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
