India, which has long been recognized as a well-endowed nation in natural mineral resources, is a major minerals producer. According to the report of Indian Ministry of Mines 2013, Indian mining and metals sector ranked the fourth among the mineral producer countries, behind China, United States, and Russia and had in fact led the economy into recovery from the global financial crisis. Since this industry has turned into a significant issue, this paper attempts to rank the performance of 23 Indian mining and metal companies and to evaluate and measure the productivity change of these sectors during different time periods (2010–2014). Besides, the authors would like to choose one advanced model of MPI to see the performance of these companies in the past-present period and the 4-year future period (2015–2018) by using forecasting results of Grey system theory. The results revealed that from the past to future period the National Mineral Development Corporation, Hindalco Industries Limited, and Coal India always keep their highest best rankings among 23 DMUs regarding performance scores. This study contributes better insights of Indian mining industry as it is the core of the economy.

The generalized process incapability index C pp has been developed in the manufacturing industry to provide numerical measures on evaluating process performance. Contributions on the estimated incapability index C pp in existing quality assurance and statistical literature are almost from the frequentist point of view. In this paper, a Bayesian approach is developed under a non-informative prior to obtain the interval estimation for the generalized process incapability index C pp . Useful maximum values required to ensure the posterior probability reaching a certain desirable level based on C pp are tabulated. A Bayesian procedure for judging whether the process satisfies the preset quality reliability requirement is proposed. A practical example is also presented to illustrate how the proposed procedure may be applied.