Book Description
Food security and the development of agriculture in a sustainable manner are facilitated by agricultural supply chains. The planning, design, coordination, organization, and monitoring of the movement of agricultural products from farm to fork is the focus of agriculture supply chain management (ASCM) in the World. The study of organization, planning, coordination, and monitoring of agriculture commodities from farm to fork is known as agriculture supply chain management (ASCM). The supply chain is a web of different functions from production to distribution. In addition to focusing on cost reduction, sustainable supply chains also have significant positive effects on the social and environmental spheres. Global suppliers and sustainability are influenced by Institutional dimensions (Pereira et al., 2023). These Institutional dimensions help in sustaining the supply chains minimizing the wastes and their detrimental impacts. However, a number of issues, including supply chain interruptions, market volatility, and climate change, frequently pose a threat to these supply chains (Routroy & Behera, 2017). The shift towards regenerative agriculture supply chain that is climate savvy will overcome the detrimental impact on social and environmental pressures. In order to reduce waste in the supply chains, optimize the resources, and maximize profit-sustaining livelihoods, agriculture has transformed its supply chain strategies.
The ASCM also manages big data that is characterized by volume, veracity, variety, and velocity (Panetto et al., 2020). Volume - Big data generated by the supply chain is massive. Velocity - The amount of information produced every second on the waste, processing, and supply chain for agricultural goods is also quick. Variety - The agricultural supply chain generates a wide range of data on production, acquisition, wastage, inventory control, and processing (Sharma et al., 2020). Veracity - Although the data can be kept at every stage of the supply chain, there are concerns about its correctness and dependability. The change may be facilitated through coordinated monitoring of the agricultural supply chain and data analytics (Kamble et al., 2020). The supply and demand of agricultural goods can be handled using descriptive analytics. This provides information about what happened and how it transpired with respect to agricultural commodities. Predictive analytics uses historical data to understand the likely course of events in the supply chain. Prescriptive Analytics goes ahead of predictive analytics. It improves decision-making skills by sensitizing the potential outcomes. It aids in addressing issues like what may be the best outcome and how it can be brought into the supply chain. The application of Artificial Intelligence in the agriculture supply chain has helped in overcoming these issues (Nayal et al., 2021).
The transition towards sustainable supply chain management is a complex function where each function needs to be critically explored (Kumari et al., 2023b). Studies need to explain institutional support, organizational restructuring, network and symbiosis, training, agri-input facilities, knowledge dissemination, quality storage, capacity building, promotional activities, data analytics, and monitoring (Kumari et al., 2023a) for ASC around the globe. Initiatives and Learning on Supply chain networks and industrial symbiosis in the Global Agriculture Supply Chain will help in sustaining the supply chain and creating a path toward regeneration. It is necessary to investigate how our understanding of by-products and their uses can be regenerated in the ASCs. The effectiveness of global agricultural supply chain management depends on how well the stakeholders are informed about new developments in the supply chain and how to use them for efficient management. Planning storage structures and Warehousing helps to increase the shelf life of agricultural commodities and a better supply chain.
The book, therefore, tries to provide justice with a stakeholder perspective to every functional level, from quality inputs to post-harvest management, storage, and processing. The chapters selected for the book will help to gain an understanding of each function which will benefit the academicians, researchers, policymakers, and practitioners. This book will provide an excellent opportunity for researchers, practitioners, and policymakers to share their knowledge and expertise on innovative solutions and best practices for advancing toward sustainable agriculture supply chains. The findings of this book will contribute to the ongoing efforts to achieve food security and sustainable agricultural development.
Key References
>Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194.
Kumari, S., Jeble, S., Venkatesh, V. G., Nagarajan, C., & Shi, Y. (2023a). Antecedents of agriculture supply chain performance during COVID-19: an emerging economy perspective. Operations Management Research, 16(1), 489-510.
Kumari, S., Venkatesh, V. G., & Shi, Y. (2023b). Assessment of Risks and Risk Management for Agriculture Supply Chain. In Supply Chain Risk and Disruption Management: Latest Tools, Techniques and Management Approaches (pp. 155-172). Singapore: Springer Nature Singapore.
Nayal, K., Raut, R.D., Queiroz, M.M., Yadav, V.S. and Narkhede, B.E. (2021), "Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective", The International Journal of Logistics Management, (ahead-of-print No. ahead-of-print). https://doi.org/10.1108/IJLM-01-2021-0002.
Pereira, M. M., Silva, M. E., & Hendry, L. C. Developing global supplier competences for supply chain sustainability: The effects of institutional pressures on certification adoption. Business Strategy and the Environment, From https://onlinelibrary.wiley.com/doi/abs/10.1002/bse.3363.
Panetto, H., Lezoche, M., Hormazabal, J. E. H., Diaz, M. D. M. E. A., & Kacprzyk, J. (2020). Special issue on Agri-Food 4.0 and digitalization in agriculture supply chains-New directions, challenges and applications. Computers in Industry, 116, 103188.
Routroy, S. and Behera, A. (2017), Agriculture supply chain: A systematic review of literature and implications for future research, Journal of Agribusiness in Developing and Emerging Economies, 7 (3), 275-302.
Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 1-42.
Impact
>The book will help the students, practitioners, and policymakers to understand the initiatives taken in every function of the Agriculture Supply Chain around the world. This will also help the agriculture supply chain to strengthen the functional levels of the chain. The book will benefit the stakeholders in risk assessment and risk management in Agriculture Supply Chain. In addition to risk management, the book will also provide measures and learnings from sustainable models of the agriculture supply chain across different parts of the world. The book will be a unique contribution towards proposing a future road map for developing and underdeveloped countries to strengthen their agriculture supply chain.
Primary audience
The book will help the students, practitioners, and policymakers to understand the initiatives taken in every function of the Agriculture Supply Chain around the world. This will also help the agriculture supply chain to strengthen the functional levels of the chain. The book will benefit the stakeholders in risk assessment and risk management in Agriculture Supply Chain. In addition to risk management, the book will also provide measures and learnings from sustainable models of the agriculture supply chain across different parts of the world. The book will be a unique contribution towards proposing a future road map for developing and underdeveloped countries to strengthen their agriculture supply chain.