Evaluation of the Contribution of Knowledge Management to Efficiency in the Manufacturing Industry Through Machine Learning

Juan Ibujés-Villacís

2024

Abstract

Knowledge management (KM) has been instrumental for organizations to improve their efficiency. The objective of this research is to determine the contribution of knowledge management (KM) to manufacturing industry efficiency, using machine learning models to predict the relevant KM factors that should be taken into account to improve efficiency. Given the quantitative nature of the research, in the first phase, data on variables associated with KM factors and efficiency were collected and processed. In the second phase, four supervised machine learning models were developed to predict which manufacturing companies are efficient in their production process based on a set of KM factors. The study was based on information from 142 manufacturing companies in the province of Pichincha, Ecuador. The results show that the relevant KM factors that contribute to business efficiency are policies and strategies, organizational structure, technology, incentive systems and organizational culture. This pioneering study in Ecuador allows predicting the relevant KM factors that impact the efficiency of manufacturing firms. This article contributes to the field of knowledge management and provides information on the KM factors that manufacturing firms should focus on to achieve greater efficiency.

Paper Citation

in Harvard Style

Ibujés-Villacís J. (2024). Evaluation of the Contribution of Knowledge Management to Efficiency in the Manufacturing Industry Through Machine Learning. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS; ISBN 978-989-758-716-0, SciTePress, pages 48-59. DOI: 10.5220/0012943500003838

in Bibtex Style

@conference{kmis24,
author={Juan Ibujés-Villacís},
title={Evaluation of the Contribution of Knowledge Management to Efficiency in the Manufacturing Industry Through Machine Learning},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS},
year={2024},
pages={48-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012943500003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS
TI - Evaluation of the Contribution of Knowledge Management to Efficiency in the Manufacturing Industry Through Machine Learning
SN - 978-989-758-716-0
AU - Ibujés-Villacís J.
PY - 2024
SP - 48
EP - 59
DO - 10.5220/0012943500003838
PB - SciTePress