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.

Purpose -With the rapid economic development of nations across the globe, there is proportionate increment in corresponding carbon footprint. There are numerous counter measures proposed to mitigate it in terms of legislation and policy framing. However, they have a shortsighted vision of predominantly focusing on manufacturing and transportation industry thereby neglecting one of the significant contributor of global emissions-agricultural industry. Among all the agri-food products, beef has the highest carbon footprint and majority of its emission are generated in beef farms. The issue is more intensive in developing nations where most of global cattle are raised and simultaneously farmers are less informed and aware of resources/technology to address emissions from their farms. Therefore, there is need to raise awareness among farmers and thereby incorporate carbon footprint as a major cattle supplier selection attribute by abattoir and processor and integrate it as a standard practice in procurement of cattle. Design/ methodology -A novel framework based on big data cloud computing technology is developed for eco-friendly cattle supplier selection. It is capable of measuring greenhouse gas emissions in farms and assimilate into the cattle supplier selection process. Fuzzy AHP, DEMATEL and TOPSIS method is employed to make an optimum tradeoff between conventional quality attributes and carbon footprint generated in farms to select the most appropriate supplier. Findings -The proposed framework would assist in shedding the environmental burden of beef supply chain as the majority of carbon footprint is generated in beef farms. Moreover, the vertical coordination in the supply chain among farmers and abattoir, processor would be strengthened. The execution of the framework is depicted in case study section. Originality-The literature is deficient of ecofriendly supplier selection in the agrifood sector particularly in developing countries. This study bridges the gap in the literature by proposing a novel framework to incorporate carbon footprint into traditional supplier selection process via an amalgamation of big data, ICT and Operations Research. The proposed framework would assist in mitigating the carbon footprint of beef products as they have highest emissions among all agri-food products. This framework is generic in nature and can be implemented in any food supply chain.

Global warming is an alarming issue for the whole humanity. The manufacturing and food supply chains are contributing significantly to the large-scale carbon emissions. Beef supply chain is one of the segments of food industry having considerable carbon footprint throughout its supply chain. The major emissions are occurring at beef farms in the form of methane and nitrous oxide gases. The other carbon hotspots in beef supply chain are abattoir, processor, logistics and retailer. There is a huge amount of pressure from government authorities to all the business firms to cut down carbon emissions. The different stakeholders of beef supply chain especially small and medium-sized stakeholders, lack in technical and financial resources to optimize and measure carbon emissions at their end. There is no integrated system which can address this issue for the entire beef supply chain. Keeping the same in mind, in this paper, an integrated system is proposed using Cloud Computing Technology (CCT) where all stakeholders of beef supply chain can minimize and measure carbon emission at their end within reasonable expenses and infrastructure. The integrated approach of mapping the entire beef supply chain by a single cloud will also improve the coordination among its stakeholders. The system boundary of this study will be from beef farms to the retailer involving logistics, abattoir and processor in between. The efficacy of the proposed system is demonstrated in a simulated case study. KeywordsCarbon footprint, beef supply chain, cloud computing technology (CCT) Disciplines Engineering | Science and Technology StudiesPublication Details Singh, A., Mishra, N., Ali, S., Shukla, N. & Shankar, R. (2015) Abstract Global warming is an alarming issue for the whole humanity. The manufacturing and food supply chains are significantly contributing to the large scale carbon emissions. Beef supply chain is one of the segments of food industry having considerable carbon footprint throughout its supply chain. The major emissions are occurring at beef farms in the form of methane and nitrous oxide gases. The other carbon hotspots in beef supply chain are abattoir, processor, logistics and retailer. There is huge amount of pressure from government authorities to all the business firms to cut down carbon emissions. The different stakeholders of beef supply chain especially small and medium sized stakeholders, lack in technical and financial resources to optimize and measure carbon emissions at their end. There is no integrated system, which could address this issue for the entire beef supply chain. Keeping the same in mind, in this paper, an integrated system is proposed using Cloud Computing Technology (CCT) where all stakeholders of beef supply chain can minimize and measure carbon emission at their end within reasonable expenses and infrastructure. The integrated approach of mapping the entire beef supply chain by a single cloud will also improve the coordination among its stakeholders. The system boundary of this study will be fr...

The food retailers have to make their supply chains more customer driven to sustain in modern competitive environment. It is essential for them to assimilate consumer's perception to improve their market share. The firms usually utilise customer's opinion in the form of structured data collected from various means such as conducting market survey, customer interviews and market research to explore the interrelationships among factors influencing consumer purchasing behaviour and associated supply chain. However, there is abundance of unstructured consumer's opinion available on social media (Twitter). Usually, retailers struggle to employ unstructured data in above decision-making process. In this paper, firstly, by the help of literature and social media Big Data, factors influencing consumer's beef purchasing decisions are identified. Thereafter, interrelationships between these factors are established using big data supplemented with ISM and Fuzzy MICMAC analysis. Factors are divided as per their dependence and driving power. The proposed frameworks enable to enforce decree on the intricacy of the factors. Finally, recommendations are prescribed. The proposed approach will assist retailers to design consumer centric supply chain.