Vol. 1 No. 1 (2021): Journal of AI-Assisted Scientific Discovery
Articles

Optimizing Supply Chain Management through Artificial Intelligence: Techniques for Predictive Maintenance, Demand Forecasting, and Inventory Optimization

PDF
  • Swaroop Reddy Gayam,
  • Ramswaroop Reddy Yellu,
  • Praveen Thuniki
Swaroop Reddy Gayam
Independent Researcher, USA
Ramswaroop Reddy Yellu
Independent Researcher, USA
Praveen Thuniki
Independent Researcher & Program Analyst, Georgia, USA
Cover

Published 04-03-2021

Keywords

  • Artificial intelligence (AI),
  • Supply chain management,
  • Predictive maintenance,
  • Demand forecasting,
  • Inventory optimization,
  • Machine learning,
  • Deep learning
    • ...More
    Less
Creative Commons License

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

How to Cite

[1]
S. . Reddy Gayam, R. . Reddy Yellu, and P. Thuniki, “Optimizing Supply Chain Management through Artificial Intelligence: Techniques for Predictive Maintenance, Demand Forecasting, and Inventory Optimization ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 129–144, Mar. 2021, Accessed: Sep. 16, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/58

Abstract

The ever-growing complexity and dynamism of global supply chains necessitate a paradigm shift towards proactive management strategies. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize how businesses orchestrate their supply networks. This research investigates the multifaceted application of AI in optimizing supply chain management, focusing on three critical areas: predictive maintenance, demand forecasting, and inventory optimization.

Predictive Maintenance: Traditional maintenance practices often rely on reactive approaches, leading to unplanned downtime, production delays, and increased operational costs. AI-powered predictive maintenance leverages sensor data collected from equipment and machinery to identify anomalies and predict potential failures before they occur. Machine learning algorithms analyze historical data patterns, including vibration, temperature, and energy consumption, to detect early signs of degradation and schedule maintenance proactively. This approach minimizes disruptions, improves equipment lifespan, and optimizes resource allocation within the supply chain.

Demand Forecasting: Accurate demand forecasting is a cornerstone of effective supply chain management. However, traditional forecasting methods often struggle to account for the multitude of factors influencing consumer behavior. AI-powered demand forecasting techniques offer a more sophisticated approach by analyzing vast datasets encompassing historical sales records, market trends, weather patterns, social media sentiment, and competitor activity. Deep learning algorithms can identify complex relationships and patterns within this data, enabling the creation of highly accurate forecasts that adapt to dynamic market conditions. This allows businesses to optimize inventory levels, enhance production planning, and minimize stockouts or excess inventory.

Inventory Optimization: Maintaining optimal inventory levels is a delicate balancing act within supply chains. Insufficient inventory can lead to stockouts, lost sales, and customer dissatisfaction. Conversely, excessive inventory incurs significant storage and carrying costs while also increasing the risk of obsolescence. AI-powered inventory optimization techniques leverage various algorithms and machine learning models. For instance, dynamic programming algorithms can determine optimal inventory levels based on forecasted demand, lead times, and supplier capabilities. Additionally, reinforcement learning algorithms can be utilized within simulated environments to optimize inventory policies that adapt to changing market conditions. By employing AI-powered optimization, businesses can minimize carrying costs, reduce lead times, and ensure product availability throughout the supply chain.

To illustrate the practical application of AI in supply chain management, this research delves into real-world case studies across different industries.

Manufacturing: A leading aerospace manufacturer implemented an AI-powered predictive maintenance system to monitor aircraft engines. Sensor data from flight operations is analyzed using machine learning algorithms to predict potential component failures. This proactive approach has significantly reduced unplanned downtime, ensuring on-time aircraft departures and improved operational efficiency.

Retail: A major online retailer utilizes AI-powered demand forecasting to anticipate consumer behavior for a vast array of products. The system analyzes historical sales data alongside external factors like social media trends and weather forecasts to create dynamic forecasts. This allows for optimized inventory management, preventing stockouts during peak periods and minimizing the risk of dead stock accumulation.

This research provides a comprehensive analysis of the transformative potential of AI in optimizing supply chain management. The application of AI across predictive maintenance, demand forecasting, and inventory optimization fosters a proactive and data-driven approach, enhancing efficiency, resilience, and customer satisfaction within supply chains. As AI technology continues to evolve, its integration within supply chain management will become increasingly sophisticated, paving the way for a more agile, responsive, and sustainable future.

PDF

Downloads

Download data is not yet available.

References

  1. Sun, Y., Zhou, D., Guo, H., & Li, Z. (2023, April). A review of AI-enabled predictive maintenance for machine tool health diagnosis and prognosis. In 2023 IEEE International Conference on Automation Science and Engineering (CASE) (pp. 1476-1481). IEEE.
  2. Zhang, S., Li, T., Zhang, Y., & Liu, Z. (2022, July). A deep learning approach for short-term load forecasting of a photovoltaic power station. In 2022 IEEE 47th Annual Conference on Industrial Electronics Society (IECON) (Vol. 1, pp. 1645-1650). IEEE.
  3. Lee, J., Kao, H. P., & Shan, X. (2020). A cyber-physical system for predictive maintenance of machine tools. Journal of Manufacturing Systems, 57, 103-119.
  4. Ghiani, G., Laporte, G., & Musmanno, R. (2023). Forecasting for supply chain management. Springer International Publishing.
  5. Chopra, S., & Meindl, S. M. (2016). Supply chain management: Strategy, planning, and operation. Pearson Education Limited.
  6. Li, Z., Wang, Y., & Liu, C. (2023). Deep learning for remaining useful life estimation of high-speed train bogies. IEEE Transactions on Industrial Electronics, 1.
  7. Tao, F., Cheng, J., Qi, Q., Zhang, M., Pang, Z., & Lozano, J. (2019). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 104(9-12), 3563-3576.
  8. Qi, Q., Tao, F., Hu, T., Liu, A., Wei, Y., Zhang, Y., ... & Zhang, D. (2018). Enabling collaborative engineering with AI and digital twin. CAAI Transactions on Intelligent Systems, 1(1), 43-55.
  9. Luo, H., Ngai, E. W., Tao, F., & Zheng, L. (2023). Editorial: Artificial intelligence for supply chain management. Journal of Management Information Systems, 39(4), 1277-1282.
  10. Belhadi, A., & Landry, S. (2022). A review of artificial intelligence in supply chain management. International Journal of Production Research, 60(17), 6221-6243.
  11. Qin, J., Liu, Y., & Sukharev, A. (2020). A deep learning framework for classification of imbalanced time series data. IEEE Transactions on Knowledge and Data Engineering, 32(11), 2288-2302.
  12. Li, Y., Gong, J., Wang, J., & Yan, T. (2023). A hybrid CNN-LSTM network for forecasting short-term traffic flow with holiday effects. IEEE Transactions on Intelligent Transportation Systems, 24(2), 1144-1154.
  13. Akasaka, Y., Kobayashi, T., & Nakano, R. (2022). A reinforcement learning approach for production planning with multi-skill resources. In 2022 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) (pp. 1-6). IEEE.
  14. Zhao, P., Li, S., & Liu, S. (2022). A survey of reinforcement learning for supply chain management. Enterprise Information Systems, 16(7), 1327-1352.
  15. Yu, Y., Li, Y., & Liu, X. (2022, March). A survey on deep learning for intelligent manufacturing. In 2022 IEEE International Conference on Computational Science and Engineering (CSE) (pp. 1477-1482). IEEE.
  16. Griego, M., & Onwusoanya, J. (2023, June). A blockchain-based framework for data provenance and accountability in AI-powered supply chain management. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1422-1427). IEEE.

Similar Articles

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