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DOI: https://doi.org/10.63345/ijrsml.v10.i3.1
Dr. Sohrab Bharucha
Director Operations
Spica Group
Pune, Maharashtra, India
https://orcid.org/0000-0003-4195-6191
Abstract— Organizational restructuring has traditionally been implemented as a reactive response to declining financial performance, operational inefficiencies, or external market disruptions. Existing restructuring models primarily emphasize retrospective performance analysis, expert judgment, or isolated optimization techniques, offering limited capability for proactively identifying organizational decline or recommending data-driven renewal strategies. Furthermore, current studies rarely integrate predictive analytics, organizational health assessment, renewal readiness evaluation, and restructuring optimization within a unified decision-support framework. To address these research gaps, this paper proposes the Predictive Restructuring Framework for Organizational Renewal (PRFOR), an intelligent framework that combines machine learning-based decline prediction, a multidimensional Organizational Health Score (OHS), a Renewal Readiness Index (RRI), and Genetic Algorithm-based restructuring optimization to support proactive organizational transformation. The framework analyzes financial, operational, workforce, innovation, governance, and digital transformation indicators to forecast restructuring requirements and recommend optimal strategic interventions before organizational performance deteriorates significantly. Experimental evaluation using a multi-industry organizational dataset demonstrates that the proposed framework achieves superior predictive capability, with the XGBoost model attaining 95.1% prediction accuracy and an AUC of 0.978. The optimized restructuring strategies further improve organizational agility, operational efficiency, employee productivity, and resource utilization while increasing revenue growth and innovation performance following renewal implementation. Compared with conventional restructuring approaches, PRFOR provides earlier risk detection, comprehensive organizational assessment, and automated decision support for strategic renewal. The proposed framework contributes a scalable and intelligent methodology that shifts organizational restructuring from reactive crisis management to proactive, evidence-based organizational renewal, enabling enterprises to enhance resilience and sustain long-term competitive advantage in rapidly evolving business environments.
Keywords— Organizational restructuring, organizational renewal, predictive analytics, machine learning
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