Use of twitter data for waste minimisation in beef supply chain

Approximately one third of the food produced is discarded or lost, which accounts for 1.3 billion tons per annum. The waste is being generated throughout the supply chain viz. farmers, wholesalers/processors, logistics, retailers and consumers. The majority of waste occurs at the interface of retailers and consumers. Many global retailers are making efforts to extract intelligence from customer’s complaints left at retail store to backtrack their supply chain to mitigate the waste. However, majority of the customers don’t leave the complaints in the store because of various reasons like inconvenience, lack of time, distance, ignorance etc. In current digital world, consumers are active on social media and express their sentiments, thoughts, and opinions about a particular product freely. For example, on an average, 45,000 tweets are tweeted daily related to beef products to express their likes and dislikes. These tweets are large in volume, scattered and unstructured in nature. In this study, twitter data is utilised to develop waste minimization strategies by backtracking the supply chain. The execution process of proposed framework is demonstrated for beef supply chain. The proposed model is generic enough and can be applied to other domains as well

Multi-agent architecture for waste minimisation in beef supply chain

Food waste is an alarming issue pertaining to the rising global hunger, huge environmental footprint, and high monetary value. In developing and developed nations, it occurs primarily due to inefficiencies upstream and downstream of the supply chain respectively. A common factor in both developed and developing nations is product flow within the supply chain from farms to retailers. This study aims to identify the root causes of waste generated across the product flow of the beef supply chain from farm to retailer. A workshop involving twenty practitioners of the beef industry was conducted and the collected information was transcribed and coded to generate a current reality tree, which assisted in identifying root causes of waste in the entire beef supply chain. A multi-agent architecture framework spanning the entire beef supply chain from farm to retailer is proposed, which is composed of autonomous agents capable of bringing all segments of the beef industry on a single platform and collaboratively assist them in mitigating root causes of waste. The proposed framework will aid the practitioners in the beef industry to reduce waste, improve their operational efficiency thereby raising food security, economic development whilst curbing their carbon footprint

Social media data analytics to improve supply chain management in food industries

© 2017 Elsevier Ltd 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 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

Improving efficiency and reducing waste for sustainable beef supply chain

In this thesis, novel methodologies were developed to improve the sustainability of beef supply chain by reducing their environmental and physical waste. These methodologies would assist stakeholders of beef supply chain viz. farmers, abattoir, processor, logistics and retailer in identification of the root causes of waste and hotspots of greenhouse emissions and their consequent mitigation. Numerous quantitative and qualitative research methods were used to develop these methodologies such as current reality tree method, big data analytics, interpretive structural modelling, toposis and cloud computing technology. Real data set from social media and interviews of stakeholders of Indian beef supply chain were used. Numerous issues associated with waste minimisation and reducing carbon footprint of beef supply chain are addressed including: (a) Identification of root causes of waste generated in the beef supply chain using Current Reality Tree method and their consequent mitigation (b) Application of social media data for waste minimisation in beef supply chain. (c) Developing consumer centric beef supply chain by amalgamation of big data technique and interpretive structural modeling (c) Reducing carbon footprint of beef supply chain using Information and Communication Technology (ICT) (d) Developing cloud computing framework for sustainable supplier selection in beef supply chain (e) Updating the existing literature on improving sustainability of beef supply chain. The efficacy of the proposed methodologies was demonstrated using case studies. These frameworks may play a crucial role to assist the decision makers of all stakeholders of beef supply chain in waste minimization and reducing carbon footprint thereby improving the sustainability of beef supply chain. The proposed methodologies are generic in nature and can be applied to other domains of red meat industry or to any other food supply chain

Big data analytics and application for logistics and supply chain management

This special issue explores big data analytics and applications for logistics and supply chain management by examining novel methods, practices, and opportunities. The articles present and analyse a variety of opportunities to improve big data analytics and applications for logistics and supply chain management, such as those through exploring technology-driven tracking strategies, financial performance relations with data driven supply chains, and implementation issues and supply chain capability maturity with big data. This editorial note summarizes the discussions on the big data attributes, on effective practices for implementation, and on evaluation and implementation methods

Social Media Analytics in Food Innovation and Production: a Review

Until recently social media and social media analytics (SMA) were basically used only for communication and marketing purposes. However, thanks to advances in digital technologies and big data analytics, potential applications of SMA extend now to production processes and overall business management. As a result, SMA has become an important tool for gaining and sustaining competitive advantage across various sectors, industries and end-markets. Yet, the food industry still lags behind when it comes to the use of digital technologies and advanced data analytics. A part of the explanation lies in the limited knowledge of potential applications of SMA in food innovation and production. The aim of this paper is to provide a review of literature on possible uses of SMA in the food industry sector and to discuss both the benefits, risks, and limitations of SMA in food innovation and production. Based on the literature review, it is concluded that mining social media data for insights can create significant business value for the food industry enterprises and food service sector organizations. On the other hand, many proposals for using SMA in the food domain still await direct experimental tests. More research and insights concerning risks and limitations of SMA in the food sector would be also needed. The issue of responsible data analytics as part of Corporate Digital Responsibility and Corporate Social Responsibility of enterprises using social media data for food innovation and production also requires a greater attention

Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique

YesThe 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.Project ‘A cross country examination of supply chain barriers on market access for small and medium firms in India and UK’ (Ref no: PM130233) funded by British Academy, UK

Food loss and waste in food supply chains. A systematic literature review and framework development approach

This study examines the state of the art of the literature in the domain of food loss and waste (FLW) in food supply chains (FSC). The authors used a systematic literature review (SLR) approach to examine and synthesise the findings of the existing literature to identify the key research themes, research gaps and avenues of future research on FLW in FSC. To this end, this SLR considered 152 articles relevant for the review. The authors uncovered the extant literature in the domain by presenting the research profile of the selected studies, along with thematic analysis. The authors identified eight key themes from the extant literature. The themes range from factors responsible for FLW generation to new, emerging areas of research such as digitalisation and food surplus redistribution. The study's findings will help clarify existing practices in FSC for waste mitigation and act as a foundation for strategic and policy initiatives in this area. The findings indicate that the major factors responsible for FLW include the poor management of perishable food items, stakeholder attitudes, buyer-supplier agreements and supply chain interruptions. Some of the important implications of the study include formal guidelines and policy-level interventions for assisting the accurate quantification of FLW along with an impetus on digitalisation to reduce FLW. The study concludes with the development of a research framework to assist future research in this domain. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe