Journal of Mines, Metals and Fuels

Jerin Joseph ¹, Rajeshwar S Kadadevaramath ², Akarsha Kadadevaramath ³, Mohan Sangli ¹

Affiliations
1 Research Scholar Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, Karnataka, India., IN
2 Professor and Head, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, Karnataka, India., IN
3 Mechanical Engineer, Intel India Pvt. Ltd Bangalore, Karnataka, India., IN

Abstract

Supply chain design is essential while considering environmental and economic issues in order to prevent negative environmental impacts induced by increasing levels of industrialization. A novel sustainable supply chain design strategy is offered in this research to address the trade-offs between environmental and economical concerns. One of the most important operational tasks in the construction of a green SCM is the identification of sustainable suppliers. Although many studies have considered economic criteria like cost, quality and lead time to select suppliers, just a few have taken environmental and social factors into account. This study suggests a number of integrated Multi-Criteria Decision Making (MCDM) methods for choosing and assessing environmentally and economically responsible suppliers. Various approaches of MCDM such as SAW, WASPAS, TOPSIS, and GRA are used in the suggested methodology.

Keywords

Supplier Selection, Green, Mcdm, Supply Chain.

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Mohan Sangli ¹, Rajeshwar S Kadadevaramath ², Jerin Joseph ¹, Akarsha Kadadevaramath ³, Immanuel Edinbarough ⁴

Affiliations
1 Research Scholars, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, IN
2 Professor and Head, Industrial Engineering Department, Siddaganga Institute of Technology Tumkur, IN
3 Engineer, Intel India Pvt. Ltd., Bangalore, Karnataka, IN
4 Professor and Head, Engg. Tech, Industrial and Manufacturing EnggDept, The University of Texas, US

Abstract

Predictive models are built by learning the combined effects of several independent variables that directly or indirectly influence the outcome. H. Response or dependent variable. In practice, data collection has data on a large number of independent variables that are outcome-sensitive and may or may not be related to the outcome. Some independent variables have a large impact on the results, while others may have little or no impact on the results. The presence of some independent variables that are irrelevant to the outcome can affect the performance of the predictive model. In this context, it is desirable and essential to identify the independent variables that most influence the forecast model to keep it lean and efficient. In this work, we used a dataset containing employee turnover rates and explored how to identify a subset of outcome-sensitive variables, thus eliminating variables that hinder the development of effective predictive models. By partially selectively influencing the independent variables, we developed lean and efficient predictive models that enabled us to act on an actionable subset of the variables to reduce staff turnover, thereby improving corporate save effort and cost.

Keywords

Predictive model, Sensitive parameter, Dimensionality.

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