Minimization of Smashed Products in Sustenance Industries by Lean and Machine Learning Tools

• Autores: Carbajal Vásquez, Keysi Alejandra, Piscoya Alvites, Renato Alejandro, Quiroz Flores, Juan Carlos, García López, Yván Jesús, Nallusamy, S.
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/19463
• Enlace del recurso: https://hdl.handle.net/20.500.12724/19463; https://doi.org/10.14445/23488360/IJME-V10I10P102
• Nivel de acceso: acceso abierto
• Materia: Lean manufacturing; Efficient production; Waste minimization; Organizational effectiveness; Producción eficiente; Minimización de residuos; Eficacia organizacional; https://purl.org/pe-repo/ocde/ford#2.11.04

This study focuses on developing a solution to one of the main problems in the food sector, product deterioration, often due to poor inventory management, low turnover, and lack of shelf-life control, among other causes. Therefore, this study is based on the design of a lean inventory management model proposed to reduce the number of deteriorated products in an egg product company in Peru, based on the analysis of the problem within the company and the study of previous research. As a result, the proposed method uses the tools of Machine Learning, Material Requirement Planning (MRP), 5S, and First Extended First Out (FEFO), reducing the main problem by 65.57% and the demand forecast error by 47.21%, thus reducing one of the leading root causes of the main problem. Thanks to this improvement, this research can contribute knowledge so that other companies with similar issues can implement the model and improve their results.