Mastering Oracle HCM Post-Deployment: Strategies for Scalable and Adaptive HR Systems
Keywords:
Oracle HCM, HR Technology, Post-DeploymentAbstract
Designed as a strong cloud-based solution, Oracle Human Capital Management (HCM) maximizes HR operations including workforce analytics, payroll & talent acquisition. Still, the actual problem begins after the deployment: ensuring the system's flexibility, scalability & fit for evolving their corporate needs. The important strategies companies should follow after deployment to maximize the value of Oracle HCM are investigated in this paper. An orderly post-implementation strategy helps HR departments to improve their system performance, streamline processes & maintain their regulatory compliance. Reducing resistance & enabling user acceptance depend on the constant training & change management. Moreover, integration of Oracle HCM with any other business systems enhances operational effectiveness & data consistency. The human resources needs of businesses alter as they grow; so, scalability becomes a major concern. By means of automated systems, artificial intelligence-generated insights, and regular system reviews, companies might maintain a flexible HR structure enabling strategic decision-making and personnel planning. Essentials are security & data privacy, which calls for rigorous access limitations & proactive risk control. From a simple HR tool, Oracle HCM becomes a strategic asset improving worker engagement, operational efficiency & sustained corporate performance thanks to a proactive post-deployment approach.
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References
Jain, Manojkumar. Quality Function Deployment And Server Task Automation. Diss. Institute of Technology, 2015.
RPG, IV. "Essentials of Subfile Programming and Advanced Topics in RPG IV."
McPhee, Michael. Mastering Kali Linux for Web Penetration Testing. Packt Publishing Ltd, 2017.
Huang, Shihong. An integrated approach to program redocumentation. University of California, Riverside, 2004.
Dyer, Lee, and Jeff Ericksen. "Dynamic organizations: Achieving marketplace agility through workforce scalability." (2006).
Morris, S., and S. Snell. "The evolution of HR strategy: adaptations to increasing global complexity." The SAGE handbook of human resource management (2010): 84-99.
Nijssen, Marc, and Jaap Paauwe. "HRM in turbulent times: how to achieve organizational agility?." The International Journal of Human Resource Management 23.16 (2012): 3315-3335.
Arena, Michael J., and Mary Uhl-Bien. "Complexity leadership theory: Shifting from human capital to social capital." People and Strategy 39.2 (2016): 22.
Colajanni, Michele, Philip S. Yu, and Daniel M. Dias. "Analysis of task assignment policies in scalable distributed Web-server systems." IEEE transactions on Parallel and Distributed Systems 9.6 (1998): 585-600.
Youndt, Mark A., et al. "Human resource management, manufacturing strategy, and firm performance." Academy of management Journal 39.4 (1996): 836-866.
Han, Rui, et al. "Enabling cost-aware and adaptive elasticity of multi-tier cloud applications." Future Generation Computer Systems 32 (2014): 82-98.
Katou, Anastasia A., and Pawan S. Budhwar. "Human resource management systems and organizational performance: a test of a mediating model in the Greek manufacturing context." The international journal of human resource management 17.7 (2006): 1223-1253.
Yan, Le, Jiong Luo, and Niraj K. Jha. "Combined dynamic voltage scaling and adaptive body biasing for heterogeneous distributed real-time embedded systems." ICCAD-2003. International Conference on Computer Aided Design (IEEE Cat. No. 03CH37486). IEEE, 2003.
Arabnejad, Hamid, et al. "A comparison of reinforcement learning techniques for fuzzy cloud auto-scaling." 2017 17th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID). IEEE, 2017.
Paauwe, Jaap, and Corine Boon. "Strategic HRM: A critical review." Human resource management (2018): 49-73
