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

International Journal of Mechanical Engineering

© 2023 by SSRG - IJME Journal

Volume 10 Issue 10

Year of Publication : 2023

Authors : Keysi Alexandra Carbajal-Vásquez, Renato Alejandro Piscoya-Alvites, Juan Carlos Quiroz-Flores, Yvan García-Lopez, S. Nallusamy

10.14445/23488360/IJME-V10I10P102

How to Cite?

Keysi Alexandra Carbajal-Vásquez, Renato Alejandro Piscoya-Alvites, Juan Carlos Quiroz-Flores, Yvan García-Lopez, S. Nallusamy, "Minimization of Smashed Products in Sustenance Industries by Lean and Machine Learning Tools," SSRG International Journal of Mechanical Engineering, vol. 10,  no. 10, pp. 12-26, 2023. Crossref, https://doi.org/10.14445/23488360/IJME-V10I10P102

Abstract:

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.

Keywords:

Lean manufacturing, Machine Learning, 5S, MRP, FEFO, Smashed products, Protein food industry.

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