Engineering Payroll Systems for Highly Unionized and Regulated Workforces: A Deterministic Rule-Based Architecture and Governance Framework
Keywords:
Payroll Engineering; Collective Bargaining Agreements; Deterministic Rule-Based Architecture; Payroll Governance Framework; Enterprise HCM Platforms; Grade-Step Progression; Retroactive Pay Computation; Multi-jurisdictional Payroll Compliance; Audit TraceabilityAbstract
Enterprise HCM systems do provide the business rules‚ rate components and payroll calculations to accomplish this․ There are enterprise implementations with enterprise-wide Collective Bargaining Agreements (CBA) in healthcare‚ utilities‚ public transit‚ higher education‚ and municipal government․ This level of complexity may be beyond that of a typical HCM implementation․ Contract elements‚ such as eligibility requirements‚ seniorities‚ shift differentials‚ premium pay‚ progressions‚ and retroactive and multi-jurisdictional events‚ do not occur in most payroll engineering scenarios; hence they require discipline to be represented appropriately․ We define payroll engineering as a discipline and introduce a five layer deterministic rule based framework to ease systematic contract to business rule processing and testing․ Business Rules include components such as components eligibility‚ component rates‚ calculation order‚ retroactive pay‚ costing and reporting‚ which are common to all contracts․ The functional architecture of Oracle Fusion Cloud HCM is comparable to other systems in its market‚ including SAP SuccessFactors‚ Ceridian Dayforce‚ ADP Workforce Now and Workday Payroll․ The paper discusses three high complexity scenarios‚ develops a new governance model with rule versioning‚ test coverage‚ parallel validation of payroll‚ and clause-to-paycheck traceability․ Results showed that the architecture reduced the rate of payroll errors by 91․1%‚ the time taken to process retroactive changes by 95․2%‚ and achieved a rate of compliance with the multi-jurisdictions rule of 99․4%․
Downloads
References
Samrat Mukherjee, “AI-Driven Autonomous IT Operations: A Human-in-the-Loop AIOps 2.0 Framework ”, Int J Intell Syst Appl Eng, vol. 12, no. 23s, pp. 4317–4325, Aug. 2024.
Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. (2017). Manufacturing Advantage: Why High-Performance Work Systems Pay Off. Cornell University Press.
Bondarouk, T., & Ruel, H. (2009). Electronic human resource management: Challenges in the digital era. International Journal of Human Resource Management, 20(3), 505–514.
Brachman, R. J., & Levesque, H. J. (2004). Knowledge Representation and Reasoning. Morgan Kaufmann.
Cao, Min, Roman Chychyla, and Trevor Stewart. "Big data analytics in financial statement audits."Accounting horizons 29, no. 2 (2015): 423-429.
Chatterjee, Suchismita. "A data governance framework for Big Data Pipelines: Integrating Privacy, security, and quality in Multitenant Cloud Environments." Technix International Journal for Engineering Research 10 (2023).
Dunleavy, Patrick, and Helen Zerlina Margetts. "The second wave of digital era governance." In APSA 2010 Annual Meeting Paper. 2010.
Shashank Daram, “A Review of Retrieval-Augmented Generation in Natural Language Processing: Architectures, Challenges, and Future Research Directions” , International Journal on Recent and Innovation Trends in Computing and Communication; vol 8 (12), 2020 :145-155.
Forgy, Charles L. "Rete: A fast algorithm for the many pattern/many object pattern match problem." In Readings in artificial intelligence and databases, pp. 547-559. Morgan Kaufmann, 1989.
Fossum, John Erik. "Extending the Coupling Concept: Slack, Agency and Fields." In Configurations, Dynamics and Mechanisms of Multilevel Governance, pp. 311-327. Cham: Springer International Publishing, 2019.
Afzal, H., & Huzaifa, M. (2024). Securing AI systems against adversarial attacks: A framework for building robust and trustworthy machine learning models. Computer Fraud & Security, 2024(5), 65.
Shashank Lohani, The Learning Management System as a Product: A Computer Science Perspective on Higher Education Strategy, International Journal of Communication Networks and Information Security; vol 11 (2), 2019 page no 353-363
Guru Charan Kakaraparthi. (2024). Integrating Serverless Architectures and Kubernetes for Scalable and High-Availability AI Workflows. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5896–5905. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7892
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
