Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Integrating Machine Learning for Supply Chain Optimization in Manufacturing and Logistics: Enhancing Retail Management and Efficiency

PDF
  • Dr. Luis García
Dr. Luis García
Associate Professor of Industrial Engineering, Monterrey Institute of Technology and Higher Education (ITESM), Mexico
Cover

Published 19-09-2024

Keywords

  • Supply Chain Optimization,
  • Manufacturing,
  • Logistics,
  • Retail Management
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Luis García, “Integrating Machine Learning for Supply Chain Optimization in Manufacturing and Logistics: Enhancing Retail Management and Efficiency”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 123–136, Sep. 2024, Accessed: Nov. 28, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/153

Abstract

Machine learning (ML) has emerged as a powerful tool for enhancing efficiency in manufacturing and logistics by optimizing supply chain processes. Forecasting plays a crucial role in retail supply chain management, and the application of AI/ML models, such as Cognitive Demand Forecasting and Demand Integrated Product Flow, has become increasingly prevalent in addressing this challenge [1]. The use of reinforcement learning (RL) algorithms in supply chain forecasting has gained traction, with companies like UPS and Amazon leveraging RL to improve forecast accuracy and meet rising consumer delivery expectations. The OpenAI Gym toolkit has become a preferred choice for building RL algorithms for supply chain use cases, enabling the development of suitable RL models for supply chain optimization challenges.

PDF

Downloads

Download data is not yet available.

References

  1. Pelluru, Karthik. "Integrate security practices and compliance requirements into DevOps processes." MZ Computing Journal 2.2 (2021): 1-19.
  2. Nimmagadda, Venkata Siva Prakash. "AI-Powered Risk Management and Mitigation Strategies in Finance: Advanced Models, Techniques, and Real-World Applications." Journal of Science & Technology 1.1 (2020): 338-383.
  3. Machireddy, Jeshwanth Reddy, and Harini Devapatla. "Leveraging Robotic Process Automation (RPA) with AI and Machine Learning for Scalable Data Science Workflows in Cloud-Based Data Warehousing Environments." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 234-261.
  4. Potla, Ravi Teja. "Integrating AI and IoT with Salesforce: A Framework for Digital Transformation in the Manufacturing Industry." Journal of Science & Technology 4.1 (2023): 125-135.
  5. Singh, Puneet. "Streamlining Telecom Customer Support with AI-Enhanced IVR and Chat." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 443-479.
  6. Sreerama, Jeevan, Mahendher Govindasingh Krishnasingh, and Venkatesha Prabhu Rambabu. "Machine Learning for Fraud Detection in Insurance and Retail: Integration Strategies and Implementation." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 205-260.
  7. Rambabu, Venkatesha Prabhu, Munivel Devan, and Chandan Jnana Murthy. "Real-Time Data Integration in Retail: Improving Supply Chain and Customer Experience." Journal of Computational Intelligence and Robotics 3.1 (2023): 85-122.
  8. Althati, Chandrashekar, Venkatesha Prabhu Rambabu, and Munivel Devan. "Big Data Integration in the Insurance Industry: Enhancing Underwriting and Fraud Detection." Journal of Computational Intelligence and Robotics 3.1 (2023): 123-162.
  9. Krothapalli, Bhavani, Lavanya Shanmugam, and Jim Todd Sunder Singh. "Streamlining Operations: A Comparative Analysis of Enterprise Integration Strategies in the Insurance and Retail Industries." Journal of Science & Technology 2.3 (2021): 93-144.
  10. Amsa Selvaraj, Priya Ranjan Parida, and Chandan Jnana Murthy, “AI/ML-Based Entity Recognition from Images for Parsing Information from US Driver’s Licenses and Paychecks”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 475–515, May 2023
  11. Deepak Venkatachalam, Pradeep Manivannan, and Jim Todd Sunder Singh, “Enhancing Retail Customer Experience through MarTech Solutions: A Case Study of Nordstrom”, J. Sci. Tech., vol. 3, no. 5, pp. 12–47, Sep. 2022
  12. Pradeep Manivannan, Deepak Venkatachalam, and Priya Ranjan Parida, “Building and Maintaining Robust Data Architectures for Effective Data-Driven Marketing Campaigns and Personalization”, Australian Journal of Machine Learning Research & Applications, vol. 1, no. 2, pp. 168–208, Dec. 2021
  13. Praveen Sivathapandi, Priya Ranjan Parida, and Chandan Jnana Murthy. “Transforming Automotive Telematics With AI/ML: Data Analysis, Predictive Maintenance, and Enhanced Vehicle Performance”. Journal of Science & Technology, vol. 4, no. 4, Aug. 2023, pp. 85-127
  14. Priya Ranjan Parida, Jim Todd Sunder Singh, and Amsa Selvaraj, “Real-Time Automated Anomaly Detection in Microservices Using Advanced AI/ML Techniques”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 514–545, Apr. 2023
  15. Sharmila Ramasundaram Sudharsanam, Pradeep Manivannan, and Deepak Venkatachalam. “Strategic Analysis of High Conversion Ratios from Marketing Qualified Leads to Sales Qualified Leads in B2B Campaigns: A Case Study on High MQL-to-SQL Ratios”. Journal of Science & Technology, vol. 2, no. 2, Apr. 2021, pp. 231-269
  16. Jasrotia, Manojdeep Singh. "Unlocking Efficiency: A Comprehensive Approach to Lean In-Plant Logistics." International Journal of Science and Research (IJSR) 13.3 (2024): 1579-1587.
  17. Gayam, Swaroop Reddy. "AI-Driven Customer Support in E-Commerce: Advanced Techniques for Chatbots, Virtual Assistants, and Sentiment Analysis." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 92-123.
  18. Nimmagadda, Venkata Siva Prakash. "AI-Powered Predictive Analytics for Retail Supply Chain Risk Management: Advanced Techniques, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 152-194.
  19. Putha, Sudharshan. "AI-Driven Energy Management in Manufacturing: Optimizing Energy Consumption and Reducing Operational Costs." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 313-353.
  20. Sahu, Mohit Kumar. "Machine Learning for Anti-Money Laundering (AML) in Banking: Advanced Techniques, Models, and Real-World Case Studies." Journal of Science & Technology 1.1 (2020): 384-424.
  21. Kasaraneni, Bhavani Prasad. "Advanced Artificial Intelligence Techniques for Predictive Analytics in Life Insurance: Enhancing Risk Assessment and Pricing Accuracy." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 547-588.
  22. Kondapaka, Krishna Kanth. "Advanced AI Techniques for Optimizing Claims Management in Insurance: Models, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 637-668.
  23. Kasaraneni, Ramana Kumar. "AI-Enhanced Cybersecurity in Smart Manufacturing: Protecting Industrial Control Systems from Cyber Threats." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 747-784.
  24. Pattyam, Sandeep Pushyamitra. "AI in Data Science for Healthcare: Advanced Techniques for Disease Prediction, Treatment Optimization, and Patient Management." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 417-455.
  25. Kuna, Siva Sarana. "AI-Powered Solutions for Automated Customer Support in Life Insurance: Techniques, Tools, and Real-World Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 529-560.
  26. Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.
  27. Gayam, Swaroop Reddy. "AI-Driven Fraud Detection in E-Commerce: Advanced Techniques for Anomaly Detection, Transaction Monitoring, and Risk Mitigation." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 124-151.
  28. Nimmagadda, Venkata Siva Prakash. "AI-Powered Risk Assessment Models in Property and Casualty Insurance: Techniques, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 194-226.
  29. Putha, Sudharshan. "AI-Driven Metabolomics: Uncovering Metabolic Pathways and Biomarkers for Disease Diagnosis and Treatment." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 354-391.
  30. Sahu, Mohit Kumar. "AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management." Journal of Science & Technology 1.1 (2020): 424-464.
  31. Kasaraneni, Bhavani Prasad. "Advanced Machine Learning Algorithms for Loss Prediction in Property Insurance: Techniques and Real-World Applications." Journal of Science & Technology 1.1 (2020): 553-597.
  32. Kondapaka, Krishna Kanth. "Advanced AI Techniques for Retail Supply Chain Sustainability: Models, Applications, and Real-World Case Studies." Journal of Science & Technology 1.1 (2020): 636-669.
  33. Kasaraneni, Ramana Kumar. "AI-Enhanced Energy Management Systems for Electric Vehicles: Optimizing Battery Performance and Longevity." Journal of Science & Technology 1.1 (2020): 670-708.
  34. Pattyam, Sandeep Pushyamitra. "AI in Data Science for Predictive Analytics: Techniques for Model Development, Validation, and Deployment." Journal of Science & Technology 1.1 (2020): 511-552.
  35. Kuna, Siva Sarana. "AI-Powered Solutions for Automated Underwriting in Auto Insurance: Techniques, Tools, and Best Practices." Journal of Science & Technology 1.1 (2020): 597-636.
  36. Selvaraj, Akila, Mahadu Vinayak Kurkute, and Gunaseelan Namperumal. "Agile Project Management in Mergers and Acquisitions: Accelerating Enterprise Integration in Large Organizations." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 295-334.
  37. Selvaraj, Amsa, Praveen Sivathapandi, and Gunaseelan Namperumal. "Privacy-Preserving Synthetic Data Generation in Financial Services: Implementing Differential Privacy in AI-Driven Data Synthesis for Regulatory Compliance." Journal of Artificial Intelligence Research 2.1 (2022): 203-247.
  38. Paul, Debasish, Sharmila Ramasundaram Sudharsanam, and Yeswanth Surampudi. "Implementing Continuous Integration and Continuous Deployment Pipelines in Hybrid Cloud Environments: Challenges and Solutions." Journal of Science & Technology 2.1 (2021): 275-318.
  39. Venkatachalam, Deepak, Debasish Paul, and Akila Selvaraj. "AI/ML Powered Predictive Analytics in Cloud Based Enterprise Systems: A Framework for Scalable Data-Driven Decision Making." Journal of Artificial Intelligence Research 2.2 (2022): 142-183.
  40. Namperumal, Gunaseelan, Chandan Jnana Murthy, and Sharmila Ramasundaram Sudharsanam. "Integrating Artificial Intelligence with Cloud-Based Human Capital Management Solutions: Enhancing Workforce Analytics and Decision-Making." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 456-502.
  41. Kurkute, Mahadu Vinayak, Akila Selvaraj, and Amsa Selvaraj. "End-to-End Cybersecurity Strategies for Autonomous Vehicles: Leveraging Multi-Layered Defence Mechanisms to Safeguard Automotive Ecosystems." Cybersecurity and Network Defense Research 3.2 (2023): 134-177.
  42. Soundarapandiyan, Rajalakshmi, Sharmila Ramasundaram Sudharsanam, and Debasish Paul. "Integrating Kubernetes with CI/CD Pipelines in Cloud Computing for Enterprise Applications." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 161-200.
  43. Sivathapandi, Praveen, Debasish Paul, and Sharmila Ramasundaram Sudharsanam. "Enhancing Cloud-Native CI/CD Pipelines with AI-Driven Automation and Predictive Analytics." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 226-265.
  44. Sudharsanam, Sharmila Ramasundaram, Gunaseelan Namperumal, and Priya Ranjan Parida. "Risk Management in Large-Scale Mergers and Acquisitions: Project Management Techniques for Ensuring Enterprise Integration Success." Journal of Science & Technology 3.1 (2022): 79-116.

Similar Articles

You may also start an advanced similarity search for this article.