[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119090-en":3,"doc-seo-119090-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119090,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","IMPROVING DEMAND FORECASTING BY IMPLEMENTING MACHINE LEARNING IN POULTRY PRODUCTION COMPANY - Tesis","Demand forecasting in poultry production faces high error rates when handled through manual procedures for sales planning, leading to recurring monthly monetary losses. This research applies machine learning using the company’s real sales database and a time series approach to predict the last quarter of 2022. Model outputs are compared against actual sales, achieving strong performance with forecast bias and forecast accuracy that reduce the error level previously managed by the company.","Universidad de Lima Facultad de Ingeniería Carrera de Ingeniería Industrial  \nIMPROVING DEMAND FORECASTING BY IMPLEMENTING MACHINE LEARNING IN POULTRY PRODUCTION COMPANY  \nTesis para optar el Título Profesional de Ingeniero Industrial  \nJoaquin Antonio Garcia Arismendiz Código 20180760  \nSandra Larissa Huertas Zuñiga  \nCódigo 20173484  \nAsesor  \nCarlos Augusto Lizárraga Portugal  \nLima – Perú  \n\n| Propuesta\u003Cbr>Carrera Ingeniería Industrial |\n| --- |\n| Título\u003Cbr>IMPROVING DEMAND FORECASTING BY IMPLEMENTING MACHINE LEARNING IN POULTRY PRODUCTION\u003Cbr>COMPANY |\n| Autores\u003Cbr>García Arismendiz Joaquin Antonio, Huertas Zuñiga Sandra Larissa [20180760@aloe.ulima.edu.pe](20180760@aloe.ulima.edu.pe), [20173484@aloe.ulima.edu.pe](20173484@aloe.ulima.edu.pe)[ ](20173484@aloe.ulima.edu.pe)Universidad de Lima |\n| Resumen: Debido al alto porcentaje de error presente en el pronóstico de la demanda de una empresade producción avícola; lo que desencadena una pérdida monetaria mensual provocada por el método manual que utiliza la empresa para el pronóstico del plan de ventas, es por ello que se buscará reducireste porcentaje de error mediante el uso de la herramienta Machine Learning, a través de la cual se utilizará la base de datos de ventas proporcionada por la empresa que se utilizó para entrenar yutilizando el método de series de tiempo, se podrá predecir el último trimestre del 2022. Finalmente, se compararon los resultados obtenidos por el modelo de Machine Learning con la venta real de la empresa y su pronóstico. Se logro como resultado un FB de 2 ,44% y un FA de 97,56%, reduciendo así el error que manejaba la empresa.\u003Cbr>Palabras Clave: Aprendizaje automático, Pronostico de la demanda, Empresa Avícola, Sesgo de Pronóstico, Precisión de los Pronósticos.\u003Cbr>Abstract: The use of manual methods to forecast demand in perishable food companies is generally subject to the variability of internal and external factors in the company, causing excess inventories and significant monetary losses, so it is relevant to carry out this research with the objective of to demonstrate that by implementing Machine Learning it is possible to improve the accuracy of the demand forecast. A case study in a company in the poultry sector in Peru, forecasting the last quarter of 2022, based on a real sales database and applying the time series method, comparing the results of the Machine Learning model, and obtaining as a result in a model with high Forecast Accuracy (FA) of 97.56% and a high Forecast Bias (FB) of 2.44% . The research is an important contribution to knowledge, demonstrating that Machine Learning is an ideal tool to project the demand for perishable food products, ideal for its application in various fields, such as loss reduction control, preventive maintenance of machines and control of supplies such as water and energy, among others.\u003Cbr>Keywords: Machine learning, Demand forecasting, Poultry company, forecast bias, Forecast accuracy. |\n\n\n| Línea de investigación IDIC – ULIMA\u003Cbr>Operations Research & Analysis |\n| --- |\n| Área y Sub-áreas de Investigación:\u003Cbr>Diseño y desarrollo de modelos para el análisis y predicción de las variables de un proceso. Desarrollo Empresarial |\n| Objetivo (s) de Desarrollo Sostenible (ODS)\u003Cbr>relacionado (s) al tema de investigación.\u003Cbr>ODS 9 – Industria, Innovación e Infraestructura |\n\nPLANTEAMIENTO DEL PROBLEMA  \nLa predicción de la demanda de alimentos es uno de los temas críticos para las empresas y el desarrollo sostenible Lutosławski et al. 2017. Moreno et al., 2019 mencionaron que en la industriade alimentos frescos, incluyendo los refrigeradores, la vida útil corta, la necesidad de mantener la calidad en los procesos de almacenamiento y distribución hacen que la precisión del pronóstico en el plan de ventas sea un factor importante en la planificación de la producción, minimizando las pérdidas de ventas por falta de productos, reduciendo devoluciones por la proximidad de la fecha devencimiento, y mejorando tamb","cbCaihs0GIjnadHD","https://ap.wps.com/l/cbCaihs0GIjnadHD","pdf",408626,1,13,"English","en",105,"# Propuesta de investigación\n## Resumen y palabras clave\n# Planteamiento del problema\n## Importancia de la precisión del pronóstico\n## Causas de errores en la predicción\n# Objetivos\n## Objetivo general\n## Objetivos específicos","[{\"question\":\"Cuál es el problema principal que aborda la investigación?\",\"answer\":\"El pronóstico manual de la demanda presenta un alto porcentaje de error en la empresa avícola, generando pérdidas monetarias mensuales en el plan de ventas.\"},{\"question\":\"Qué enfoque usa el estudio para mejorar el pronóstico?\",\"answer\":\"Se implementa Machine Learning con una base de datos real de ventas y un método de series de tiempo para predecir el último trimestre de 2022.\"},{\"question\":\"Cómo se validan los resultados del modelo?\",\"answer\":\"Los resultados del modelo se comparan con las ventas reales de la empresa y su pronóstico, reportándose mejoras cuantificadas mediante métricas de error como FB y FA.\"}]","IMPROVING DEMAND FORECASTING BY IMPLEMENTING MACHINE LEARNING IN POULTRY PRODUCTION COMPANY - 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