AI and Machine Learning Integration in Oracle Field Service
  IJCTT-book-cover
 		          
 
© 2024 by IJCTT Journal
Volume-72 Issue-10
Year of Publication : 2024
Authors : Rohini Isarapu, Sharathchandra Gowda
DOI :  10.14445/22312803/IJCTT-V72I10P103

How to Cite?

Rohini Isarapu, Sharathchandra Gowda, "AI and Machine Learning Integration in Oracle Field Service," International Journal of Computer Trends and Technology, vol. 72, no. 10, pp. 9-14, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I10P103

Abstract
This paper explains the exercise of AI and ML in OFSC and the acts of these electronics in the field to help management. This research investigates AI electronics in OFSC, stressing their use in predicting support competency and slating service. These technologies are complicated, affecting animate nerve organ networks, the study of computers, and liberal ML methods. To that effect, this paper shows in what way or manner AI-located field aid resolutions are changing manufacturing practices. This is through a harsh test of legitimate arrangement and enactment of the foundation, stressing Verizon and OFSC. The study indicates gains in overall accomplishment accompanying the arrangement, naming a surge in the output of technicians and a decrease in the number of hours gone along the way by nearly 25%. It still uses AI in some facets of field duty administration, to a degree resolutions on optimum routes and ideas and handling consumers within OFSC. The paper still looks at the issues emerging from the unification of AI, bestowing unprejudiced news on the current progress of AI in field duty administration. Another factor is that dossier solitude concedes that possibility takes plenty of work as it is an impressionable subject, including individual news. The abilities required to conduct this plan grant permission likewise be mechanics and specific. Then, merging machine intelligence and machine learning into Prophecy Field Aid Cloud considerably reinforces field aid administration.

Keywords
Artificial intelligence in field service, Dynamic scheduling, Machine learning, Oracle field service cloud, and predictive maintenance.

Reference

[1] IBM, Field Service Management, Ibm.com, 2021. [Online]. Available: www.ibm.com/topics/field-service-management#:~:text=Field%20service%20management%20typically%20involves
[2] Ipseeta Satpathy, Arpita Nayak, and V. Jain, “The Strategic Role of Artificial Intelligence (AI) in Service Delivery Systems,” AI Innovations in Service and Tourism Marketing, pp. 291-310, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Ajaya K. Swain, and Valeria R. Garza, “Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident Resolution,” Information Systems Frontiers, vol. 25, pp. 819-834, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Ruonan Sun, Shirley Gregor, and Erwin Fielt, “Generativity and the Paradox of Stability and Flexibility in a Platform Architecture: A Case of the Oracle Cloud Platform,” Information and Management, vol. 58, no. 8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Harness AI and Application Innovation for Success, Oracle, 2024. [Online]. Available: https://www.oracle.com/a/ocom/docs/artificial-intelligence/cio-harness-ai-and-app-innovation-solution.pdf
[6] Salvatore Claudio Fanni et al., “Natural Language Processing,” Imaging Informatics for Healthcare Professionals, pp. 87-99, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Edward F. Watson III, and Andrew H. Schwarz, “Enterprise and Business Process Automation,” Springer Handbook of Automation, pp. 1385-1400, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Stephan Schmidt, Development of Effective Gearbox Fault Diagnosis Methodologies Utilising Various Levels of Prior Knowledge, ProQuest,” PhD Thesis, University of Pretoria, 2019.
[Google Scholar] [Publisher Link]
[9] Chase E. Golden, Michael J. Rothrock Jr, and Abhinav Mishra, “Comparison Between Random Forest and Gradient Boosting Machine Methods for Predicting Listeria spp. Prevalence in the Environment of Pastured Poultry Farms,” Food Research International, vol. 122, pp. 47-55, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Christophe Guille et al., How Field Service Organizations Can Make Work Management Systems Work, Bain and Company, 2022. [Online]. Available: https://www.bain.com/insights/how-field-service-organizations-can-make-work-management-systems-work/
[11] Dimitris Mourtzis, Nikos Panopoulos, and John Angelopoulos, “Chapter 5 - Production Management Guided by Industrial Internet of Things and Adaptive Scheduling in Smart Factories,” Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology, pp. 117-152, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Mariam Alzeraif, and Ali Cheaitou, “Artificial Intelligence-based Optimization Models for the Technical Workforce Allocation and Routing Problem considering Productivity,” International Journal of Advanced Computer Science and Applications (IACSA), vol. 14, no. 12, pp. 321-329, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hafiza Anisa Ahmed, Narmeen Zakaria Bawany, and Jawwad Ahmed Shamsi, “CaPBug - A Framework for Automatic Bug Categorization and Prioritization Using NLP and Machine Learning Algorithms,” IEEE Access, vol. 9, pp. 50496-50512, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Hadi Habibzadeh et al., “A Survey on Cybersecurity, Data Privacy, and Policy Issues in Cyber-Physical System Deployments in Smart Cities,” Sustainable Cities and Society, vol. 50, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[15] David Leslie, “Understanding Artificial Intelligence Ethics and Safety,” Understanding Artificial Intelligence Ethics and Safety, pp. 1-97, 2019.
[CrossRef] [Google Scholar] [Publisher Link]