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@article{198325,
        author = {VAIBHAV NARAYAN MOTE},
        title = {Supply Chain Analytics as a Strategic Imperative: An Integrated Framework for Data-Driven Decision-Making in Modern Supply Chains},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10591-10608},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198325},
        abstract = {Background: Modern supply chains have undergone a decisive transformation, evolving from peripheral logistical support functions into core drivers of enterprise competitive positioning. Notwithstanding this elevation, supply chains remain chronically vulnerable to demand volatility, inventory imbalances, constrained multi-tier visibility, and structural inflexibility that conventional managerial approaches prove ill-equipped to resolve.
Objective: This paper constructs a theoretically grounded and operationally actionable framework for supply chain analytics, examining its application systematically across strategic, tactical, and operational decision-making horizons.
Method: A conceptual-analytical methodology is employed, integrating established theoretical frameworks with formal quantitative models — comprising the SCOR Model, the Economic Order Quantity (EOQ) and its multi-echelon extensions, the Gravity Model, mixed-integer programming, and decision tree analysis — alongside a structured synthesis of peer-reviewed supply chain management literature.
Findings: System-wide coordinated inventory optimization consistently outperforms isolated stage-level approaches, with documented total cost reductions of 15–35% across representative configurations. Adaptive demand forecasting methods measurably attenuate variability and suppress bullwhip effect amplification. Supply chain flexibility yields value that scales proportionally with environmental uncertainty.
Contribution: Three distinct scholarly contributions are advanced: (1) the integration of the SCOR Model as analytics organizing framework, a treatment absent from comparable reviews; (2) a progressive analytics investment roadmap calibrated to organizational maturity; and (3) a unified treatment of technical optimization and behavioural enablers within a single coherent framework. Implications are articulated for IoT integration, artificial intelligence, environmental sustainability, supply chain resilience, and humanitarian logistics.},
        keywords = {Supply Chain Analytics; Data-Driven Decision-Making; Demand Forecasting; Inventory Optimization; SCOR Model; Supply Chain Resilience; Prescriptive Analytics; Multi-Echelon Inventory},
        month = {April},
        }