Integrated Methods in Replenishment Planning Processes
[EN] Small and medium-sized enterprises (SMEs) face significant chal-lenges in digitalizing decision-making processes, particularly in demand forecast-ing, replenishment, and production planning. This study investigates the appli-cation of integrated methods in replenishment planning. A set of key h...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::15e2f5b2f7b384c1125001feb8a71763 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/235250 |
| Access Level: | acceso embargado |
| Palabra clave: | Hybrid approach Integrated methods Replenishment planning 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación |
| Sumario: | [EN] Small and medium-sized enterprises (SMEs) face significant chal-lenges in digitalizing decision-making processes, particularly in demand forecast-ing, replenishment, and production planning. This study investigates the appli-cation of integrated methods in replenishment planning. A set of key hybrid approaches employed in replenishment planning, including deep reinforcement learning, hybrid machine learning forecasting models, and optimization model have been identified. The findings indicate that integrating data-driven models with knowledge-based systems enhances decision-making, optimizes inventory levels, and mitigates procurement risks. Despite the benefits of hybrid approaches, fur-ther research is needed to expand integrated replenishment models, particularly for industries with complex supply chains. Future developments should focus on combining rule-based models with AI-driven predictive analytics to improve replenishment efficiency and adaptability in dynamic markets. |
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