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Cloud computing technology: Reducing carbon footprint in beef supply chain

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  • Singh, Akshit
  • Mishra, Nishikant
  • Ali, Syed Imran
  • Shukla, Nagesh
  • Shankar, Ravi

Abstract

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.

Suggested Citation

  • Singh, Akshit & Mishra, Nishikant & Ali, Syed Imran & Shukla, Nagesh & Shankar, Ravi, 2015. "Cloud computing technology: Reducing carbon footprint in beef supply chain," International Journal of Production Economics, Elsevier, vol. 164(C), pages 462-471.
  • Handle: RePEc:eee:proeco:v:164:y:2015:i:c:p:462-471
    DOI: 10.1016/j.ijpe.2014.09.019
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    2. Nagesh Shukla & Senevi Kiridena, 2016. "A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 6984-6996, December.
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    7. Kopp, Thomas & Nabernegg, Markus K., 2022. "Inequality and Environmental Impact from Food Consumption - Can the Two Be Reduced Jointly?," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322125, Agricultural and Applied Economics Association.
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    11. Nishikant Mishra & Akshit Singh, 2018. "Use of twitter data for waste minimisation in beef supply chain," Annals of Operations Research, Springer, vol. 270(1), pages 337-359, November.
    12. Khushboo E-Fatima & Rasoul Khandan & Amin Hosseinian-Far & Dilshad Sarwar, 2023. "The Adoption of Robotic Process Automation Considering Financial Aspects in Beef Supply Chains: An Approach towards Sustainability," Sustainability, MDPI, vol. 15(9), pages 1-34, April.
    13. Hui Zhang & Haiqian Ke, 2022. "Spatial Spillover Effects of Directed Technical Change on Urban Carbon Intensity, Based on 283 Cities in China from 2008 to 2019," IJERPH, MDPI, vol. 19(3), pages 1-19, February.
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    15. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    16. Dragomirov Nikolay & Boyanov Luben, 2021. "Supply Chain Management and Logistics Big Data Challenges in Bulgaria," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 12(1), pages 171-181, January.
    17. Frank Ebinger & Bramwel Omondi, 2020. "Leveraging Digital Approaches for Transparency in Sustainable Supply Chains: A Conceptual Paper," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    18. Nakatani, Jun & Tahara, Kiyotaka & Nakajima, Kenichi & Daigo, Ichiro & Kurishima, Hideaki & Kudoh, Yuki & Matsubae, Kazuyo & Fukushima, Yasuhiro & Ihara, Tomohiko & Kikuchi, Yasunori & Nishijima, Asak, 2018. "A graph theory-based methodology for vulnerability assessment of supply chains using the life cycle inventory database," Omega, Elsevier, vol. 75(C), pages 165-181.
    19. Trang Thi Pham & Tsai-Chi Kuo & Ming-Lang Tseng & Raymond R. Tan & Kimhua Tan & Denny Satria Ika & Chiuhsiang Joe Lin, 2019. "Industry 4.0 to Accelerate the Circular Economy: A Case Study of Electric Scooter Sharing," Sustainability, MDPI, vol. 11(23), pages 1-16, November.
    20. Khwanchol Kampan & Takuji W. Tsusaka & Anil Kumar Anal, 2022. "Adoption of Blockchain Technology for Enhanced Traceability of Livestock-Based Products," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    21. Liyin Shen & Yingli Lou & Yali Huang & Jindao Chen, 2018. "A driving–driven perspective on the key carbon emission sectors in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 349-371, August.
    22. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.
    23. Sina Davoudi & Peter Stasinopoulos & Nirajan Shiwakoti, 2024. "Two Decades of Advancements in Cold Supply Chain Logistics for Reducing Food Waste: A Review with Focus on the Meat Industry," Sustainability, MDPI, vol. 16(16), pages 1-67, August.
    24. Gupta, Himanshu & Yadav, Avinash Kumar & Kusi-Sarpong, Simonov & Khan, Sharfuddin Ahmed & Sharma, Shashi Chandra, 2022. "Strategies to overcome barriers to innovative digitalisation technologies for supply chain logistics resilience during pandemic," Technology in Society, Elsevier, vol. 69(C).

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