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Inventory control using (Q, R) policy with constant lead time and backlog |
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| รหัสดีโอไอ | |
| Title | Inventory control using (Q, R) policy with constant lead time and backlog |
| Creator | Tanapat Suntornsirikul |
| Contributor | Jirachai Buddhakulsomsiri, Advisor |
| Publisher | Thammasat University |
| Publication Year | 2568 |
| Keyword | Inventory management, Continuously stocked, Constant lead time, Daily demand, Ordering cost, Holding cost, Backlog cost, Economic order quantity, Reorder point, Periodic review policy |
| Abstract | This study focuses on inventory optimization using the (????, ????) policy in a continuously stocked system with constant lead time and backlog. The objective is to determine the optimal order quantity (????) and reorder point (????) that minimize total annual cost, which consists of ordering cost, holding cost, and backlog cost. Effective inventory management requires balancing these cost components while maintaining product availability and service performance.A quantitative analytical approach was applied using historical demand data from 2022–2023. The study first analyzed daily demand to estimate demand during lead time and its probability distribution. Initial values of economic order quantity and reorder point were derived using analytical formulations. To enhance practical applicability, a spreadsheet-based simulation model was developed to evaluate system behavior under different parameter combinations. A grid search technique was then employed to identify the optimal (????, ????) policy that minimizes total annual cost.The results show that the optimized policy significantly reduces total cost by lowering backlog occurrences, although holding cost slightly increases due to higher inventory levels. Overall, the reduction in backlog cost leads to improved cost efficiency.This study demonstrates that integrating analytical methods with simulation provides a practical decision-support tool for inventory management under periodic review policy, enabling more effective and data-driven operational decisions. |
