Empirical likelihood based variable selection

Information criteria form an important class of model/variable selection methods in statistical analysis. Parametric likelihood is a crucial part of these methods. In some applications such as the generalized linear models, the models are only specified by a set of estimating functions. To overcome the non-availability of well defined likelihood function, the information criteria under empirical likelihood are introduced. Under this setup, we successfully solve the existence problem of the profile empirical likelihood due to the over constraint in variable selection problems. The asymptotic properties of the new method are investigated. The new method is shown to be consistent at selecting the variables under mild conditions. Simulation studies find that the proposed method has comparable performance to the parametric information criteria when a suitable parametric model is available, and is superior when the parametric model assumption is violated. A real data set is also used to illustrate the usefulness of the new method.     

Implementation of Lean Six Sigma framework with environmental considerations in an Indian automotive component manufacturing firm: a case study

Lean Six Sigma is a systematic data driven methodology that integrates two powerful business improvement strategies Lean Manufacturing and Six Sigma with the goal of removing wastes and reducing process variation. Lean Six Sigma has a positive effect on environmental performance as defect reduction and reducing process variation leads to reduction in raw material consumption, energy consumption and reduced scrap which in turn reduces the overall environmental impacts. In this context, this study uses a Lean Six Sigma framework with environmental considerations to reduce overall defects and environmental impacts concurrently to improve the firm’s operational and environmental performance. The framework is based on Define Measure Analyze Improve Control methodology where traditional Lean Six Sigma and environmental impact assessment tools are integrated to systematically deploy LSS strategies with environmental considerations. The framework is validated with an industrial case study conducted in an Indian automotive component manufacturing organisation and the inferences are derived. On successful deployment of the framework, the internal defects was brought down to 6000 ppm from 16,000 ppm and environmental impacts was reduced to 33 Pt from 42 Pt. Deployment of the developed framework helped in improving the firm’s sigma level and also reduced the overall environmental impacts.