The purpose of this paper is to develop a criterion for selection of critical sub‐processes when all the sub‐processes cannot be taken up simultaneously for improvement. There exist various methods but the practitioners get utterly confused because of the existence of these multiple options. In this paper, the goal is to assist practitioners in the selection of the critical sub‐processes.
The authors discuss various statistical methods such as correlation and regression, simulation, basic statistics such as average, standard deviation, coefficient of variation % (C.V.%), etc. for the selection and identification of the critical sub‐processes. The strengths and weaknesses of these methods have been compared through empirical analysis based on real‐life case examples.
The stepwise regression and simulation have been found to yield identical results. However, from the perspective of application, stepwise regression has been found to be a preferred option.
The roadmap thus evolved for the selection of the critical sub‐processes will be of great value to the practitioner, as it will help them understand the ground reality in an unambiguous manner, resulting in a superior strategy for process improvement.
