Drivers of implementing Big Data Analytics in food supply chains for transition to a circular economy and sustainable operations management

Purpose The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM). Design/methodology/approach Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia. Findings The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM. Research limitations/implications The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics. Originality/value The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

Automated summary

This study evaluates Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM), using an interpretive structural modelling (ISM) methodology to indicate relationships between identified drivers to stimulate transition to CE and SOM. The findings show certain drivers are independent factors, some are dependent factors, and others are linkage factors, with Governmental Incentive being the most fundamental driver to achieve BDA applications in FSC transition to CE and SOM. Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.