REVISTA DYNA MANAGEMENT

Key words:

Efectividad Global del Equipo (OEE), Mejora Continua, Lean Manufacturing, modelos de regresión logística, Overall Equipment Effectiveness, Continuous Improvement, logistic regression models

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

ABSTRACT:
Nowadays, most companies are managing large amounts of information and they often lack the ability to use them as a competitive advantage in their decision-making process. This research applies statistical analysis to the information provided by a company in the cosmetics industry, not only in a descriptive but in an explanatory and predictive function. Data collection corresponds to two production units of a factory, hairdressing and coloring, with different packaging lines in the conditioning phase. Production lines performance is paramount to take advantage of them, as well as improving efficiency and productivity. A systematic way to monitor the use of productive resources is the indicator Overall Equipment Effectiveness (OEE). The advantage of OEE is that it measures in a single indicator all parameters of industrial production: availability, performance and quality. The contribution of this study is to identify factors that cause the deviation of these both rates respect the target previously set by corporate management. A logistic regression model has been formulated. As a practical contribution of this paper, a series of recommendations within the program of continuous improvement of the plant is proposed, promoting the effort to involve employees in this "learning process". The main finding of this study is the explanatory power of production time loss, rather than time inefficiencies in the production line management. The most significant time loss is due to formula or reference or format changes. The combined effect of micro-stoppages and change of workshift does significantly contribute to the explanatory power of the model.

Keywords: Overall Equipment Effectiveness, Continuous Improvement, Lean Manufacturing, logistic regression models