Reinforcement Learning Based Shift and Leave Optimization Engine Built on SAP SuccessFactors Time Management and SAP BTP Services

Leonhardt Weiss, Keisuke Moriyama, Alina Kovalenko, Victor Salgado and Ananya Kulkarni

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 4, 909-923

Abstract: Human capital management platforms increasingly store extensive workforce information that reflects employee performance, skill development, organizational mobility, and leadership potential. Systems such as SAP SuccessFactors integrate multiple talent management modules that support performance evaluation, learning development, and succession planning processes. Despite the availability of these comprehensive datasets, talent management decisions related to promotion, internal transfer, and leadership succession often rely on manual evaluation across multiple system components. This research proposes an LLM enabled HR decision support layer designed to generate interpretable recommendations for promotion readiness, internal mobility opportunities, and succession candidate identification within SAP SuccessFactors environments. The framework integrates workforce data from core HR records, performance management systems, learning histories, and succession planning indicators to construct comprehensive employee capability profiles. Analytical models evaluate workforce signals such as performance trajectories, competency development, and career progression patterns to generate structured indicators of employee advancement potential. A large language model reasoning component transforms these analytical outputs into human readable explanations that describe the factors influencing each recommendation. The decision support layer operates as an intelligent advisory service that integrates with existing SAP SuccessFactors workflows while preserving human oversight in critical talent management decisions. By combining workforce analytics with interpretable language based reasoning, the proposed framework enables organizations to improve transparency in promotion and succession decisions, strengthen internal mobility strategies, and enhance the consistency of talent management practices within enterprise HR ecosystems.

Keywords: Large Language Models; HR Decision Support Systems; SAP SuccessFactors; Explainable Artificial Intelligence; Talent Management Analytics; Promotion Recommendation Systems; Internal Mobility Intelligence; Succession Planning Analytics; Workforce Data Intelligence; Human Capital Management Systems; Organizational Talent Analytics; Career Progression Modeling; AI Assisted HR Decision Support; Employee Capability Profiling; Leadership Pipeline Analytics (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i4:id:hcseit23564535

DOI: 10.32628/CSEIT23564535

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