Optimizing Grocery Quality and Supply Chain Efficiency Using AI-Driven Predictive Logistics

• Author(s): Areeba Farooq ; Anate Benoit Nicaise Abbey ; Ekene Cynthia Onukwulu
• Paper ID: 1704902
• Page: 661-668
• Published Date: 31-07-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 1 July-2023

Abstract

The grocery supply chain is a complex network that demands precision in managing product quality, minimizing waste, and ensuring efficient logistics. This paper explores the transformative potential of AI-driven predictive logistics in optimizing grocery supply chains, focusing on its ability to enhance forecasting accuracy, reduce waste, and ensure product freshness. It examines the role of predictive analytics in addressing supply chain challenges and highlights AI’s contributions to improving quality monitoring and real-time tracking. The paper also identifies barriers to implementation, such as data availability, integration complexities, and cost, while presenting actionable strategies for stakeholders to overcome these challenges. By integrating advanced technologies and fostering collaboration, grocery supply chains can achieve unprecedented efficiency, sustainability, and customer satisfaction.

Keywords

Predictive logistics, Grocery supply chain, Artificial intelligence, Product quality optimization, Real-time tracking, Supply chain efficiency

Citations

IRE Journals:
Areeba Farooq , Anate Benoit Nicaise Abbey , Ekene Cynthia Onukwulu "Optimizing Grocery Quality and Supply Chain Efficiency Using AI-Driven Predictive Logistics" Iconic Research And Engineering Journals Volume 7 Issue 1 2023 Page 661-668

IEEE:
Areeba Farooq , Anate Benoit Nicaise Abbey , Ekene Cynthia Onukwulu "Optimizing Grocery Quality and Supply Chain Efficiency Using AI-Driven Predictive Logistics" Iconic Research And Engineering Journals, 7(1)