Employing cloud computing technology to mitigate carbon footprint of beef supply chain
5.7 Execution of the CCT based eco-friendly supplier selection of cattle
This section demonstrates the working of the proposed methodology. A beef abattoir and processor company is operating in India. The maximum chunk of their products are being exported to foreign countries. However, they do sell some amount of their products in local markets as well. In the past, the decision of selection of their cattle supplier was driven by the conventional requirements of consumers (both local and abroad), which were high quality, minimum price, traceability, etc. However, there is lot of pressure on this firm both from the government and the consumers to cut down the carbon emission in their supply chains. This company has ample resources to optimize the carbon emission at their end. However, the majority of emission in their supply chain takes place at beef farms. In order to cut down the carbon emission in their beef supply chain, the abattoir and processor company has to make both their and their beef farms operations eco-friendly. The farmers have less knowledge and no mechanism to measure the carbon emission and take preventive measures to mitigate them. They lack the awareness and resources to purchase a carbon calculator to quantify the carbon footprint in their farms. The carbon calculators are very expensive and often very sophisticated to utilize. The abattoir and processor firm will select an appropriate carbon calculator which is both precise and user friendly and install them on a private cloud maintained by them. All the potential suppliers (beef farmers) to
134 this firm can access this calculator via cloud by just having Internet connection. These beef farmers have to make an account on the cloud and enter the details of their farm like breed, age, diet, weight, etc. of cattle as shown in figure 5.8. The values of farmer profile are being shown in Table 5.3. The carbon calculator installed on the cloud will process these details as shown in figure 5.10 and generate the results of carbon emission for these farmers. Thereafter, the cloud will extract the breed, conformation and fatness score for all the farmers and utilize Grey Relational Analysis as described above (section 5.6) to calculate the quality of beef corresponding to various breed. The calculated linguistic terms and grey numbers representing the quality of meat for each farmer are shown in table 5.1 and 5.2. The higher the value of variable for quality, the better is the quality of meat. For example, supplier S1 has better quality of meat compared to that of S2. Thereafter, abattoir and processor will set the importance of different attributes over the cloud depending on demand of market, consumer preference, country of sale, etc. For example, in this case, quality of meat, price and carbon footprint are the three variables having highest importance in descending order. As soon as importance of various attributes of supplier selection, quality of meat and carbon footprint are calculated, the Topsis method will generate the ranking of the supplier from most appropriate to least appropriate, which is shown in table 5.4, while making trade-off between different attributes. Based on the criteria set by abattoir and processor and farmer’s profile, supplier S8 is the most appropriate supplier, who produces high quality of meat in minimum carbon emission. The abattoir and processor will start negotiating with these suppliers starting from the most appropriate supplier. When both the parties mutually agree, then the cattle are procured from the most fitting supplier.
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Figure 5.10 showing information entered by farmer is being processed by carbon calculator uploaded on private cloud
Table 5.4 Ranking of beef cattle supplier obtained by Topsis method
Rank Supplier Relative Closeness
1 S8 0.7051 2 S10 0.60853 3 S1 0.55855 4 S5 0.50763 5 S2 0.49106 6 S6 0.4886 7 S3 0.30528 8 S4 0.26601 9 S7 0.14268 10 S9 0.098091 5.8 Managerial implications
An integrated framework is proposed in this chapter to measure and mitigate the carbon footprint generated by the whole beef supply via CCT infrastructure. It would be very beneficial to SMEs of beef supply chain as they are deficient of resources and knowledge
136 of carbon footprint generated by their farms. The proposed framework would prevent them from procuring expensive carbon calculator on their own as it could be utilised via SaaS from private cloud in a cost-effective manner.
Every segment of beef supply chain could utilise carbon calculator uploaded on cloud and obtain their carbon footprint results, which would be visible to managers and decision makers of other segments of beef supply chain. A feedback in the form of suggestive measures would also be provided by carbon calculator. It would assist managers of different segments of the supply chain in optimum decision making to reduce their carbon footprint and improve efficiency. For instance, the farmers would be given guidance about the breed of cattle associated with lowest carbon footprint. The integrated framework would assist policy makers of retailer to identify the segments associated with high carbon footprint and inefficient product flow, which could be addressed by the feedback given by carbon calculators.
The private cloud developed by the retailer is encompassing the entire beef supply chain and it would assist in addressing carbon footprint of a particular segment generated because of its interdependency on other segments of supply chain. For instance, it will suggest the logistics firm various means to mitigate their carbon hotspots, which are inter- dependent on retailer. It would also assist in revealing the good and bad practices followed by a specific segment of supply chain with regards to their carbon footprint. For instance, distinct logistics firms might be employed in the interface of farm to abattoir and from processor to retailer. The carbon footprint information of both the firms could be used by the managers of these logistics firms to replace their bad practices with good practices of the other firm. This research has a huge impact of the traditional approach of measurement of carbon emissions at one segment of beef supply chain. It would assist in enhancing the vertical and horizontal coordination in the whole supply chain resulting in improved and sustainable product flow within the supply chain. For instance, the coordination among the managers of farming enterprises and logistics firms would be strengthened in terms of efficient planning of shipping of cattle and specific measures to be considered such as ample space allowance, journey time within permissible limits, etc.
Consumers have adopted a very selective approach towards traceability associated with beef products post horsemeat scandal on one of the supermarket in the UK. The information sharing attribute of the proposed framework would assist in mitigating this
137 problem. Hence, it will create the opportunities for retailer managers to raise the price of beef products following traceability procedures. Simultaneously, there is a rise in the consumer’s awareness about the carbon footprint of all the products consumed by them. It could be mitigated by this study and could be beneficial for retailer in promoting their sustainable beef products and draw the attention of consumers. It would assist the decision maker of retailer to identify the stakeholders of beef supply chain which has to be altered to meet the government target of eco-friendly businesses.
The integrated framework proposed in this study would assist all segments of beef supply chain to identify, measure and prioritise their carbon hotspots while addressing them. Also, all managers of beef supply chain could track their progress in reducing their carbon emissions as their history of carbon footprint results would be saved in the private cloud database.
During the process of supplier selection by abattoir and processor, there will be a trade-off made between the carbon emission occurring at farm end and the conventional factors like breed, conformation, fatness score etc. The manager of abattoir and processor will have to curb emissions both at their premises and also carbon footprint generated at the premises of their suppliers to make their supply chain eco-friendly. Hence, they have to consider the carbon emission at beef farms while doing the supplier selection. This framework will give a broader view to the manager of abattoir and processor, as those farmers will also be able to connect to them via cloud, which were out of range earlier. The manager of abattoir and processor will be able to target different segments of market preferring different quality parameters with this system. The manager will utilize GRA (Grey Relation Analysis) to vary the three different quality parameters viz. breed, conformation and fatness score and select the most appropriate supplier for a particular market segment. The cloud-based framework will help farmers to optimize their carbon emission and other conventional factors as per their requirement of abattoir and processor. It will make them aware of modern trends and also help them to raise their cattle as per demand of abattoir and processor. Simultaneously, farmers will also learn from the good practices of the other farmers to reduce their carbon emission, as the relevant information of all the farmers will be visible on cloud. The abattoir and processor will also upload guidelines on the cloud- based framework for farmers on procedures and techniques to reduce their carbon footprint and improve other factors. It will help the farmers to save money and develop an
138 appropriate strategy. They will be aware of what breed of cattle needs to be raised, what to feed them, etc.
5.9 Conclusion
All segments of beef supply chain are generating carbon footprint. Traditionally, these segments were only concerned about their financial revenue. Nonetheless, due to the pressure from government legislation, they have to take into account the carbon emissions done by their operations. The SMEs of beef supply chain could not address this issue pertaining to their deficiency in financial and technological resources. There is weak vertical coordination in the supply chain as there is no integrated framework to share the carbon footprint results of different stakeholders among each other. In order to address these shortcomings, this chapter proposes an integrated and collaborative framework based on CCT to optimise and measure carbon footprint of entire beef supply chain. Firstly, the carbon hotspots associated with all segments of supply chain: farms, abattoirs, processors, logistics and retailers are identified. Then, a private cloud is created by the retailer to encompass the whole beef supply chain irrespective of their locations. The carbon footprint generated in the process of product flow of beef products from farm to retailer would be mitigated and quantified. The vertical and horizontal coordination in the supply chain would also be strengthened resulting in improved efficiency and sustainability of supply chain. The execution of the proposed framework has been demonstrated via case study method.
This chapter also highlights eco-friendly supplier selection of beef cattle by abattoir and processor. It shows how carbon footprint generated in beef farms can be taken into account along with breed, age, diet, average weight of cattle, conformation, fatness score, traceability and price. Quality of beef is dependent on combination of breed, conformation and fatness score of the cattle. GRA (Grey Relation Analysis) is being used to combine these three factors and the resultant factor is being known as Quality. Then, quality, carbon footprint and other previously mentioned factors detrimental for supplier selection are assigned a weightage according to the priority of customers and quality inspector of abattoir and processor. Topsis method will process the information of various beef cattle suppliers in terms of above mentioned factors and generate a ranking list of suppliers, starting from most appropriate to least appropriate supplier. The proposed technique in this
139 study is being successfully demonstrated on Indian beef industry in case study section. This research will not only help abattoir and processor in reducing their carbon footprint but will also help beef farmers to cut down their carbon emission. As most of the carbon footprint of beef supply chain is being generated in farms, this study will help in curbing these emissions. More farmers would be able to connect to abattoir and processor by using the cloud-based framework described in this chapter. These farmers will learn the modern trends associated with beef beyond conventional factors like price and breed. There will be an opportunity for farmers to learn from the good practices of other farmers in minimizing their carbon emission and also improving in terms of other factors.
This study has some operational limitations. Some of the farmers in India are uneducated and reluctant to adopt modern practices. They need to be motivated to engage in sustainable practices in the beef farms by raising awareness about the numerous benefits associated with it. Also, the weightage assigned to all the variables quality, price, traceability, carbon footprint, etc. could be biased due to the limited information collected from consumer’s preferences and quality inspector of abattoir and processor. It could be mitigated by increasing the sample size of the information collected from both the sources to optimise the allocated weightage to all the variables. Some parts of rural India are still deprived of internet connectivity. Therefore, this cloud based framework could not be implemented at such locations. Government and private players associated with the Digital India plans could play a crucial role is addressing this situation.
The proposed mechanism utilised CCT for measuring and minimising carbon footprint of all stakeholders of beef supply chain and helped abattoir and processor in eco-friendly supplier selection of cattle. The frameworks proposed in chapter 3-6 assists in reducing carbon footprint and physical waste of beef supply chain to improve its sustainability. These objectives, could be achieved if consumer centric beef supply chain is developed, which is associated with less waste, low carbon footprint and assists retailer to capture larger market share. The next chapter is focused on making beef supply chain consumer centric by using amalgamation of big data analytics, Interpretive Structural Modelling (ISM) and MICMAC techniques. A thorough literature review and big data analytics is utilised to identify the most significant factors influencing the beef purchasing decision of consumers. Then, ISM and MICMAC analysis was performed to investigate the relationship between these factors to develop a consumer centric beef supply chain.
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