A six sigma based approach to evaluate the on time performance of Indian railways

Purpose The purpose of this paper is to propose an approach to evaluate "on time" performance for a class of Indian railways (IR). This approach is build-up on the theme of six sigma level computation for continues data. The arrival data obtained from a class of IR called "Rajdhani Express" exhibited a unique characteristic: neither did the data followed a distribution nor could it be transformed to fit into a distribution. In this work, the authors present an approach to evaluate on time performance of IR, given such "unruly" data. Design/methodology/approach An attempt is made to develop a lucid approach, given an unruly data. Initially, the authors plot a histogram using Scott's method. Later, the authors use Taguchi's quality loss function to assign weights to each of the bins in histogram. Weights to each of the bins are assigned based on the predefined rules. Finally, sigma level is computed by using weighted defect per million opportunities (DPMO) approach. In this paper, the authors discuss the proposed algorithm, an illustrative example with an emphasis on a class of IR. Findings Given the unique characteristic (unruly data) of arrival data of IR, the proposed methodology helps in quantifying it is on time performance. This method extends the conventional DPMO approach of computing the sigma level. The proposed method is validated using various data sets of different distributions. Further, this approach can be generalized and applied to data set of any distribution. Since distribution of the data is not the pre-requisite, the proposed approach can be applied to compare (and benchmark) data sets of different distributions. This methodology, thus, paves path to develop newer approaches in quantifying and benchmarking the sigma levels. Research limitations/implications In this manuscript, the authors present a case of Rajdhani Express trains, a class of IR. A practicing manager can use this approach to compare the performance of various classes of railways and benchmark their performance. Such an approach, with suitable modifications (if any) can be applied to evaluate performance in various service industries. Originality/value Usually, if the data are unruly, the sigma level is computed by using ad-hoc methods that may provide compromising solutions and/or inaccurate results. The developed methodology proposes a unique approach to quantify sigma level, given such an unruly nature of the data. This approach thus fills the long needed gap in addressing such situations. This approach can be applied in various similar situations. A case of IR is presented.