Continuous Delivery for AI-Native Supply Chains: PM Frameworks for SAP Automation
Abstract
The rapid evolution of global supply chains has necessitated the integration of artificial intelligence (AI) and automation within enterprise systems to enhance agility, efficiency, and resilience. This study investigates the role of continuous delivery (CD) practices in enabling AI-native supply chains through SAP automation and examines how project management (PM) frameworks moderate these relationships. A mixed-method research design was employed, combining survey data from 220 respondents across 15 organizations with qualitative interviews and secondary data analysis. The results demonstrate that higher deployment frequency and automation rates significantly improve SAP automation outcomes, while longer lead times and higher change failure rates exert negative effects. AI-native supply chain capabilities, such as predictive demand forecasting and anomaly detection, further amplify the benefits of CD, acting as mediators in driving automation performance. Importantly, PM frameworks including Agile, DevOps maturity, and hybrid governance models were found to moderate the CD–SAP relationship, with DevOps maturity providing the strongest positive effect. Qualitative findings reinforced these results, highlighting governance, organizational resistance, and skill gaps as critical barriers to adoption. By integrating CD principles with SAP automation and embedding them within structured PM frameworks, enterprises can transform supply chains into adaptive, intelligent systems. The study contributes to theory by bridging software engineering practices with supply chain management and offers practical insights for organizations aiming to enhance competitiveness in digital ecosystems.
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