Abstract
Enterprise logistics financing has become increasingly dependent on efficient transaction settlement mechanisms due to growing supply chain complexity, distributed business operations, and increasing requirements for real-time financial coordination. Traditional settlement processes often experience delays caused by fragmented information systems, manual verification procedures, limited predictive capability, and inefficient decision workflows. These limitations affect liquidity management, supplier relationships, and overall logistics performance. This research proposes a Blended Sequential Decision Methodology (BSDM) designed to reduce settlement latency by integrating decision support principles, sequential optimization strategies, predictive analysis, and adaptive learning mechanisms.
The proposed methodology combines rule-based reasoning, case-based decision processes, and intelligent computational approaches to improve financial settlement efficiency. Decision support systems provide structured mechanisms for analyzing complex operational conditions and generating optimized recommendations (Gachet and Haettenschwiler, 2006). The framework extends these concepts by introducing sequential decision layers where transaction states are continuously evaluated and optimized according to changing logistics conditions.
The proposed model consists of four major stages: transaction state identification, intelligent decision generation, settlement optimization, and adaptive feedback learning. The first stage captures financial and logistics transaction information, including payment status, verification requirements, and operational dependencies. The second stage applies blended reasoning approaches by combining historical cases with predictive decision models. The third stage focuses on reducing settlement delays through optimized workflow execution. The final stage enables continuous improvement by analyzing previous settlement outcomes.
The research highlights that a blended decision framework can improve settlement responsiveness, increase operational transparency, and support proactive financial management. However, challenges related to data availability, model interpretability, integration complexity, and organizational adoption remain significant. This study contributes a conceptual foundation for intelligent settlement systems capable of improving financial efficiency in modern logistics ecosystems.