Utilizing predictive analytics to enhance supply chain efficiency and reduce operational costs
1 Independent Researcher, Nashville, TN, USA.
2 Christfill Global Enterprises, Lagos Nigeria.
Review
International Journal of Engineering Research Updates, 2024, 07(01), 001–021.
Article DOI: 10.53430/ijeru.2024.7.1.0029
Publication history:
Received on 11 May 2024; revised on 26 June 2024; accepted on 29 June 2024
Abstract:
This study investigates the application of predictive analytics to enhance supply chain efficiency and reduce operational costs. The primary objective is to understand how predictive analytics can be leveraged to optimize various aspects of supply chain management, including demand forecasting, inventory management, and logistics. The research methodology involved a comprehensive literature review, coupled with a case study analysis of several organizations that have successfully implemented predictive analytics in their supply chain operations. Key findings reveal that predictive analytics significantly improves demand forecasting accuracy, which in turn optimizes inventory levels, reduces stockouts and overstock situations, and enhances overall supply chain responsiveness. Additionally, predictive analytics helps in identifying potential disruptions in the supply chain, allowing for proactive measures to mitigate risks and maintain continuity. The study also highlights the cost benefits, where organizations reported a notable reduction in operational costs due to improved efficiency and better resource allocation. The conclusions drawn emphasize the transformative potential of predictive analytics in supply chain management, suggesting that its strategic implementation can lead to substantial improvements in efficiency and cost savings. This research underscores the need for organizations to invest in advanced analytics tools and skills to fully harness the benefits of predictive analytics in their supply chain operations.
Keywords:
Predictive analytics; Supply chain management (SCM); Demand forecasting; Inventory management; Risk management; Internet of Things (IoT); Blockchain; Data integration; Cloud computing; Edge computing; Data visualization; Operational efficiency; Cost reduction; Real-time analytics; Supply chain optimization
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0