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      Resilience and sustainability enhancements in food supply chains using Digital Twin technology: A grey causal modelling (GCM) approach

      , , ,
      Computers & Industrial Engineering
      Elsevier BV

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          Food waste within food supply chains: quantification and potential for change to 2050

          Food waste in the global food supply chain is reviewed in relation to the prospects for feeding a population of nine billion by 2050. Different definitions of food waste with respect to the complexities of food supply chains (FSCs)are discussed. An international literature review found a dearth of data on food waste and estimates varied widely; those for post-harvest losses of grain in developing countries might be overestimated. As much of the post-harvest loss data for developing countries was collected over 30 years ago, current global losses cannot be quantified. A significant gap exists in the understanding of the food waste implications of the rapid development of ‘BRIC’ economies. The limited data suggest that losses are much higher at the immediate post-harvest stages in developing countries and higher for perishable foods across industrialized and developing economies alike. For affluent economies, post-consumer food waste accounts for the greatest overall losses. To supplement the fragmentary picture and to gain a forward view, interviews were conducted with international FSC experts. The analyses highlighted the scale of the problem, the scope for improved system efficiencies and the challenges of affecting behavioural change to reduce post-consumer waste in affluent populations.
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            Understanding the concept of supply chain resilience

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              The rise of blockchain technology in agriculture and food supply chains

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                Author and article information

                Contributors
                Journal
                Computers & Industrial Engineering
                Computers & Industrial Engineering
                Elsevier BV
                03608352
                May 2023
                May 2023
                : 179
                : 109172
                Article
                10.1016/j.cie.2023.109172
                4bbbff97-5d20-4a1d-88f6-daad3f764861
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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