A Critical Investigation into the Impact of Big Data in the Food Supply Chain for Realizing Sustainable Development Goals in Emerging Economies

In light of the present circumstances, corporate executives, government officials, and academics may now place a higher priority on the collection and analysis of crucial data as a potent instrument for solving the issues of managing the contemporary food supply chain. As food and beverage (F&B) companies place a greater emphasis on collecting, processing, and analyzing relevant data from a variety of sources throughout their respective food systems, data management has become an invaluable resource in modern food supply chains (FSCs). This is because modern FSCs are designed to be more efficient than traditional supply chains. In this context, the phrase “big data” (BD) has only very recently begun to be used to refer to huge quantities of heterogeneous and geographically dispersed data assets that have fast rates of change, a wide variety of sizes, and high volumes of information. Recent research has stated that implementing BD in FSCs might result in a yearly gain in value that ranges from USD 120 billion to USD 150 billion. The current study is focused on analyzing the impact of big data in the food supply chain for realizing sustainable development goals in emerging economies. The researcher intends to collect data from primary and secondary sources. This paper focuses on understanding the conceptual framework that incorporates the relationship between FSC performance and BD applications. 

Keywords: Big data application, Food supply chain, Machine learning, Neural network, Sustainable development goals.