• Dr.Sivadi BalaKrishna
• Ms. Ganga Bhavani
• K. Priya Darshini
• N. Sindhu
• G. Triveni , Ch. Keerthi

One of the fastest-growing sectors is logistic consignment industries, through which rapid advancements take place in machine learning and provide a lot of opportunities to bring on board cost optimization and efficiency. This paper makes an insight into cost savvy logistics using machine learning techniques. The predictive analytics and advanced algorithm that enable machine are exploited in this paper to bring ease to a variety of logistic process, beginning from demand forecasting to route optimization to inventory management. These results indicate that big cost savings can be made through better accuracy in demand prediction, route optimization of transportation, and efficient resource allocation. Real-time data processing allows for dynamic decision-making, which further raises the efficiency of operations. Case studies and empirical data show applications and benefits of machine learning in reducing logistics cost while maintaining high service levels. It is within places that this study underlines the potential of machine learning to eventually revolutionize logistics and turn into an important tool for inexpensive and efficient logistics operations.

Logistics consignment industry, Machine learning, Cost optimization, Predictive analytics, Demand forecasting, Route optimization, Inventory management, Real-time data processing, Dynamic decision-making, Resource allocation

"ANALYSIS ON COST SAVVY LOGISTICS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.153-160, October-2024, Available :http://www.jetir.org/papers/JETIRGN06018.pdf

"ANALYSIS ON COST SAVVY LOGISTICS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp153-160, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06018.pdf