Predictive Change Governance in Enterprise SAP Landscapes: A Risk-Aware AI Model for Transport Release Optimization
DOI:
https://doi.org/10.22399/ijcesen.5521Keywords:
SAP transport governance, predictive change governance, enterprise application lifecycle management, transport risk scoring, release optimization, regression test prioritizationAbstract
When dealing with enterprise SAP landscapes, the release of business change must happen rapidly and be regression stable, transport stable, audit traceable and compliant with control. There is a wealth of existing SAP governance evidence (which is not necessarily coordinated) which includes Central ATC code quality findings, BPCA business-process impact estimates, UPL and SCMON production-usage signals, Test Suite and HPALM regression evidence, ChaRM transport history, and CSOL and Downgrade Protection collision risk. However, these evidence streams are only reported in typical governance workflows in isolation and are not natively integrated into an explainable transport-level risk score that can be leveraged to drive release sequencing and test prioritization. This paper outlines a model for predictive SAP transport governance, which we call the Transport Risk Score (TRS), proposed by the practitioners. The model combines the ATC delta severity, BPCA process criticality, test coverage gaps, object dependency depth, historical incident rate, CSOL/Downgrade collision risk, Retrofit conflict risk, emergency change factors and calendar / freeze period risk to a normalized 0-100 score. The paper also introduces a risk aware release queue optimizer, a UPL-enriched BPCA test-scope optimizer, a TRS Evidence Package Generator and a validation protocol with chronological splits, calibration, precision-recall analysis, false-negative cost and decision utility. The study draws on academic literature on ERP governance, static analysis, regression test selection, defect prediction, process mining, release planning and explainable AI, as well as anonymised design evidence from SAP solution managers in enterprise ERP implementations across the food service, beverage, pharmaceutical, semiconductor, healthcare, retail and oil and gas sectors, together with anonymized practitioner design evidence from enterprise SAP implementation contexts. The study draws on academic literature on ERP governance, static analysis, regression test selection, defect prediction, process mining, release planning, and explainable AI, together with anonymized practitioner design evidence from enterprise SAP implementation contexts across the food service, beverage, pharmaceutical, semiconductor, healthcare, retail and oil and gas sectors provided by enterprise SAP solution managers at the official SAP forums. TRS is not intended to provide autonomous release authority; rather, it offers a reusable, auditable decision-support mechanism for transport-risk visibility, targeted regression, release optimization, and post-release learning.
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