Prediction of novel operating parameters using Six Sigma: A study in the steel making process

Abstract

Abstract It is now imperative to improve global competitiveness in an organization by upgrading process performance, enriching operational excellence besides organizational excellence. This paper illustrates flexible and practical application of Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) to optimize the process parameters for the operating regime for dephosphorization in hot metal with low silicon (< 0.4%) while steel making. The statistical techniques such that hypothesis testing, non-parametric test and regression techniques were used to statistically define the key process parameters which had significant impact on dephosphorization. Six Sigma methodology was adopted to define the optimal values required to achieve the desired phosphorus level (< = 0.015%). This paper is based on case study of a renowned steel company of India, imprints that amount of oxygen blown and tapping temperature were significantly impacting the above dephosphorization process. The regression model was recommended for reference of steel engineers and managers for maintaining an optimum level of above process parameters in augmenting production of low Phosphorus steel grade. Steel industrialists, academics, consultants, researchers and Six Sigma practitioners will benefit from this study. The success of this study would help more such industries in improving the performance of similar processes.