Abstract
Machine learning algorithms are implemented in predictive logistics management to optimize the supply chain for vehicle sales. These algorithms are capable of predicting future demand, managing inventory levels, and optimizing distribution procedures by analyzing historical data, prevailing market patterns, and additional external factors. Organizations may enhance customer satisfaction, minimize lead times, and maintain appropriate inventory levels by implementing predictive models. This approach not only improves the overall efficacy of the supply chain, but it also decreases the costs associated with inventory shortages and excess inventory. The integration of machine learning (ML) into logistics management (LM) offers a data-driven solution to the complex and ever-changing supply chain scenarios.
| Original language | English |
|---|---|
| Title of host publication | 2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350364699 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024 - Lucknow, India Duration: 15 Nov 2024 → 16 Nov 2024 |
Publication series
| Name | 2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024 |
|---|
Conference
| Conference | 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024 |
|---|---|
| Country/Territory | India |
| City | Lucknow |
| Period | 15/11/24 → 16/11/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Car Sales and Machine Learning Algorithms
- Demand Forecasting
- Inventory Management
- Predictive Logistics Management
- Supply Chain Optimization
- and Distribution Processes
ASJC Scopus subject areas
- Computer Science Applications
- Software
- Modeling and Simulation