An Integrated Big Data and Predictive Analytics Framework for Supply Chain Resilience, International Trade and Economic Growth

Abstract

Global supply networks face compound disruptions arising from geopolitical conflict, climate hazards, cyber incidents, logistics bottlenecks, demand volatility, and policy uncertainty. Existing studies demonstrate that big data analytics can improve forecasting and supply chain performance, but they seldom explain the full conversion mechanism through which data resources become actionable resilience or how firm-level resilience contributes to trade continuity and economic performance. This article develops a multilevel capability to outcome framework that links six stages: multi-source data acquisition, governed data integration, predictive intelligence, resilience orchestration, international trade continuity, and productivity-oriented growth outcomes. The theoretical novelty lies in three mechanisms. First, it distinguishes predictive accuracy from decision actionability and identifies resilience orchestration as the dynamic capability that converts forecasts into coordinated operational responses. Second, it specifies cross-level transmission from firm capability to supply-network stability and then to trade continuity. Third, it defines explicit boundary conditions, including shock observability, response discretion, input substitutability, network concentration, cyber exposure, and institutional digital capacity. To address feasibility concerns, the paper proposes a focused first-stage validation in export-oriented automotive-component manufacturing using a three-wave, 24-month panel that combines survey measures with operational and shipment records. Longitudinal structural equation modelling and firm fixed-effects estimation are designated as the primary methods; machine-learning comparison and macroeconomic aggregation are retained as secondary extensions rather than simultaneous requirements. Economic growth is treated as a distal outcome mediated by resilient trade and productivity, not as a direct consequence of technology adoption. The revised framework offers a more parsimonious, falsifiable, and practically implementable research program for firms and policy institutions.