Cost optimization in supply chain management using ai techniques vs traditional methods: a case study of fresh basket logistics pvt. Ltd

Author(s)	Mr. KETHA SASIKANTH, Ms. PUSARLA RAJESWARI
Country	India
Abstract	This project investigates cost optimization in supply chain management (SCM) by comparing the effectiveness of artificial intelligence (AI) techniques with traditional approaches. Focusing on a case study of Fresh-basket Logistics Pvt. Ltd., an Indian enterprise, the research examines how AI-driven tools enhance operational efficiency. Key areas analyzed include demand forecasting, inventory optimization, and logistics routing, all of which play a critical role in reducing costs and improving service levels within the supply chain framework. The study utilizes real-life datasets and implements code-based simulations to measure the performance of AI applications against conventional methods. Metrics such as accuracy, lead time reduction, and cost savings are used to evaluate results. Findings demonstrate that AI models consistently outperform traditional techniques, leading to smarter decision-making and optimized resource allocation. This work highlights the transformative potential of AI in modern SCM and provides practical insights for businesses seeking to enhance their logistical and inventory strategies
Keywords	Artificial Intelligence, Supply Chain Management, Cost Optimization, Inventory Control, Logistics
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-18
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58222

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