Role of RFID in Machinal Process of Manufacturing: A Critical Review of Contemporary Literature

Harikumar  Pallathadka; Laxmi Kirana Pallathadka; Shoraisam Kiran  Singh

doi:10.55544/ijrah.2.6.35

Authors

Harikumar Pallathadka Manipur International University, Imphal, Manipur, INDIA.
Laxmi Kirana Pallathadka Manipur International University, Imphal, Manipur, INDIA.
Shoraisam Kiran Singh Manipur International University, Imphal, Manipur, INDIA.

DOI:

https://doi.org/10.55544/ijrah.2.6.35

Keywords:

RFID technology, Supply Chain Management, smart technologies, manufacturing companies, consumer application

Abstract

RFID (radio frequency identification) is a modern supply chain management technology that is increasingly being used. RFID technology's potential to identify, detect, and monitor data across the supply chain significantly influences logistics and supply chain operations. The system may provide suppliers, manufacturers, distributors, and retailers with accurate, real-time inventory information. This exact stock data would result in lower labor costs, automated company practices, and improved supply chain efficiency. If executed correctly, it may minimize the ordering wait duration and Inventory management costs, improve inventory data quality, help avoid stockouts, and enhance the frequency of stock movements. RFID technology has prompted much debate and supposition over its possible repercussions. RFID is a new technical breakthrough that enables supply chain partners to cooperate closely by providing real-time informational transparency. Mean and T-test is applied in the study to find the result of the study with 193 respondents.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Zeba, G., Čičak, M., & Dabić, M. (2018). The role of RFID technology in the Intelligent Manufacturing. Industry 4.0, 3(6), 326-329.

Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarriá, D., & Menesatti, P. (2013). A review on agri-food supply chain traceability by means of RFID technology. Food and bioprocess technology, 6(2), 353-366.

Liu, M., Ma, J., Lin, L., Ge, M., Wang, Q., & Liu, C. (2017). Intelligent assembly system for mechanical products and key technology based on internet of things. Journal of Intelligent Manufacturing, 28(2), 271-299.

Tzeng, S. F., Chen, W. H., & Pai, F. Y. (2008). Evaluating the business value of RFID: Evidence from five case studies. International journal of production economics, 112(2), 601-613.

Rafique, M. Z., Ab Rahman, M. N., Saibani, N., Arsad, N., & Saadat, W. (2016). RFID impacts on barriers affecting lean manufacturing. Industrial Management & Data Systems.

Raut, R. D., Gotmare, A., Narkhede, B. E., Govindarajan, U. H., & Bokade, S. U. (2020). Enabling technologies for Industry 4.0 manufacturing and supply chain: concepts, current status, and adoption challenges. IEEE Engineering Management Review, 48(2), 83-102.

Chaudhuri, A., Dukovska-Popovska, I., Subramanian, N., Chan, H. K., & Bai, R. (2018). Decision-making in cold chain logistics using data analytics: a literature review. The International Journal of Logistics Management.

Chen, H., Chen, Y., & Yang, L. (2020). Intelligent early structural health prognosis with nonlinear system identification for RFID signal analysis. Computer Communications, 157, 150-161.

Oner, M., Ustundag, A., & Budak, A. (2017). An RFID-based tracking system for denim production processes. The International Journal of Advanced Manufacturing Technology, 90(1), 591-604.

Caizzone, S., & DiGiampaolo, E. (2015). Wireless passive RFID crack width sensor for structural health monitoring. IEEE Sensors Journal, 15(12), 6767-6774.

Leng, J., & Jiang, P. (2019). Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information. Journal of Intelligent Manufacturing, 30(3), 979-994.

Huang, G. Q., Zhang, Y. F., & Jiang, P. Y. (2008). RFID-based wireless manufacturing for real-time management of job shop WIP inventories. The International Journal of Advanced Manufacturing Technology, 36(7), 752–764.

Huang, G. Q., Zhang, Y. F., & Jiang, P. Y. (2007). RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts. Robotics and Computer-Integrated Manufacturing, 23(4), 469–477.

Lu, W., Huang, G. Q., & Li, H. (2011). Scenarios for applying RFID technology in construction project management. Automation in construction, 20(2), 101-106.

Rafique, M. Z., Ab Rahman, M. N., Saibani, N., Arsad, N., & Saadat, W. (2016). RFID impacts on barriers affecting lean manufacturing. Industrial Management & Data Systems.

Adams, G. (2007). Pharmaceutical manufacturing: RFID–reducing errors and effort. Filtration & Separation, 44(6), 17–19.

Abdirad, M., & Krishnan, K. (2021). Industry 4.0 in logistics and supply chain management: a systematic literature review. Engineering Management Journal, 33(3), 187–201.

Ding, K., & Jiang, P. (2018). RFID-based Production Data Analysis in an IoT-enabled Smart Job-shop, Journal of Automatica Sinica, 5(1), 128-138.

Khayyam, S., Ramish, A., Ur Rehman, K., & Syed, A.R. (2022). Strategising Radio Frequency Identification (RFID) in the Retail Supply Chains of Pakistan: A Multiple Case Study, Operations and Supply Chain Management, 15(1), 27–40.

Pallathadka, A., Sauer, J., Chang, H., & Grimm, N. B. (2022). Urban flood risk and green infrastructure: Who is exposed to risk and who benefits from investment? A case study of three US Cities. Landscape and Urban Planning, 223, 104417.

Pallathadka, A. K., Chang, H., & Ajibade, I. (2021). The spatial patterns of Pluvial flood risk, blue-green infrastructure, and social vulnerability: A case study from two Alaskan Cities. International Journal of Geospatial and Environmental Research, 8(3), 2.

Pallathadka, A. K., Chang, H., & Han, D. (2022). What explains spatial variations of COVID-19 vaccine hesitancy?: A social-ecological-technological systems approach. Environmental Research: Health.

Pallathadka, A., Pallathadka, L., Rao, S., Chang, H., & Van Dommelen, D. (2021). Using GIS-based spatial analysis to determine urban greenspace accessibility for different racial groups in the backdrop of COVID-19: a case study of four US cities. GeoJournal, 1-21.

Sriram, V. P., Raj, K. B., Srinivas, K., Pallathadka, H., Sajja, G. S., & Gulati, K. (2021). An Extensive Systematic Review of RFID Technology Role in Supply Chain Management. 2021 IEEE 6th International Conference on Signal Processing, Computing and Control (ISPCC), 789–794. https://doi.org/10.1109/ISPCC53510.2021.9609414

Pallathadka, H., Mustafa, M., Sanchez, D. T., Sekhar Sajja, G., Gour, S., & Naved, M. (2021). Impact of Machine Learning on Management, Healthcare and Agriculture. Materials Today: Proceedings, xxxx. https://doi.org/10.1016/j.matpr.2021.07.042

Shanthi, D., Kuncha, P., Dhar, M. S. M. S. M., Jamshed, A., Pallathadka, H., & Anusha Linda Kostka, J. E. E. (2021). The Blue Brain Technology using Machine Learning. Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021; IEEE Xplore, 1370–1375. https://doi.org/10.1109/ICCES51350.2021.9489075

Sajja, G. S., Pallathadka, H., Phasinam, K., & Arcinas, M. M. (2022). Machine Learning Techniques in Business Forecasting: A Performance Evaluation. ECS Transactions, 107(1), 11431–11437. https://doi.org/10.1149/10701.11431ecst

Pallathadka, H., Naved, M., Phasinam, K., & M. Arcinas, M. (2022). A Machine Learning Based Framework for Heart Disease Detection. ECS Transactions, 107(1), 8667–8673. https://doi.org/10.1149/10701.8667ecst

Beram, S. M., Pallathadka, H., Patra, I., & Prabhu, P. (2022). A Machine Learning Based Framework for Preprocessing and Classification of Medical Images. ECS Transactions, 107(1), 7589–7596. https://doi.org/10.1149/10701.7589ecst

Kumar, V., Pallathadka, H., Sharma, S. K., Thakar, C. M., Singh, M., & Pallathadka, L. K. (2021). Role of machine learning in green supply chain management and operations management. Materials Today: Proceedings, xxxx. https://doi.org/10.1016/j.matpr.2021.11.625

Zamani, A. S., Anand, L., Rane, K. P., Prabhu, P., Mateen Buttar, A., Pallathadka, H., Raghuvanshi, A., & Dugbakie, B. N. (2022). Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection. Journal of Food Quality, 2022(1598796). https://doi.org/10.1155/2022/1598796

Paricherla, M., Babu, S., Phasinam, K., Pallathadka, H., Zamani, A. S., Narayan, V., Shukla, S. K., & Mohammed, H. S. (2022). Towards Development of Machine Learning Framework for Enhancing Security in Internet of Things. Security and Communication Networks, 2022(477507), 1–5. https://doi.org/10.1155/2022/4477507

Pallathadka, H., Jawarneh, M., Sammy, F., Garchar, V., Sanchez, T., & Naved, M. (2022). A Review of Using Artificial Intelligence and Machine Learning in Food and Agriculture Industry. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2215–2218. https://doi.org/10.1109/ICACITE53722.2022.9823427

Pallathadka, H., Wenda, A., Ramirez-Asís, E., Asís-López, M., Flores-Albornoz, J., & Phasinam, K. (2021). Classification and prediction of student performance data using various machine learning algorithms. Materials Today: Proceedings, xxxx. https://doi.org/10.1016/j.matpr.2021.07.382

Loganathan, B., Patra, I., Garchar, V., Pallathadka, H., Naved, M., & Gour, S. (2022). Development of Machine Learning Based Framework for Classification and Prediction of Students in Virtual Classroom Environment. 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), 1–5. https://doi.org/10.1109/ICACTA54488.2022.9752918

Kapula, P. R., Singh, A., Khan, S. P., Kumar, S., Kotni, V. V. D. P., & Pallathadka, H. (2023). Machine Learning for the Manufacturing of Digital Marketing Techniques and Its Impact on Health Care System. In S. , H. A. , A. P. K. , K. H. Yadav (Ed.), Smart Innovation, Systems and Technologies (Vol. 290, pp. 387–397). Springer. https://doi.org/10.1007/978-981-19-0108-9_41

Raghuvanshi, A., Singh, U. K., Sajja, G. S., Pallathadka, H., Asenso, E., Kamal, M., Singh, A., & Phasinam, K. (2022). Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming. Journal of Food Quality, 2022, 1–8. https://doi.org/10.1155/2022/3955514

Nancy, P., Pallathadka, H., Naved, M., Kaliyaperumal, K., Arumugam, K., & Garchar, V. (2022). Deep Learning and Machine Learning Based Efficient Framework for Image Based Plant Disease Classification and Detection. 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), 1–6. https://doi.org/10.1109/ICACTA54488.2022.9753623

Lakineni, P. K., Nayak, K. M., Pallathadka, H., Gulati, K., Pandey, K., & Patel, P. J. (2022). Fraud Detection in Credit Card Data using Unsupervised & Supervised Machine Learning-Based Algorithms. 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–4. https://doi.org/10.1109/ICSES55317.2022.9914287

Jasti, V. D. P., Zamani, A. S., Arumugam, K., Naved, M., Pallathadka, H., Sammy, F., Raghuvanshi, A., & Kaliyaperumal, K. (2022). Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis. Security and Communication Networks, 1–7. https://doi.org/10.1155/2022/1918379

Pallathadka, H., Sajjar, G. S., Phasinam, K., Ritonga, M., Naved, M., Bansal, R., & Quinonez-Chodquecota, J. (2021). An investigation of various applications and related challenges in cloud computing. Materials Today: Proceedings, xxxx. https://doi.org/10.1016/j.matpr.2021.11.383

Sajjaa, G. S., Pallathadka, H., Naved, M., & Phasinam, K. (2022). Various Soft Computing Based Techniques for Developing Intrusion Detection Management System. ECS Transactions, 107(1), 3335–3341. https://doi.org/10.1149/10701.3335ecst

Alam, J., P, H. S., Mirza, A., Swadia, B. U., & Pallathadka, H. (2022). Making Internet of Things Robust Through Blockchain Technology: A Quantitative Approachin the World of Technology Advancements. 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–7. https://doi.org/10.1109/ICSES55317.2022.9914014

Pallathadka, H., Poddar, A., Soto, R. M. H. R. M. H., Cavaliere, L. P. L. L. P. L., More, A. B. A. B., & Regin, R. (2021). Production Planning and Scheduling of Mediating Effect of Electronic Applications. 2021 7th International Conference on Advanced Computing & Communication Systems (ICACCS) Production; IEEE Xplore, 1994–1998. https://doi.org/10.1109/ICACCS51430.2021.9441711