Circular supply chain emphasizes surge in application of reuse, recycling and remanufacturing and thereby promotes the transformation of manufacturing characteristics from linear ('take-produce-utilize-dump') to circular model of flow of products, by-products and waste. Supply chains of manufacturing industries have become global in last few decades. Products manufactured in developing nations like India and China are being sent to developed nations for consumption in higher volumes. Developed nations have the regulatory policies, technological knowhow and modern infrastructure to adopt circular supply chain model. Their counterpart is trailing in these aspects. In literature, limited research work has been performed on identifying challenges of implementing circular supply chain management in developing nations and their contextual association. In this article, based on thorough literature review and feedback received from experts, sixteen important barriers were identified to circular supply chain management adoption in Indian context. The listed barriers were then analysed using an integrated Interpretive Structural Modelling-MICMAC approach. This study attempts to identify the contextual interactions among identified barriers and to examine their hierarchical levels in effective adoption and implementation of circular supply chain management. The findings of this research will contribute in transforming supply chains in terms of bringing economic prosperity, addressing global warming issues and generating numerous employment opportunities. Finally, some crucial policy measures and recommendations are proposed to assist managers and government bodies to adopt and manage the concepts of circular supply chains effectively in Indian context.

This paper proposes a big-data analytics-based approach that considers social media (Twitter) data for the identification of supply chain management issues in food industries. In particular, the proposed approach includes (i) the capturing of relevant tweets based on keywords, (ii) the pre-processing of the raw tweets, and (iii) text analysis using a support vector machine (SVM) and hierarchical clustering with multiscale bootstrap resampling. The result of this approach included a cluster of words which could inform supply-chain (SC) decision makers about customer feedback and issues in the flow/quality of food products. A case study in the beef supply chain was analysed using the proposed approach, where three weeks of data from Twitter were used. The results indicated that the proposed text analytics approach can be helpful to effectively identify and summarise crucial customer feedback for the supply chain management. This study proposes a holistic approach, in which social media data are utilised in the domain of the food supply chain. The findings of the analysis have been linked to all the segments of the supply chain.

This paper investigates the relationship between lean, process innovation, green practices and green supply chain management performance. Data were gathered by surveying 374 firms in the manufacturing supply chain industry. Structural equation modeling (SEM) was employed to analyze the collected data. The study results show general support for the theoretical research framework. Findings reveal that there is a synergetic effect between process innovations, green and lean practices playing a crucial role towards the improvement of green supply chain performance. This paper presents an innovative approach since it studies simultaneously the three dimensions of sustainability (environmental, social and economic), the lean, the innovation process and green paradigms which are considered strategic for supply chain competitiveness. Investigation of the relationships between the four strategies is a contribution that the authors hope will become a forward step in the promotion of sustainability as a third dimension of the manufacturing supply chain, along with the efficiency and responsiveness dimensions.

To provide the overview of this SI, initially a broad search was conducted to document the number of papers published in the area of 'Big data analytics' the search period used was from 2012 to 14 March 2018. An overview of the number of articles is presented in Fig. 1. After looking into the journal's specific search, Fig. 2 clearly demonstrates that the Annals of Operations Research was the leading journal in terms of the numbers of papers from Scopus.

Purpose Managing risks is becoming a highly focused activity in the health service sector. In particular, due to the complex nature of processes in the pharmaceutical industry, several risks have been associated to its supply chains. The purpose of this paper is to identify and analyze the risks occurring in the supply chains of the pharmaceutical industry and propose a decision model, based on the Analytical Hierarchy Process (AHP) method, for evaluating risks in pharmaceutical supply chains (PSCs). Design/methodology/approach The proposed model was developed based on the Delphi method and AHP techniques. The Delphi method helped to select the relevant risks associated to PSCs. A total of 16 sub risks within four main risks were identified through an extensive review of the literature and by conducting a further investigation with experts from five pharmaceutical companies in Bangladesh. AHP contributed to the analysis of the risks and determination of their priorities. Findings The results of the study indicated that supply-related risks such as fluctuation in imports arrival, lack of information sharing, key supplier failure and non-availability of materials should be prioritized over operational, financial and demand-related risks. Originality/value This work is one of the initial contributions in the literature that focused on identifying and evaluating PSC risks in the context of Bangladesh. This research work can assist practitioners and industrial managers in the pharmaceutical industry in taking proactive action to minimize its supply chain risks. To the end, the authors performed a sensitivity analysis test, which gives an understanding of the stability of ranking of risks.