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

What is it about?

The aim of this study is to analyse Big Data analytics (BDA) drivers in the context of food supply chain (FSC) for transition to Circular Economy (CE) and Sustainable Operations Management (SOM). Therefore, ten different BDA drivers in FSCs for transition to CE, which are Supply Chain (SC) Visibility, Operations Efficiency, Information Management & Technology, Collaborations between SC partners, Data-driven innovation, Demand management & Production Planning, Talent Management, Organizational Commitment, Management Team Capability, Governmental Incentive are determined. Interpretive structural modelling (ISM) methodology is used to indicate the relationships between the identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with different experts from industry and academia. The results show that, Information Management & Technology, Governmental Incentive and Management Team Capability drivers are classified as an independent factors, Organizational Commitment, and Operations Efficiency are categorized as a dependent factor. SC Visibility, Data-driven innovation, Demand management & Production Planning, Talent Management, Collaborations between SCs partners can classified as a linkage factor. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSCs transition from linearity to CE and SOM. Besides, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM. The interactions between these drivers will provide benefits to the industry and academia to prioritise and investigate these drivers efficiently when implementing BDA.

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