qi, Meng Zhang, He Zhang, and Fangyuan Sui. 2018. “Digital Twin-Driven Product Design, Manufacturing and Service with Big Data.” The International Journal of Advanced Manufacturing Technology 94: 3563–76.
[17] Ahmed, Ejaz, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Imran Khan, Abdelmuttlib Ibrahim Abdalla Ahmed, Muhammad Imran, and Athanasios V Vasilakos. 2017. “The Role of Big Data Analytics in Internet of Things.” Computer Networks 129: 459–71.
[18] Zhong, Ray Y, George Q Huang, Shulin Lan, Q Y Dai, Xu Chen, and T Zhang. 2015. “A Big Data Approach for Logistics Trajectory Discovery from RFID-Enabled Production Data.” International Journal of Production Economics 165: 260–72.
[19] Arunachalam, Deepak, Niraj Kumar, and John Paul Kawalek. 2018. “Understanding Big Data Analytics Capabilities in Supply Chain Management: Unravelling the Issues, Challenges and Implications for Practice.” Transportation Research Part E: Logistics and Transportation Review 114: 416–36.
[20] Hazen, Benjamin T, Christopher A Boone, Jeremy D Ezell, and L Allison Jones-Farmer. 2014. “Data Quality for Data Science, Predictive Analytics, and Big Data in Supply Chain Management: An Introduction to the Problem and Suggestions for Research and Applications.” International Journal of Production Economics 154: 72–80.
[21] Tao, Fei, Qinglin Qi, Ang Liu, and Andrew Kusiak. 2018. “Data-Driven Smart Manufacturing.” Journal of Manufacturing Systems 48: 157–69.
[22] Kambatla, Karthik, Giorgos Kollias, Vipin Kumar, and Ananth Grama. 2014. “Trends in Big Data Analytics.” Journal of Parallel and Distributed Computing 74 (7): 2561–73.
[23] Witkowski, Krzysztof. 2017. “Internet of Things, Big Data, Industry 4.0--Innovative Solutions in Logistics and Supply Chains Management.” Procedia Engineering 182: 763–69.
[24] Dubey, Rameshwar, Angappa Gunasekaran, Stephen J Childe, Constantin Blome, and Thanos Papadopoulos. 2019. “Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture.” British Journal of Management 30 (2): 341–61.
| Style | # |
|---|---|
| MLA | Marcelo Y. Villacis, Oswaldo T. Merlo, Diego P. Rivero, S. K. Towfek. "Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach." Journal of Intelligent Systems and Internet of Things, Vol. 11, No. 1, 2024 ,PP. 55-64 (Doi : https://doi.org/10.54216/JISIoT.110106) |
| APA | Marcelo Y. Villacis, Oswaldo T. Merlo, Diego P. Rivero, S. K. Towfek. (2024). Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach. Journal of Journal of Intelligent Systems and Internet of Things, 11 ( 1 ), 55-64 (Doi : https://doi.org/10.54216/JISIoT.110106) |
| Chicago | Marcelo Y. Villacis, Oswaldo T. Merlo, Diego P. Rivero, S. K. Towfek. "Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach." Journal of Journal of Intelligent Systems and Internet of Things, 11 no. 1 (2024): 55-64 (Doi : https://doi.org/10.54216/JISIoT.110106) |
| Harvard | Marcelo Y. Villacis, Oswaldo T. Merlo, Diego P. Rivero, S. K. Towfek. (2024). Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach. Journal of Journal of Intelligent Systems and Internet of Things, 11 ( 1 ), 55-64 (Doi : https://doi.org/10.54216/JISIoT.110106) |
| Vancouver | Marcelo Y. Villacis, Oswaldo T. Merlo, Diego P. Rivero, S. K. Towfek. Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach. Journal of Journal of Intelligent Systems and Internet of Things, (2024); 11 ( 1 ): 55-64 (Doi : https://doi.org/10.54216/JISIoT.110106) |
| IEEE | Marcelo Y. Villacis, Oswaldo T. Merlo, Diego P. Rivero, S. K. Towfek, Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 11 , No. 1 , (2024) : 55-64 (Doi : https://doi.org/10.54216/JISIoT.110106) |