Enterprise decision-making increasingly depends on the ability to integrate heterogeneous information, manage uncertainty, optimize resource allocation, and respond rapidly to dynamic operational conditions. Conventional information management approaches often treat information acquisition, decision analysis, and optimization as separate activities, limiting their ability to address complex and uncertain decision environments. This paper proposes a Metaheuristic-Driven Intelligent Information Management Framework for Enterprise Decision Optimization that conceptually integrates fuzzy information representation, dynamic network modeling, adaptive decision mechanisms, and metaheuristic optimization. The framework is theoretically positioned using the supplied literature on fuzzy optimal allocation, dynamic network flow, emergency evacuation decision-making, human behavioral uncertainty, and route optimization. Although the referenced studies primarily address emergency evacuation and transportation-related decision environments, their underlying principles—dynamic optimization, fuzzy constraints, uncertain behavioral responses, collective decision-making, and resource allocation—are transferable to enterprise information management. The proposed methodology consists of information acquisition, uncertainty modeling, decision-network construction, metaheuristic search, adaptive evaluation, and decision-support generation. Analytical findings indicate that combining fuzzy representations with dynamic optimization can improve the robustness of enterprise decisions under uncertain conditions, while metaheuristic search can address large combinatorial decision spaces more flexibly than rigid deterministic approaches. The framework contributes a conceptual bridge between intelligent information management and optimization-driven enterprise decision support, while recognizing limitations associated with computational complexity, parameter sensitivity, and the absence of empirical enterprise datasets. The proposed framework provides a foundation for future simulation-based and real-world validation.

This study examines the role of dealership-based financial products in shaping vehicle affordability and managing consumer financial risk in the contemporary U.S. automotive retail market. Rising average transaction prices, which approached $48,600 for new vehicles in 2024, combined with a 63% increase in auto loan delinquencies between 2021 and 2024, have intensified the relevance of Finance and Insurance (F&I) instruments for both buyers and retail institutions. Using a systematic review of peer-reviewed literature, regulatory reports, and industry data, this paper analyzes how products such as Guaranteed Asset Protection (GAP) insurance, vehicle service contracts, payment protection, and dealer-arranged financing collectively function as a consumer risk-transfer mechanism. The analysis identifies a taxonomy of F&I products by risk category, evaluates their distributional effects across credit tiers, and proposes an original Dealer Financial Product Accessibility Model (DFPAM) linking product bundling strategy to affordability outcomes. Findings indicate that structured F&I bundling, when aligned with borrower credit profiles, can reduce 12-month default probability for subprime consumers and extend effective vehicle access to households otherwise excluded by credit constraints. The results are of interest to automotive retail managers, consumer finance regulators, and researchers in applied financial economics.