Abstract

This study into how stochastic inventory modeling can be combined with resource optimization strategies to make inventory management better when supply and demand are uncertain. Traditional inventory practices often struggle to adapt to fluctuating market conditions, leading to inefficiencies and increased costs. By employing stochastic models, such as the Newsvendor model and Q, r systems, we analyze how randomness in demand and lead times can be effectively managed. Through simulations and case studies across various industries, this research evaluates the performance of these models and identifies best practices for optimizing inventory levels while minimizing holding and shortage costs. The findings highlight the significant benefits of adopting a stochastic approach, providing actionable insights for practitioners to improve service levels and overall supply chain efficiency. By bridging the gap between theoretical modeling and practical implementation, this study adds to the body of literature already in existence and opens the door for further research into sophisticated inventory management techniques. 

Author

Indarpal Singh*1 , Sanjay Kumar2 , Arvind Kumar3 , Pankaj Kumar Garg4  ( Pages 243-258 )
Email:1indarpal.singh@dcac.du.ac.in
Affiliation: 1Department of Mathematics, Delhi College of Art & Commerce, DU      DOI:

Keyword

Stochastic inventory model, optimization

References

1. Silver, E. A., Pyke, D. F., & Peterson, R.(1998). Inventory Management and Production Planning and Scheduling. Wiley. A foundational text covering various inventory management techniques, including stochastic models.
2. Nahmias, S., & Olsen, T. L.(2015). Production and Operations Analysis. Waveland Press. This book offers insights into inventory systems and stochastic modeling techniques.
3. Boute, A., & Pahl, J.(2008). Stochastic Inventory Control: Models and Methods. Springer. A comprehensive resource focusing specifically on stochastic inventory control methods.
4. Zipkin, P.(2000). "Foundations of Inventory Management." Operations Research, 48(1), 100-110. Discusses the foundational concepts of inventory management, including stochastic approaches.
5. Kelle, P., & Silver, E. A.(1989). "The Role of Safety Stock in Inventory Management: A Review." Journal of Operations Management, 8(1), 51-72. Reviews the role of safety stock in inventory systems and its importance under uncertainty.
6. Davis, T. & Nahmias, S.(1995). "Inventory Optimization in a Stochastic Environment." International Journal of Production Research, 33(8), 2031-2042. Explores optimization techniques specific to stochastic environments.
7. Kahn, S. M., & Moinzadeh, K.(2006). "Dynamic Inventory Management under Stochastic Demand." Proceedings of the INFORMS Annual Meeting. Discusses dynamic inventory management strategies in the face of uncertainty.
8. Guan, Y.(2010). "Stochastic Inventory Models with Demand Uncertainty: A Study of Safety Stock Strategies." Master's Thesis, University of California. Provides an in-depth analysis of safety stock strategies in stochastic inventory models.
9. Wagner, H. M., & Whitin, T. M.(1958). "Dynamic Inventory Control." Management Science, 5(1), 1-13. A seminal paper on dynamic inventory control that lays groundwork for stochastic approaches.

1. Gendreau, M., & Potvin, J. Y.(2010). "Metaheuristics in Combinatorial Optimization." Handbook of Metaheuristics, 2nd edition, Springer