Nowadays along with the rapid development of industrialization across the globe, the environmental and ecological impacts of products have become a serious issue. Taking into account purely the economic impacts of industrial decisions, and excluding their ecological impacts, make the human beings and animals more at risk to many threats such as global warming, ozone layer depletion, toxic environments, and natural resources depletion. To minimize the environmental effect, implementation of green supply chain management (GSCM) is much more essential for industries in the environmental and social point of view. The purpose of this chapter is to analyze barriers to an implementation of green supply chain management in a stone crushing plant of Southern India by using modified simple additive weighting (SAW) to rank approaches. Further, this study will help the small-scale industries to understand the factors affecting implementation of GSCM in their organizations.
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The developing issues in the global market for green concerns and a shortage of natural resources have constrained official to observe supply chain systems from an environmental point of view. Stone crushing industry supply chain with high environmental risks, because of the mining process involves like drilling, blasting, braking, transportation and crushing process produces pollutions like air pollution, water pollution, soil pollution, noise pollution Mali et.al (2016). At present, environmental issues become real critical barriers for many supply chain corporations concerning the sustainability of their businesses. (Gilbert, 2000) the green supply chain management is developing field inspired by the requirement for environmental awareness. In this context, several studies have been proposed by both academia and industry trying to develop a new technique for analysis of barriers related to the implementation of green supply chain management (GSCM).
In the literature, researchers have been recommended various experimental criteria for green supply chain (GSC) performance measurement and projected both qualitative and quantitative frameworks. Mathiyazhagan et.al (2013b) has analyzed pressures of GSCM among 36 barriers. Wang and Chan (2013) proposed the fuzzy hierarchical approach to identify an improvements area when implementing when supply chain initiatives green. However, these are mainly operational in nature and specific to the focal company. Prior studies have applied the multi-criteria decision making (MCDM) methods used to address a few issues of existing methods. The application of MCDM has grown extensively in the past few decades, and so has the number of techniques to evaluate the alternatives and select the best of them. The scientific literature mentions that over more than 70 MCDM techniques are available (Sun & Li, 2010), to help the decision maker take an appropriate decision in different phases of life-cycle, spreading its wings through the fields of sciences, business, production engineering. The collection of data from a literature survey or published articles and mainly depends on experts opinion, this kind of data information involves many interrelation process parameters are uncertainty, inexact and ambiguity in nature this kind of data or information will affect the ranking the parameters. The most of the MCDM methods having rank reversal problem Sameer and Muzasikkr (2018). The ranking of parameters is essential to improve the green supply chain management. The addition/removal of GSCM factors leads to rank reversal; the improper ranking of parameters affects the performance of the whole green supply chain management. In this chapter, introduces a modified simple additive weighting (SAW) of MCDM technique to rank the parameters to improve the GSCM performance.
The aim of this chapter is to study and analysis of barriers in GSCM using MCDM methods. In this context, the present chapter proposed an MCDM based approach, i.e. modified simple additive weighting (SAW) method. In this chapter, first, a brief introduction of Green Supply Chain Management and its barriers to implementation in the industry has been addressed. The second section, a brief introduction to multi-criteria decision making (MCDM) techniques and issues of rank reversal illustrated. Next section, proposed a modified SAW method which uses linear deviation and helps in minimizing rank reversal chances compare to other existing methods. Thereafter, critical analysis of the implementation of GSCM carried out real-time data of the southern part of Indian (Bangalore) industry. And finally, the chapter concludes with important guidelines concerning the application of modified MCDM methods for effective utilization of process parameters for any green supply chain management and future direction of work.