Business Management
Nemer Badwan; Lamees Al-Zoubi; Suhaib Al-Khazaleh
Volume 7, Issue 1 , January 2023, , Pages 1-27
Abstract
Financial risk management and risk identification management have evolved into critical components of global supply networks. Machine learning, business intelligence, and big data technologies are used to detect system risks, manage financial risks, and help identify the cause of risks. The company may ...
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Financial risk management and risk identification management have evolved into critical components of global supply networks. Machine learning, business intelligence, and big data technologies are used to detect system risks, manage financial risks, and help identify the cause of risks. The company may strive to improve the source of the risk. The methodology concentrates on three major supply chain danger areas: shipping, marketing, and distribution, with a licensed insurance model built, evaluated, and matched to current approaches before being adjusted based on the results. In this scenario, we're also using Business Intelligence (BI), which helps businesses make better data-driven decisions. As a result, reduce losses incurred. A framework of risk detection models based on machine learning, business intelligence and big data is provided to control financial risks in the supply chain. This study aims to identify risks, identify their sources and mitigate them. The proposed approach focuses on three distinct areas of supply chain risk: transportation, sales, and delivery. A risk disclosure model is used, tested, compared with existing methodologies, and then refined depending on the results for each area. Regardless of the supply chain data used, the proposed method is adaptable to any other type of supply chain.