Optimization of the Yellow Corn Cleaning Process with the Implementation of a Mechanical System

This work optimizes the yellow corn cleaning process through the implementation of a mechanical system in a company of the food sector, for which the production processes and customer requirements have been analyzed, based on the analysis of the time taking, the examination of each process with the Ishikawa diagram and through indicators in the Pareto diagram, decisions were made to improve their production times since initially there was a daily production of only 92.84 bags/day in a daily workday of 8 hours, with a minimum requirement of 400 bags/day. Finally, after evaluating the necessary requirements and the required production, a new corn cleaning machine was implemented, which was built and verified, the new production times, in which the productivity was improved by 357 %, managing to produce 424.53 bags/day in a workday of 8 hours a day, likewise it generates an improvement of 78.13% in the production process, on the other hand, the cleaning process has been reduced by 87.38%, being the activity that took the most time for the company, secondly, the times not only decreased in a specific area but in all the corresponding processes such as, in the process of receiving raw material, humidity control, verification of species, feeding to the corn well, weighing control, sack sewing and storage.

Authors

Institutions

Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/rcae62637.2024.10834183
Primary Topic
Industrial Automation and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of the Yellow Corn Cleaning Process with the Implementation of a Mechanical System

Samir Genoary Rojas Santana, José Antonio Velásquez Costa, Haru Helen Mallco Segura, Yanira Malena Chavez Esteban
Industrial Automation and Control Systems
article

Optimization of the Yellow Corn Cleaning Process with the Implementation of a Mechanical System

Samir Genoary Rojas Santana, José Antonio Velásquez Costa, Haru Helen Mallco Segura, Yanira Malena Chavez Esteban
article en

Abstract

This work optimizes the yellow corn cleaning process through the implementation of a mechanical system in a company of the food sector, for which the production processes and customer requirements have been analyzed, based on the analysis of the time taking, the examination of each process with the Ishikawa diagram and through indicators in the Pareto diagram, decisions were made to improve their production times since initially there was a daily production of only 92.84 bags/day in a daily workday of 8 hours, with a minimum requirement of 400 bags/day. Finally, after evaluating the necessary requirements and the required production, a new corn cleaning machine was implemented, which was built and verified, the new production times, in which the productivity was improved by 357 %, managing to produce 424.53 bags/day in a workday of 8 hours a day, likewise it generates an improvement of 78.13% in the production process, on the other hand, the cleaning process has been reduced by 87.38%, being the activity that took the most time for the company, secondly, the times not only decreased in a specific area but in all the corresponding processes such as, in the process of receiving raw material, humidity control, verification of species, feeding to the corn well, weighing control, sack sewing and storage.

Universidad Continental (PE)
Industry, innovation and infrastructure
Openalex Percentile: Top 69%
Industrial Automation and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.