A System-Dynamic Based Simulation and Bayesian Optimization for Inventory Management
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910334705467392 |
|---|---|
| author | Maitra, Sarit |
| author_facet | Maitra, Sarit |
| contents | Inventory management is a fundamental challenge in supply chain management. The challenge is compounded when the associated products have unpredictable demands. This study proposes an innovative optimization approach combining system-dynamic Monte-Carlo simulation and Bayesian optimization. The proposed algorithm is tested with a real-life, unpredictable demand dataset to find the optimal stock to meet the business objective. The findings show a considerable improvement in inventory policy. This information is helpful for supply chain analytics decision-making, which increases productivity and profitability. This study further adds sensitivity analysis, considering the variation in demand and expected output in profit percentage. This paper makes a substantial contribution by presenting a simple yet robust approach to addressing the fundamental difficulty of inventory management in a dynamic business environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A System-Dynamic Based Simulation and Bayesian Optimization for Inventory Management Maitra, Sarit Optimization and Control Computational Complexity Inventory management is a fundamental challenge in supply chain management. The challenge is compounded when the associated products have unpredictable demands. This study proposes an innovative optimization approach combining system-dynamic Monte-Carlo simulation and Bayesian optimization. The proposed algorithm is tested with a real-life, unpredictable demand dataset to find the optimal stock to meet the business objective. The findings show a considerable improvement in inventory policy. This information is helpful for supply chain analytics decision-making, which increases productivity and profitability. This study further adds sensitivity analysis, considering the variation in demand and expected output in profit percentage. This paper makes a substantial contribution by presenting a simple yet robust approach to addressing the fundamental difficulty of inventory management in a dynamic business environment. |
| title | A System-Dynamic Based Simulation and Bayesian Optimization for Inventory Management |
| topic | Optimization and Control Computational Complexity |
| url | https://arxiv.org/abs/2402.10975 |