2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 0 , Issue 2 , PP: 75-82, 2019 | Cite this article as | XML | Html | PDF
Noura Metawa 1 *
Predictive modeling plays a pivotal role in enhancing supply chain financial optimization by accurately forecasting business demand. This study investigates the efficacy of employing Gradient Boosting Decision Trees (GBDT) as a predictive modeling technique for precisely forecasting business demand within the context of supply chain management. Leveraging a comprehensive analysis of historical business sales data, this research scrutinizes the effectiveness of GBDT in capturing intricate demand patterns and fluctuations. Through a meticulous methodology, encompassing iterative GBDT modeling, the study demonstrates the model's ability to iteratively refine predictions, resulting in enhanced accuracy in forecasting business sales. Visual representations showcasing temporal trends, volatility, and decomposition of sales data provide critical insights into demand dynamics, serving as foundational elements for improved predictive models. The comparative analysis between predicted and actual sales data highlights the predictive capabilities of the GBDT approach, offering valuable insights for optimizing supply chain financial management. While presenting promising results, ongoing research aims to further enhance GBDT's predictive power by refining algorithms and exploring additional influential factors in demand variability. This research contributes to the advancement of predictive modeling techniques within supply chain financial optimization, aiding businesses in strategic decision-making and resource allocation.
Predictive modeling , Business demand forecasting , Supply chain management , Financial optimization , Inventory management , Demand analysis strategies , Financial efficiency enhancement.
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