Internet of Things (IoT) for Smart Inventory Management
Keywords:
IoT, Cloud Server, Android application, ESP 32, Smart Inventory, Industrial AutomationAbstract
A smart inventory system is a computational time efficient system that helps businesses to manage and track their inventory levels, orders, and deliveries. It facilitates companies to have real-time visibility into their inventory and helps them make more informed and quick decisions about restock and how much to order. One key feature of a smart inventory system is its ability to automatically reorder items when they reach a certain threshold, eliminating the need for manual intervention. This helps to ensure that businesses always have the right amount of inventory on hand, reducing the risk of running out of stock or having excess inventory that takes up valuable storage space. This paper includes deployment of this system in real world which benefits to handle smart inventory system with improved accuracy 30% and efficiency in inventory tracking around 10%, reduced lead times for ordering and restocking, and the ability to track inventory across multiple locations. This paper briefly elaborates the implementation of smart inventory system that greatly improves a business's inventory management process, leading to increased profitability by more than 50%, average foot fall increased to 25% and reducing the waiting time of customer by nearly 75% making customer more satisfied.
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References
Singh, K. (2019). Smart warehouse management using electronic sensor-based computational intelligence. SCI, 823.
Vamsi, A. M. (2019, October). IoT based autonomous warehouse management for warehouses. EAISICC, 371–376.
Tejesh, B. S. S. (2018). Warehouse management system using IoT and open source framework. Alexandria Engineering Journal, 57(4), 3817–3823.
Chen, M.-C., Cheng, Y.-T., & Siang, C.-Y. (2022). Development of inventory management system based on radio frequency identification technology. Sensors and Materials, 34(3), 1163–1177.
Olanrewaju, R. F. (2021). Cloud-based warehouse system for effective management of under and over-stock hazards. ICCCE, 274–278.
Korkmaz, I. (2015). A cloud-based and Android-supported scalable home automation system. compeleceng, 43, 112–128.
Liang, C.-C. (2013). Smart inventory management system of food-processing-and-distribution industry. In Procedia Computer Science, 17, 373–378. Elsevier.
Paul, S. (2019). Study of smart inventory management system based on the Internet of Things. International Journal on Recent Trends in Business and Tourism, 3(3).
Bose, R., Mondal, H., Sarkar, I., & Roy, S. (2022). Design of smart inventory management system for construction sector based on IoT and cloud computing. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 2.
Gawande, S. P., & Tambe, S. B. (2019, June). Inventory management system for warehouse. International Research Journal of Engineering and Technology (IRJET), 6(6), 3454–3458.
Tan, W. C., & Sidhu, M. S. (2022). Review of RFID and IoT integration in supply chain management. Operations Research Perspectives, 9, 1342–1348.
Zope, A. S., Jambhale, M. S., Korde, N. M., Khan, H. A., & Bhargav, A. (2017). IoT based industrial automation. International Journal of Engineering Research and Technology, 5(1), 821–827.
Mane, P. B., Pethakar, M. S., & Pawar, R. R. (2017). Watermarking and cryptography-based image authentication on reconfigurable platform. Bulletin of Electrical Engineering and Informatics, 6(2), 181–187.
Mandwale, A. J., Purohit, H. G., & Gawande, M. B. (2015, January). Different approaches for implementation of Viterbi decoder on reconfigurable platform. In 2015 International Conference on Pervasive Computing (ICPC) (pp. 1–4). IEEE.
Mane, P. B., & Mulani, A. O. (2018). High-speed area-efficient FPGA implementation of AES algorithm. International Journal of Reconfigurable and Embedded Systems, 7(3), 157–165.
Kashid, M. M., Shaikh, S. A., Kadam, M. S., & Mujawar, A. N. (2022). IoT-based environmental parameter monitoring using machine learning approach. In Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021 (Vol. 1, pp. 43–51). Springer Nature Singapore.
Yadav, D., & Gangwar, S. (2023). Generic home automation system using IoT gateway based on WiFi and ant+ sensor network. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 153–165. https://doi.org/10.17762/ijritcc.v11i3.6332
Rossi, G., Nowak, K., Nielsen, M., García, A., & Silva, J. (n.d.). Enhancing collaborative learning in engineering education with machine learning. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/119
Kumar, A., Dhabliya, D., Agarwal, P., Aneja, N., Dadheech, P., Jamal, S. S., & Antwi, O. A. (2022). Cyber-internet security framework to conquer energy-related attacks on the internet of things with machine learning techniques. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/8803586
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