Search results

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.

The purpose of this paper is to analyze the productive efficiency and the best operational practices in an armaments manufacturer.

A longitudinal case study is performed using data envelopment analysis (DEA). Using DEA, an assessment of six years in the company manufacturing process is conducted. The research aims at developing an internal benchmark on three production lines of the company.

The results show that only one of the three analyzed production lines increased efficiency over time. With this result, the most efficient production line may be used as a reference in relation to the best operational practices of the company. Moreover, it was found that the current indicators to evaluate efficiency are insufficient and may lead to wrong management decisions.

This research could allow a larger understanding of the factors that really contribute to increased operational efficiency. This is due to internal benchmark assist in the identification of the best practices. The identification of best practices can contribute to enhance the efficiency of inefficient operations without the need for external comparisons.

DEA contributes due to its robustness, for the evaluation of productive efficiency. One of the contributions of this study is to identify opportunities for improvement in key components of the operation through targets, internal benchmarking and robust assessment of productive efficiency.