An integrated lean and green approach for improving sustainability performance: a case study of a packaging manufacturing SME in the U.K.

Abstract

Competitive landscape, informed consumers and stringent regulations have forced many manufacturing small- and medium-sized enterprises (SMEs) to focus on operational efficiency along with sustainability issues in recent years. While many manufacturing organisations have been taking lean initiatives for the past few years for operational excellence, an impulsive rush to adopt lean without a strategic deployment vision has led to scattered implementation of lean tools and projects without desired success. Many researchers and practitioners prescribe value stream mapping as a foundation for lean transformation initiatives; however, little empirical work is available on the symbiosis of lean and green paradigms to reap maximum benefits. This research, through a systematic methodology and a novel tool called Green Integrated Value Stream Mapping (GIVSM), integrates both paradigms in a case study on a U.K. packaging-manufacturing SME. Applying the GIVSM demonstrates that simultaneous deployment of lean and green paradigms have synergistic effect for improving both operational efficiency and environmental performance. In addition, continuous improvement framework with sustainable procurement is proposed to overcome the lean-green misalignments. This study also provides a guiding reference for practitioners to undertake similar improvement projects and identifies opportunities to expand this academic research on integrated lean-green approach into other industry sectors.     

Test for randomness of the technology parameter in a stochastic frontier regression model

This paper considers the problem of testing for randomness of the technology parameter in a stochastic frontier regression model. A test statistic is proposed and its asymptotic distribution theory is discussed. Simulation results show that the proposed test maintains its level and also quite powerful against various alternatives. An empirical investigation has been carried out by applying the suggested test procedure to the data set on electric utility companies. The results are consistent with the general finding that the technology parameter of the stochastic frontier model used for modeling these data is random.