AI-Enabled Orchestration of Event-Driven Business Processes in Workday ERP for Healthcare Enterprises
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914164893548544 |
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| author | Sharma, Monu |
| author_facet | Sharma, Monu |
| contents | The adoption of cloud-based Enterprise Resource Planning (ERP) platforms such as Workday has transformed healthcare operations by integrating financial, supply-chain, and workforce processes into a unified ecosystem. However, traditional workflow logic in ERP systems often lacks the adaptability required to manage event-driven and data-intensive healthcare environments.
This study proposes an AI-enabled event-driven orchestration framework within Workday ERP that intelligently synchronizes financial and supply-chain workflows across distributed healthcare entities. The framework employs machine-learning triggers, anomaly detection, and process mining analytics to anticipate and automate responses to operational events such as inventory depletion, payment delays, or patient demand fluctuations. A multi-organization case analysis demonstrates measurable gains in process efficiency, cost visibility, and decision accuracy.
Results confirm that embedding AI capabilities into Workday's event-based architecture enhances operational resilience, governance, and scalability. The proposed model contributes to the broader understanding of intelligent ERP integration and establishes a reference for next-generation automation strategies in healthcare enterprises. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Enabled Orchestration of Event-Driven Business Processes in Workday ERP for Healthcare Enterprises Sharma, Monu Software Engineering The adoption of cloud-based Enterprise Resource Planning (ERP) platforms such as Workday has transformed healthcare operations by integrating financial, supply-chain, and workforce processes into a unified ecosystem. However, traditional workflow logic in ERP systems often lacks the adaptability required to manage event-driven and data-intensive healthcare environments. This study proposes an AI-enabled event-driven orchestration framework within Workday ERP that intelligently synchronizes financial and supply-chain workflows across distributed healthcare entities. The framework employs machine-learning triggers, anomaly detection, and process mining analytics to anticipate and automate responses to operational events such as inventory depletion, payment delays, or patient demand fluctuations. A multi-organization case analysis demonstrates measurable gains in process efficiency, cost visibility, and decision accuracy. Results confirm that embedding AI capabilities into Workday's event-based architecture enhances operational resilience, governance, and scalability. The proposed model contributes to the broader understanding of intelligent ERP integration and establishes a reference for next-generation automation strategies in healthcare enterprises. |
| title | AI-Enabled Orchestration of Event-Driven Business Processes in Workday ERP for Healthcare Enterprises |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2511.15852 |