Conjectures of Computer Vision Technology on Industrial Information Management Systems: A Futuristic Gaze

Authors

https://doi.org/10.48313/maa.v2i2.43

Abstract

Computer Vision Technology (CVT) has emerged as a promising tool for enhancing Industrial Information Management Systems (IMSs) by enabling machines to interpret and understand visual information. This study explores the potential impact of CVT on IMSs in the industrial sector, focusing on its conjectures and implications for the future. The rapid advancement of CVT has raised expectations for its potential applications in industrial IMSs. However, there is a lack of research on the specific conjectures of CVT in this context, and the implications of its integration on industrial processes and operations remain unclear. This study seeks to address this gap by examining the potential benefits and challenges of CVT in industrial IMSs, and by proposing strategies for its successful implementation. This study employs a qualitative research approach, drawing on existing literature and related studies to analyze the conjectures of CVT on industrial IMSs. The research methodology includes a review of relevant literature, interviews with industry experts, and analysis of real-world applications of CVT in industrial settings. The findings are synthesized to identify key trends and insights on the potential impact of CVT on industrial IMSs. The findings revealed that CVT has the potential to revolutionize industrial IMSs by enabling real-time monitoring, analysis, and decision-making based on visual data. The technology can enhance efficiency, accuracy, and safety in industrial processes, leading to improved productivity and cost savings. However, challenges such as data privacy, security, and integration with existing systems need to be addressed for successful implementation of CVT in industrial IMSs. With careful planning and strategic implementation, CVT can pave the way for a more efficient, intelligent, and sustainable future for industrial IMSs.

Keywords:

Computer vision, Inventory management, Industrial process, Machine learning

References

  1. [1] Ottakath, N., Al-Ali, A., Al-Maadeed, S., Elharrouss, O., & Mohamed, A. (2023). Enhanced computer vision applications with blockchain: A review of applications and opportunities. Journal of king saud university-computer and information sciences, 35(10), 101801. https://doi.org/10.1016/j.jksuci.2023.101801
  2. [2] Abosuliman, S. S., & Almagrabi, A. O. (2021). Computer vision assisted human computer interaction for logistics management using deep learning. Computers & electrical engineering, 96, 107555. https://doi.org/10.1016/j.compeleceng.2021.107555
  3. [3] Cao, K., Chen, Y., Gao, S., Zhang, H., & Dang, H. (2022). Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management. Applied sciences, 12(2), 755. https://doi.org/10.3390/app12020755
  4. [4] Mashayekhy, Y., Babaei, A., Yuan, X. M., & Xue, A. (2022). Impact of Internet of Things (IoT) on inventory management: A literature survey. Logistics, 6(2), 33. https://doi.org/10.3390/logistics6020033
  5. [5] Ikpe, A. E., Ohwoekevwo, J. U., & Ekanem, I. I. (2024). Overview of the role of medical robotics in day-to-day healthcare services: A paradigm shift in clinical operations. Ibom medical journal, 17(2), 192–203. http://ibommedicaljournal.org/index.php/imjhome/article/download/422/1070
  6. [6] Alberto, F. E. (2024). ROI analysis of RFID-based intelligent inventory management systems. Frontiers in management science, 3(3), 52–59. https://doi.org/10.56397/FMS.2024.06.07
  7. [7] El Jaouhari, A., Alhilali, Z., Arif, J., Fellaki, S., Amejwal, M., & Azzouz, K. (2022). Demand forecasting application with regression and iot based inventory management system: a case study of a semiconductor manufacturing company. International journal of engineering research in africa, 60, 189–210. https://doi.org/10.4028/p-8ntq24
  8. [8] Unhelkar, B., Joshi, S., Sharma, M., Prakash, S., Mani, A. K., & Prasad, M. (2022). Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0--A systematic literature review. International journal of information management data insights, 2(2), 100084. https://doi.org/10.1016/j.jjimei.2022.100084
  9. [9] Yin, H., Chen, C., Hao, C., & Huang, B. (2022). A Vision-based inventory method for stacked goods in stereoscopic warehouse. Neural computing and applications, 34(23), 20773–20790. https://doi.org/10.1007/s00521-022-07551-4
  10. [10] Tubis, A. A., & Rohman, J. (2023). Intelligent warehouse in industry 4.0—systematic literature review. Sensors, 23(8), 4105. https://doi.org/10.3390/s23084105
  11. [11] Damle, A., Bangera, M., Tripathi, S., & Meena, M. (2020). Analysis of barcode scanning and management. SAMRIDDHI: a journal of physical sciences, engineering and technology, 12(SUP 1), 90–95. file:///C:/Users/pajohesh3/Downloads/1911-Article%20Text-1218-1-10-20201228.pdf
  12. [12] Balaska, V., Tsiakas, K., Giakoumis, D., Kostavelis, I., Folinas, D., Gasteratos, A., & Tzovaras, D. (2022). A viewpoint on the challenges and solutions for driverless last-mile delivery. Machines, 10(11), 1059. https://doi.org/10.3390/machines10111059
  13. [13] Agarwal, V., & Ankolikar, S. (2022). Deployment of RFID sensors in supply chain management--a review. Journal of mechatronics and artificial intelligence in engineering, 3(2), 47–64. https://doi.org/10.21595/jmai.2022.22565
  14. [14] Mohammed, K., Elmogy, M., & Tolba, A. S. (2017). International journal of intelligent computing and information science. International journal of intelligent computing and information science, 17(1)33-49. https://www.researchgate.net
  15. [15] Sharma, P., & Panda, S. (2023). Cloud computing for supply chain management and warehouse automation: a case study of azure cloud. International journal of smart sensors and adhoc networks, 3(4), 19-29. https://www.researchgate.net/
  16. [16] Pasaribu, J. S. (2021). Development of a Web Based Inventory Information System. International journal of engineering, science and information technology, 1(2), 24–31. http://download.garuda.kemdikbud.go.id/article.php?article=1974150&val=21071&title=Development%20of%20a%20Web%20Based%20Inventory%20Information%20System
  17. [17] Raza, S. A. (2022). A systematic literature review of RFID in supply chain management. Journal of enterprise information management, 35(2), 617–649. https://doi.org/10.1108/JEIM-08-2020-0322
  18. [18] Soleimani, R., Soleimani-Babakamali, M. H., Meng, S., Avci, O., & Taciroglu, E. (2024). Computer vision tools for early post-disaster assessment: Enhancing generalizability. Engineering applications of artificial intelligence, 136, 108855. https://doi.org/10.1016/j.engappai.2024.108855
  19. [19] Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics, 8(3), 73. https://doi.org/10.3390/logistics8030073

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Published

2025-06-23

Issue

Vol. 2 No. 2 (2025): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Ekanem, I. I. ., Ohwoekevwo, J. U. ., & Ikpe, A. E. . (2025). Conjectures of Computer Vision Technology on Industrial Information Management Systems: A Futuristic Gaze. Metaheuristic Algorithms With Applications, 2(2), 178-190. https://doi.org/10.48313/maa.v2i2.43