Veuillez utiliser cette adresse pour citer ce document : https://hdl.handle.net/20.500.12394/17078
Titre: Optimization of the Warehouse Logistics System, through the Application of Lean Warehouse and Machine Learning Algorithms
Autre(s) titre(s): Optimización del Sistema logístico de almacenes, mediante la aplicación de algoritmos Lean Warehouse y Machine Learning
Auteur(s): Ponce Alcocer, Andrea Isabel
Orcon Gomez, Diego Kensey
Gonzalo Lujan, Karla Veronica
Vilchez Baca, Herbert Antonio
metadata.dc.contributor.advisor: Vílchez Baca, Herbert Antonio
Mots-clés: Almacén Lean
Lean warehouse
Aprendizaje automático
Machine learning
Optimización
Optimization
Algoritmos
Algorithms
Editeur: Universidad Continental
Date de publication: 2025
metadata.dc.date.available: 25-avr-2025
Référence bibliographique: Ponce, A., Orcon, D., Gonzalo, K., & Vilchez, H. (2025). Optimization of the Warehouse Logistics System, through the Application of Lean Warehouse and Machine Learning Algorithms [Tesis de licenciatura, Universidad Continental]. Repositorio Institucional Continental. https://repositorio.continental.edu.pe/handle/20.500.12394/17078
metadata.dc.identifier.doi: https://doi.org/10.1007/978-3-031-70981-4_57
Résumé: Abstract. In 2022, the alcoholic beverage market in Peru experienced Growth due to the reduction of COVID-19 pandemic restrictions is pro- jected to reach pre-pandemic demand levels by 2026. In this context, logistics efficiency becomes crucial for profitability and customer satis- faction. This study proposes the combination of Lean Warehouse and Machine Learning algorithms to optimize the warehouse logistics sys- tem, it covers the preliminary analysis, implementation, and analysis of results, various Lean Warehouse techniques were applied, such as 5S, SLP, FEFO, and multicriteria ABC analysis, at the same time, Ma- chine Learning algorithms were applied, such as forecasting (SARIMA- LSTM), which allowed accurate forecasts of future demand and favored the distribution of the warehouse, as well as clustering (K-means) for the optimal grouping of products according to their expiration date. Key Performance Indicators (KPIs) were also introduced to gauge the success and efficiency of the logistics system. The results of the research showed substantial improvements in logistics efficiency, such as a reduc- tion in processing time per order guide by 103 minutes and an increase in process flow by 32.56%. These improvements benefited the company in terms of costs and efficiency, with a reduction in lost sales of S/34,386.75. Organizational adaptation and continuous management are essential to maintain and improve results over time.
metadata.dc.relation: https://link.springer.com/chapter/10.1007/978-3-031-70981-4_57
Extension: 3 páginas
metadata.dc.rights.accessRights: Acceso restringido
metadata.dc.source: Universidad Continental
Repositorio Institucional - Continental
Collection(s) :Tesis

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