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More circular models of living and eating are growing traction in their adoption by businesses and governments (Bek and Lim, 2018). The pandemic has been viewed as an opportunity to ‘build back better’ with circular economy approaches key to a lower carbon economic recovery plan (Ellen MacArthur Foundation, 2020). Understanding the potential of these solutions and navigating the risks faced remains a priority for the future of food.

Potential solutions such as maximising the composting of by-products and green waste could save 1.7 billion tonnes of CO2 annually. Moving to regenerative systems of food production can minimise the impact on natural inputs, such soil condition, to yield savings in operation costs. Diverting surpluses away from landfill and incineration driven by the creation and increase in taxes is one ported solution (Feedback, 2020). More research is needed to better understand the decisions being made in food supply chains and how, at a practical, operational and strategic level, circular economy principles are part of this process

Greater insight on the type of analytical methods required to understand the proliferation of more circular processes in this sector is critical. For example developed mathematical models to reveal network design, distribution and efficiency is one effective approach. This tack seeks to open this topic further by exploring how various approaches, such as operations research, forecasting, data mining and machine learning amongst other approaches, are revealing important insights with regards to decision making in food supply chains and the transition to a circular economy.

Food supply related studies may cover following (not limited to) topics:

  • Operations research and developed mathematical models for the food supply chain.
  • Forecasting and other statistical analysis in the food supply chain area.
  • Data mining applications for the food related studies.
  • Artificial intelligence/Machine learning application in the food industry.
  • Multiple Criteria Decision Making for food logistics.
  • System dynamics and simulation for the Food supply chain related studies.