Contribution

In this thesis, various methodologies were developed to mitigate the waste and carbon footprint generated in the beef supply chain. The major contributions of this study are as following:

a. This research presents a thorough literature review on waste and carbon emissions generated during the product flow in the beef supply chain. Different issues, limitations and the frameworks developed for waste minimization in beef supply chain were discussed. The research work done in the domain of reducing carbon footprint of beef supply chain was examined.

176 b. During the research, it was revealed that 45000 tweets associated with beef products are made on daily basis on an average. These tweets are focused on quality attributes and issues related to rancidity, flavour, discoloration and presence of foreign bodies, etc. The retailer of beef products could use this valuable data to identify the root causes of waste underlying the supply chain and consequently develop waste minimization strategy. The consumer complaints on Twitter are unstructured in format and vague in nature. The literature is deficient of a framework to link these complaints to root causes of waste with various segments of beef supply chain (Singh et al., 2017; Mishra and Singh, 2016). In this thesis, a novel mechanism is proposed to capture and examine this Twitter data and back track it to the root causes of waste in the beef supply chain. The root causes of waste in beef supply chain could be addressed for waste minimization, boosting consumer satisfaction, enhancing brand value and thereby improving the financial revenue of retailer. Hence, this thesis makes a vital contribution to existing literature by linking the consumer complaints on Twitter in the downstream of beef supply chain to their respective root causes in the upstream of beef supply chain.

c. A thorough investigation of waste generated in Indian beef supply chain was performed to identify its root causes to address the imbalance between production and consumption. Various stakeholders of beef supply chain were interviewed, which was analyzed via Current Reality Tree method to explore the root causes and preventive measures to mitigate them. During the study, it was revealed that majority of waste in beef supply chain is attributed to natural characteristics such as short shelf life, fluctuations in demand and temperature sensitivity. There were numerous management root causes leading to significant amount of waste such as poor quality of meat, lack of vitamin E in diet of cattle, scarcity of information exchange, management of cold chain, lack of skilled labour, forecasting issues, promotions, quality of packaging, lack of waste minimisation strategy, etc. It was concluded that a strong vertical coordination within the beef supply chain is the foremost action needs to be taken to address the root causes of waste. It will improve the information exchanged between the stakeholders of supply chain.

177 d. Usually, measurement of carbon footprint in beef supply chain is done on a segment level (Nguyen et al., (2010); Ogino et al., (2007); Bustamante et al., (2012); Kythreotou et al., (2011)) i.e. at farms, abattoir and processor, logistics and retailer level. The availability of integrated model for mapping carbon emission of entire beef industry is quite rare (Singh et al., 2015). In this thesis, an integrated, collaborative and centric framework is proposed for measuring and optimizing carbon footprint of entire beef supply chain using cloud computing technology. Firstly, carbon hotspots are identified for all segments of supply chain (farms, logistics, abattoir, processor and retailer). Then, a private cloud is developed by retailer to map the whole beef supply chain irrespective of their geographical locations. Apart from optimizing and measuring the carbon footprint of entire beef supply chain, it also improves the vertical and horizontal coordination of supply chain making their operations eco-friendly and efficient. The efficacy of proposed system is demonstrated via case study. Therefore, this research addresses the shortcoming of existing literature by mitigating the carbon footprint of entire beef supply chain from farm to retailer.

e. The cloud based framework for eco-friendly supplier selection of beef cattle would provide opportunity to more farmers to connect with abattoir and processor using cloud based framework. There will be rise in awareness of beef farmers about the modern trends of raising cattle beyond the conventional characteristics of price and breed. It will assist farmers to replicate the good practices of other farmers in reducing carbon footprint and also improving in terms of conventional characteristics.

f. In the past, stakeholders of beef supply chain were only concerned about their profit and productivity. However, in current scenario, they must also consider the carbon footprint generated by their operations because of pressure from government legislation. The small and medium size stakeholders of beef supply chain are not capable to address this issue due to lack of awareness and financial resources (Singh et al., 2015). The cloud based integrated framework proposed in this thesis would assist the small and medium size stakeholders to mitigate this issue in a cost-effective way. Therefore, the small and medium size farmers could overcome the financial, technological barriers and contribute in developing

178 ecofriendly beef supply chain by implementing the proposed integrated framework in this thesis.

g. A novel mechanism for eco-friendly supplier selection of beef cattle by abattoir and processor is proposed, which would take into account carbon footprint along with conventional characteristics of cattle such as breed, age, diet, average weight of cattle, conformation, fatness score, traceability and price. These characteristics are assigned a weightage as per the priority of consumers and quality inspector of abattoir and processor. The aforementioned information of different cattle suppliers is analysed by Toposis method to generate a ranking list of suppliers from most appropriate to least appropriate supplier. The execution of proposed framework is demonstrated on a case study on Indian beef supply chain. It will assist both beef farmers, abattoir and processor in reducing carbon footprint

h. The food industries are aware of the factors influencing consumer’s purchasing decisions. Nonetheless, they could not fathom how these factors are linked with each other. The food retailers employ various means to receive consumer feedback such as market research, interview of consumers, collecting consumer feedback within retail stores, etc. However, the response rates of these methods are low and usually they are biased in nature. Hence, these methods give limited outlook of the consumer priorities (Mishra et al., 2017). The information available on social media reflects the true opinion of consumers, which could give precise insights to decision makers of retailers. In this thesis, Twitter analytics is being used to identify the consumer preferences for buying beef products to give them ‘sense of empowerment’ and therefore made an attempt to bridge the gap in the existing literature and provide an insightful framework to industrial practitioners for capturing consumer feedback.

i. This study has a two-fold contribution to the literature on the consumer interest in beef. Firstly, although many research studies in the beef industry have focused on the motivational factors affecting consumers’ purchasing decisions while purchasing beef (Clark et al., 2017; Lewis et al., 2016; Morales et al., 2013; Hocquette et al., 2014), none of them have offered an alternative approach to theory

179 building emerging from the various quality characteristics and other factors that could be considered while purchasing beef (Mishra et al., 2017). This research undertakes a comprehensive review of literature generating the most important eleven factors or clusters and devises a theoretical framework based on the interrelationships of those variables emerging from the consumers (social media data) and experts’ opinion using Interpretive Structural Modelling (ISM) and fuzzy Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement (MICMAC) analysis. Secondly, this research further extends the existing literature on consumers’ decisions toward purchasing beef by offering a strategic framework, which is not only based on literature but also validated using the big data clustering technique that divide all such potential variables in the most important clusters that influence consumers’ beef purchasing decisions. In the current research, the number of such clusters coincides to eleven factors. Therefore, the proposed theoretical framework extrapolates eleven factors at eight different layers and their interrelationships highlighting the specific roles of these variables. In conclusion, this thesis makes a contribution to the existing literature by highlighting the most significant drivers behind purchase of beef products and their interrelationships which are crucial in developing consumer centric beef supply chain.