AUTOMATION OF TIME, MATERIAL, AND EXPENSE (TME) REPORTING IN ROBOTIC SERVICE OPERATIONS
Keywords:
Robotic TME Reporting, Service Automation, Enterprise System Integration, Predictive Maintenance, Data-Driven Decision MakingAbstract
This comprehensive article examines the transformative impact of robotic systems on Time, Material, and Expense (TME) reporting in service industries. The article delves into the sophisticated mechanisms by which robots collect, process, and report TME data, focusing on their capacity to enhance resource management and drive continuous improvement in service delivery. Through an analysis of advanced sensor integration, automated report generation, and seamless enterprise system integration, the article highlights the significant improvements in data accuracy, real-time reporting, and operational efficiency achieved through robotic TME systems. Case studies from manufacturing and utility sectors provide concrete evidence of the benefits, including substantial reductions in downtime, maintenance costs, and response times, alongside improvements in inventory management and predictive maintenance capabilities. The article also addresses key challenges such as data accuracy, integration complexity, and cost management while emphasizing the opportunities for enhanced efficiency, improved resource allocation, and data-driven decision-making. By synthesizing findings from recent technological advancements and industry applications, this article offers valuable insights into the current state and future potential of robotic TME reporting, underlining its crucial role in shaping the future of service operations across diverse industries.
References
Ivančić, Lucija & Suša Vugec, Dalia & Vuksic, Vesna. (2019). Robotic Process Automation: Systematic Literature Review. 10.1007/978-3-030-30429-4_19. [Online]. Available: https:// https://www.researchgate.net/publication/335400552_Robotic_Process_Automation_Systematic_Literature_Review
M. Grieves and J. Vickers, "Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems," in Transdisciplinary Perspectives on Complex Systems, F.-J. Kahlen, S. Flumerfelt, and A. Alves, Eds. Cham: Springer International Publishing, 2017, pp. 85–113. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-38756-7_4
Rao, S.K., Prasad, R. Impact of 5G Technologies on Industry 4.0. Wireless Pers Commun 100, 145–159 (2018). https://doi.org/10.1007/s11277-018-5615-7. [Online]. Available: https://doi.org/10.1007/s11277-018-5615-7
Zheng, P., wang, H., Sang, Z. et al. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13, 137–150 (2018). https://doi.org/10.1007/s11465-018-0499-5
M. Abouelyazid, “Advanced Artificial Intelligence Techniques for Real-Time Predictive Maintenance in Industrial IoT Systems: A Comprehensive Analysis and Framework”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 271–313, Feb. 2023, Accessed: Aug. 06, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/83
Yuqian Lu, Chao Liu, Kevin I-Kai Wang, Huiyue Huang, Xun Xu, Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues, Robotics and Computer-Integrated Manufacturing, Volume 61,2020, 101837, ISSN 0736-5845, https://doi.org/10.1016/j.rcim.2019.101837.
Qiu, Robert Caiming, Zhen Hu, Zhe Chen, Nan Guo, Raghuram Ranganathan, Shujie Hou and Gang Zheng. “Cognitive Radio Network for the Smart Grid: Experimental System Architecture, Control Algorithms, Security, and Microgrid Testbed.” IEEE Transactions on Smart Grid 2 (2011): 724-740.. [Online]. Available: https://ieeexplore.ieee.org/document/5971793
J. Lee, B. Bagheri, and H. Kao, "A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems," Manufacturing Letters, vol. 3, pp. 18-23, Jan. 2015. [Online]. Available: https://doi.org/10.1016/j.mfglet.2014.12.001
