<![CDATA[Digital transformation has become a critical strategic priority for organizations seeking to enhance operational performance, decision-making efficiency, and competitiveness in an increasingly complex business environment. However, despite the rapid adoption of technologies such as artificial intelligence (AI), predictive analytics, automation, and digital platforms, many organizations continue to face challenges in translating digital insights into actionable operational decisions because of fragmented processes, limited employee understanding, and inadequate governance mechanisms. This study aimed to develop the Predict–Simulate–Logistics–Execute Governance (PSLE-G) Framework as a conceptual model for simplifying digital transformation and improving operational decision-making. The study employed a conceptual research approach supported by a systematic literature review of previous studies on digital transformation, operational decision-making, supply chain management, decision intelligence, digital twins, and organizational governance. The literature synthesis indicated that existing frameworks primarily emphasize technological capabilities, optimization functions, and analytical support while providing limited guidance on how employees can systematically translate digital information into coordinated operational decisions. The proposed PSLE-G Framework integrates four interconnected stages—Predict, Simulate, Logistics, and Execute—supported by governance and continuous learning mechanisms. The framework contributes to bridging the gap between digital technologies and day-to-day operational practices by providing a structured approach that may enhance decision consistency, employee adaptability, cross-functional collaboration, and organizational resilience. The study concludes that the PSLE-G Framework provides a practical conceptual foundation for organizations navigating digital transformation and establishes opportunities for future empirical validation across different industries and organizational contexts.]]>
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