Predictive analytics on artificial intelligence in supply chain optimization

Anber Abraheem Shlash Mohammad, Iyad A.A Khanfar, Badrea Al Oraini, Asokan Vasudevan, Ibrahim Mohammad Suleiman and Zhou Fei

Data and Metadata, 2024, vol. 3, 395

Abstract: AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced response time, enhanced logistics route optimization, improved demand planning as well as real-time inventory control. Thus, the idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics. SCO-AI implementation has seen significant improvements in inventory turnover rate, rates of on-time delivery as well as overall supply chain costs. In this period of high business turbulence, such kind of research builds up the robustness of a given supply chain while at the same time minimizing operational risks by means of simulations and case studies

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:395:id:1056294dm2024395

DOI: 10.56294/dm2024395

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