Author
Listed:
- Priya Ramanathan
- Lukas Weber
- Sofia Martinez
- Ethan Caldwell
- Ananya Kulkarni
Abstract
Human capital systems are increasingly expected to operate as intelligent digital infrastructures capable of responding dynamically to organizational change. Conventional implementations of SAP SuccessFactors primarily rely on configuration-driven workflows and batch-oriented data processing, which often limits the platform’s ability to react to real-time workforce signals, operational disruptions, and evolving compliance requirements. This research investigates the development of self-optimizing human capital platforms through the integration of machine learning models, event-streaming architectures, and advanced database engineering practices within SAP SuccessFactors environments. The proposed framework introduces predictive analytics for identifying workforce patterns, event-driven pipelines that capture and process HR transactions as they occur, and optimized data-layer strategies designed to manage large-scale employee datasets with improved performance and reliability. Event streaming technologies enable continuous synchronization between HR modules, integration layers, and external systems, while machine learning algorithms support adaptive decision-making in areas such as workforce planning, talent mobility, performance monitoring, and compliance tracking. In parallel, database engineering techniques—including schema optimization, query acceleration, and scalable data pipelines—ensure that analytical workloads and operational transactions can coexist without degrading system efficiency. The study demonstrates how the convergence of these technological layers can transform SAP SuccessFactors from a configuration-centric HR platform into a responsive and adaptive ecosystem capable of learning from operational data. By enabling real-time insights and automated optimization of HR processes, the proposed approach contributes to the development of next-generation human capital platforms designed to support continuous organizational evolution and data-driven workforce governance.
Suggested Citation
Priya Ramanathan & Lukas Weber & Sofia Martinez & Ethan Caldwell & Ananya Kulkarni, 2020.
"Toward Self-Optimizing Human Capital Platforms: Machine Learning, Event Streaming, and Database Engineering for Adaptive SAP SuccessFactors Operations,"
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 6(6), pages 448-462, December.
Handle:
RePEc:jbh:ijsrcs:v6:y2020:i6:id:hcseit2066444
DOI: 10.32628/CSEIT2066444
Note: Article URL: https://ijsrcseit.com/CSEIT2066444
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v6:y2020:i6:id:hcseit2066444. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (email available below). General contact details of provider: https://ijsrcseit.com/home .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.