Previsão de séries temporais aplicado aos indicadores de performance em um processo de laminação a quente

Abstract

Measuring a company's efficiency is fundamental for decision-making, influenced by the performance of its assets, including technology, industrial capacity, quantity of products and employee qualifications. The efficiency of production systems often depends on large volume production with little variety of products, which is directly linked to the efficiency of processes or bottlenecks. Applying demand forecasting models based on time series is an effective tool to obtain this information. However, to date, no studies have been found that applied these models in a hot rolling process, which raises the opportunity for investigation. The main objective of the dissertation is to develop a prediction model for performance indicators in a non-flat hot rolling processin a steel industry based on time series. The case study demonstrated how the global efficiency index factors impact the rolling process. The results indicated the ARIMA (2,0,2) model as the most appropriate, and its predictions revealed daily values of the OEE global efficiency index between 0.404 and 0.993. The results showed that the L2 Lamination process can work to achieve a challenging working range (0.699 < OEE ≤ 0.891), based on benchmarks from technical literature. The tool developed can be valuable for defining strategies and directing decision-making based on the insights provided by this forecasting model. The research demonstrated applying time series models in the steel industry contributes to management and efficiency improvement strategies.