Integrated Framework for Enhancing Resilience and Profitability Across Healthcare, Supply Chains, and SMEs
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Abstract
In an era of digital proliferation and dynamic global challenges, the ability to transform data into informed decisions has emerged as a critical success factor across industries. This paper explores the cross-sectoral implementation of Big Data and Business Intelligence (BI) to improve resilience and profitability in healthcare systems, supply chains, and small-to-medium enterprises (SMEs). The study proposes an integrated data-to-decision (D2D) framework and validates its efficacy through sector-specific analyses. In healthcare, BI tools are enabling predictive diagnostics, operational cost control, and resource optimization. In supply chains, real-time analytics and AI-driven insights support risk mitigation and sustainability. Meanwhile, SMEs leverage data analytics to compete more effectively, forecast demand, and improve customer retention. Drawing from empirical data, theoretical frameworks like RBV and UTAUT, and real-world case studies, the research highlights how an orchestrated use of BI systems enhances organizational agility, profitability, and long-term value. Additionally, it investigates ethical implications such as data privacy, algorithmic bias, and the digital divide. This paper concludes with strategic recommendations to promote standardized data protocols, support for digital transformation in SMEs, and cross-sector collaboration for equitable data utilization.
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