PurposeThe authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.Design/methodology/approachSSCIs are identified by reviewing the extant literature and topic modeling. Further, they are evaluated based on existing SDGs and ranked using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Notably, the evaluation of indicators is a multi-criteria decision-making (MCDM) process within a fuzzy environment. The methodology has been explained using a case study from the automobile industry.FindingsThe case study identifies appropriate SSCIs and differentiates them among peer suppliers for gaining a competitive advantage. The results reveal that top-ranked sustainability indicators include the management of natural resources, energy, greenhouse gas (GHG) emissions and social investment.Practical implicationsThe study outcome will enable suppliers, specialists and decision makers to understand the criteria that improve supply chain sustainability in the automobile industry. The analysis provides a comprehensive understanding of the competitive package of indicators for gaining strategic advantage. This proactive sustainability indicator selection promotes and enhances sustainability reporting while fulfilling regulatory requirements and increasing collaboration potential with trustworthy downstream partners. This study sets the stage for further research in SSCIs’ competitive strategy in the automobile industry along with its supply chains.Originality/valueThis study is unique as it provides a framework for determining relevant SSCIs, which can be distinguished from peer suppliers, while also matching economic, environmental and social metrics to achieve a competitive advantage.

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Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature available indicates that research is currently limited on banks’ stress quantification in countries like India where there have been fewer failed banks. The literature also indicates a lack of scientific and quantitative approaches that can be used to predict bank survival and failure probabilities. Against this backdrop, the present study attempts to establish a bankruptcy prediction model using a machine learning approach and to compute and compare the financial stress that the banks face. The study uses the data of failed and surviving private and public sector banks in India for the period January 2000 through December 2017. The explanatory features of bank failure are chosen by using a two-step feature selection technique. First, a relief algorithm is used for primary screening of useful features, and in the second step, important features are fed into the support vector machine to create a forecasting model. The threshold values of the features for the decision boundary which separates failed banks from survival banks are calculated using the decision boundary of the support vector machine with a linear kernel. The results reveal, inter alia, that support vector machine with linear kernel shows 92.86% forecasting accuracy, while a support vector machine with radial basis function kernel shows 71.43% accuracy. The study helps to carry out comparative analyses of financial stress of the banks and has significant implications for their decisions of various stakeholders such as shareholders, management of the banks, analysts, and policymakers.

Information and communication technology (ICT) has been the backbone of businesses for some time now. ICT adoption has rendered data availability in vertically connected organizations. In recent times, the rise of analytics has paved the way to rethink the structure and working of vertically connected firms within a supply chain. Big data analytics (BDA) for instance, has become a prominent tool to analyse unstructured data. This study explores the application and benefit of various types of analytics such as descriptive analytics, predictive analytics and prescriptive analytics in the process of supply chain management (SCM). Additionally, the study also looks at ways by which analytics could integrally be included within the SCM curriculum in higher education to prepare future supply chain analyst. Notably, the article is positioned for bridging the gap between the use of analytics in SCM, both from academia and the industry perspective.

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