Measuring the production performance indicators for metal-mechanic industry: an LDA modeling approach
International Journal of Productivity and Performance Management
ISSN: 1741-0401
Article publication date: 10 June 2024
Abstract
Purpose
This study proposes a novel method to improve the accuracy of overall equipment effectiveness (OEE) estimation in the metallurgical industry. This is achieved by modeling the frequency and severity of stoppage events as random variables.
Design/methodology/approach
An analysis of 80,000 datasets from a metal-mechanical firm (2020–2022) was performed using the loss distribution approach (LDA) and Monte Carlo simulation (MCS). The data were further adjusted with a product price index to account for inflation.
Findings
The variance analysis revealed supporting colleagues (59.8% of variance contribution), food breaks (29.8%) and refreshments (9.0%) as the events with the strongest influence on operating losses.
Research limitations/implications
This study provides a more rigorous approach to operational risk management and OEE measurement in the metal-mechanical sector. The developed algorithm supports the establishment of risk management guidelines and facilitates targeted OEE improvement efforts.
Originality/value
This research introduces a novel OEE estimation method specifically for the metallurgical industry, utilizing LDA and MCS to improve accuracy compared to existing techniques.
Keywords
Acknowledgements
Funding: This paper is derived from the project coded 206001326 “Multivariate and multicriteria methods in the explanation of diverse business realities, phase III” (Métodos multivariados y multicriterio en la explicación de diversas realidades empresariales, fase III) supported and funded by Tecnológico de Antioquia I.U.
Citation
Restrepo, J.A., Giraldo, E.A. and Vanegas, J.G. (2024), "Measuring the production performance indicators for metal-mechanic industry: an LDA modeling approach", International Journal of Productivity and Performance Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPPM-04-2023-0201
Publisher
:Emerald Publishing Limited
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