Data-driven decision-making (DDDM) is increasingly viewed as a strategic driver of efficiency, agility, and competitiveness by reducing uncertainty and enabling evidence-based choices. However, its effective adoption is often constrained by challenges such as poor data quality, fragmented governance, technological limitations, cultural resistance, and ethical concerns. This study provides a comprehensive review of literature and industry practices to evaluate the applications, benefits, and challenges of DDDM across production planning, logistics, quality management, and performance optimization. The analysis reveals significant gaps, particularly the absence of comprehensive frameworks that integrate technological infrastructure, governance mechanisms, human expertise, organizational culture, and sustainability objectives. To address these gaps, the study proposes a strategic framework organized around six interdependent pillars: technological infrastructure, data governance, human-centric empowerment, organizational alignment, ethical safeguards, and sustainability orientation. Reinforced by the Lean Six Sigma DMAIC methodology, the framework provides a systematic and iterative pathway for translating data insights into continuous improvement and operational excellence. By bridging perspectives from analytics, operations management, and organizational behavior, the study contributes both theoretically and practically, offering a roadmap for developing resilient, responsible, and high-performing organizations. It underscores that the unique value of DDDM lies not only in advanced analytics but in aligning data, people, and processes to achieve sustainable excellence.
A Fri, study studied this question.