Evaluation and Prediction Methods for a Steel Company Using Six Sigma Metrics, Capability Indicators, and Markov Chains

Abstract

The operational dynamics of the steel industry constitute one of the work systems with the highest severity and accident rates. To address this, this research multidimensionally evaluates and stochastically predicts the preventive capability of the safety system in a steel plant. Using a quantitative, evaluative, and longitudinal three-phase design, the retrospective evaluation of nine preventive variables was employed using Six Sigma metrics (DPMO, Z, Y), along with the evaluation of overall performance through the Geometric Capability Indicator (GCI) and the Arithmetic Capability Indicator (ACI), and the stochastic modeling of the process using Markov chains. It was demonstrated that evaluating processes in isolation hides structural inefficiencies, as four variables showed an Excellent individual performance (Z≈6.0), but the comprehensive multivariate evaluation revealed a Deficient systemic state (GCI of 0.471 and ACI of 0.493). Furthermore, Markov modeling on the compliance of the process management index predicted a 100% probability of long-term stagnation in a deficient absorbing state (x1=1). It is concluded that the proposed method functions as a rational anticipation system that provides potential managerial benefits by offering early warning indicators of operational degradation, supporting corrective decision-making on unstable preventive indicators.