Process Transformation Through SAP Business Process Intelligence: A Conceptual Framework for Data-Driven ERP Transformation
Abstract: Enterprise Resource Planning (ERP) transformation programs are increasingly expected to deliver not only technology modernization but also measurable improvements in operational performance, process standardization, compliance, and enterprise agility. However, many ERP transformation initiatives remain constrained by fragmented process landscapes, legacy customizations, limited end-to-end visibility, and functionally siloed decision-making. Traditional process assessment methods, which often rely on stakeholder interviews, workshops, and static process documentation, provide useful qualitative insights but may not fully reveal how business processes operate across enterprise systems. Process mining and Business Process Intelligence (BPI) offer a more evidence-based approach by using event data from operational systems to discover, monitor, and improve real business processes. This paper develops a conceptual framework for SAP BPI-enabled ERP transformation, drawing on literature from Business Process Management (BPM), Business Process Reengineering (BPR), process mining, and ERP implementation critical success factors. The proposed framework integrates process discovery, process assessment, conformance analysis, optimization, governance, continuous monitoring, and business value realization. The paper argues that SAP BPI, supported by SAP Signavio and related process mining capabilities, can help organizations shift from system-centric to process-centric transformation. The study contributes to academic and practitioner understanding by positioning process intelligence as a governance and operating model capability rather than merely an analytical tool.
Keywords: SAP Business Process Intelligence, process mining, Business Process Management, ERP transformation
How to Cite?: Atul Aggarwal, Avnish Goyal, "Process Transformation Through SAP Business Process Intelligence: A Conceptual Framework for Data-Driven ERP Transformation", Volume 15 Issue 11, November 2026, International Journal of Science and Research (IJSR), Pages: 401-405, https://www.ijsr.net/getabstract.php?paperid=SR261109225240, DOI: https://dx.doi.org/10.21275/SR261109225240