Exploring the dynamics of bigdata adoption in the Indian food industry with fuzzy analytical hierarchical process.

Purpose: The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India. Design/methodology/approach: The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the "analytical hierarchical process" (AHP) and the "fuzzy analytical hierarchical process" (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry. Findings: The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC. Research limitations/implications: This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround. Originality/value: The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further. [ABSTRACT FROM AUTHOR]