COGNITIVE LOGISTICS ORCHESTRATION: A MODEL-BASED ARCHITECTURE FOR PREDICTIVE ALIGNMENT AND SYSTEMIC RESILIENCE IN VOLATILE SUPPLY NETWORKS

Paper ID: EIJTEM_2026_13_3_272-285

Author's Name: Simon Suwnazy Dzreke, Semefa Elikplim Dzreke, Evans Dzreke, Franklin Manasey Dzreke

Volume: 13

Issue: 3

Year: 2026

Page No: 272-285

Abstract:

The modern logistics network is a volatile, adaptive system. The disruptions spread quickly across the connected nodes. This highlights the weaknesses of supply chains optimized for efficiency but lacking execution speed and predictive acuity. This study builds and tests a Model-Based Risk Management (MBRM) system that leverages real-time sensing, predictive modeling, and regulated autonomous execution to transform information velocity into operational resilience. The sequential mixed-methods design integrates the results from ten multinational firms with a 2000-step, 1000-agent simulation described in the Overview, Design concepts, and Details (ODD) protocol. Empirical results demonstrate that Predictive Alignment (PA), Systemic Resilience (SR), and Signal Fidelity (SF) are positively associated with Value Orchestration Efficiency (VOE), whereas shorter decision-to-execution latency decreases volatility. Simulation results show that integrated cognitive architectures exhibit higher VOE, recover 7X faster, and reduce predicted revenue-volatility variance by 90+% in the presence of compound shocks. Qualitative insights indicate that responsible deployment depends on regulated autonomy, interpretability methods and specialized organizational roles. This study conceptualizes predictive intelligence as an operational tool to anticipate, absorb, and respond to disturbances in perpetually variable supply networks.

Keywords: Predictive logistics, resilience engineering, digital twins, autonomous orchestration, supply-chain volatility, agent-based modeling

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