food supply chain research paper

• Open access
• Published: 23 August 2023

Meta-analysis of food supply chain: pre, during and post COVID-19 pandemic

• Abdul Kafi   ORCID: orcid.org/0000-0002-7300-6898 1 ,
• Nizamuddin Zainuddin 1 ,
• Adam Mohd Saifudin 1 ,
• Syairah Aimi Shahron 1 ,
• Mohd Rizal Razalli 1 ,
• Suria Musa 1 &
• Aidi Ahmi 2  

Agriculture & Food Security volume  12 , Article number:  27 ( 2023 ) Cite this article

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Metrics details

Despite the unprecedented impact of COVID-19 on the food supply chain since 2020. Understanding the current trends of research and scenarios in the food supply chain is critical for developing effective strategies to address the present issue. This study aims to provide comprehensive insights into the pre, during, and post COVID-19 pandemic in the food supply chain.

Methodology

This study used the Scopus database from 1995 to November 6, 2022, to analyse the food supply chain. Bibliometric analysis was conducted using VOSviewer software to create knowledge maps and visualizations for co-occurrence, co-authorship, and country collaboration. Biblioshiny, a shiny app for the Bibliometrix R package, was then used to explore theme evaluation path maps in the research domain.

The bibliometric analysis of 2523 documents provides important insights into present and future publication trends. Top author keywords included blockchain, traceability, food safety, sustainability, and supply chain management. The Sustainability (Switzerland) journal ranked first in productivity, and the International Journal of Production Economics received the highest citations. The United Kingdom was the most productive country, collaborating with partners in Europe, Asia, and North America. The Netherlands had the highest percentage of documents with international authors, while India and China had the lowest. The thematic evaluation maps revealed that articles focused on important research topics including food processing industry, information sharing, risk assessment, decision-making, biodiversity, food safety, and food waste.

This study contribute to the growing body of literature on the food supply chain by providing a comprehensive analysis of research trends during different phases of the pandemic. The findings can be used to inform policymakers and industry leaders about the measures required to build a more resilient and sustainable food supply chain infrastructure for the future. This study considered only Scopus online database for bibliometric analysis, which may have limited the search strategy. Future studies are encouraged to consider related published articles by linking multiple databases.

Introduction

Food supply chains are extensively explored and dependent on worldwide situations. The COVID-19 pandemic has addressed the deficiency of flexibility in food supply chain, resulting in financial and social disasters with global implications [ 1 ]. Similarly, the COVID-19 pandemic has impacted the food supply chain, from field to consumer, which represents an important sector in any country. The discriminatory nature of the pandemic had a remarkable impact on people's lives and health standards, as well as global business, supply chains, and major economies [ 2 ]. Associated restrictions imposed during the pandemic affected the food safety of household by directly disrupting the food supply chain [ 3 , 4 ]. As a consequence of the severity, the stability of the food supply chain is essential to prevent interruption towards the national economy, social security and public health. Significant association of food supply chain occurred at the combination of dynamic, fragile and complicated that can simultaneously be influenced by the regulator actions, such as road closures, lockdown, and vehicle movement control. While such restrictions prevent the spread of a disease, they can also disrupt the trade of agricultural products and market chains [ 5 ]. Since the restriction was imposed by governments around the world, food distribution declined to 60% [ 6 ]. While, the COVID-19 pandemic also created serious obstruction in many sectors such as livestock production, vegetable production, plantation, cultivation and harvesting due to a shortage of labour as these sectors are comparatively labour-concentric [ 7 ]. Although, labour shortages also represented a crucial problem even before the beginning of the COVID-19 pandemic [ 8 ]. This issue is undermining the capability of farmers and agriculture enterprises to operate because of scarcity of workers owing to illness and physical distance during harvesting. These circumstances delayed the provision of food and agriculture input and integrated issues in the food supply chain to market [ 9 ]. However, several firms depend on their key inputs, whereby the maximum are more vulnerable to disruptions since domestic markets have to meet their requirements. Barriers to logistics which interfere food supply chain further weaken high-value commodities because of their limited shelf life [ 6 , 10 ]. Therefore, maintenance of logistical efficiency, particularly during and after the global crisis is a vital aspect of the food sector. Correspondingly, raw material procurement from suppliers is the biggest bottleneck in the food supply chain and ensuring a continuous flow of food from producers to end users [ 11 ]. The challenge is to risk the capacity of agriculture producers to operate as usual which can negatively affect freshness and food safety, food quality, and limit market entrance and pricing [ 10 ]. The effects on agricultural systems due to the pandemic depend heavily on the composition and intensity of agricultural activity and vary according to the product manufactured and the country. In low-income countries, productivity is mostly labour oriented, whereas, in high-income countries, capital-intensive practices are generally dependent on agricultural production. Therefore, the supply chain should remain operational with a focus on crucial logistical problems [ 6 ]. In addition, the supply chain involves not just producers, distributors and customers, but also labour concerted food processing plants. Production in several plants has been limited, interrupted or temporarily stopped owing to workers who have been identified COVID -19 positive and who have hesitated to go to work presuming they are sick, especially in meeting processing firms at the time of the pandemic outbreak [ 12 , 13 ]. Besides, another important factor that caused food supply chin disruption during COVID -19 pandemic is centralized food production. This approach has contributed to the production and cost reduction of food processors. Centralisation has some limitation such as inflexible and long supply chain problems. Furthermore, it might cause challenges to meet demand through a small numbers of very big production facilities [ 14 ] such as closing the full process if a pandemic leads to high volume production lines with less options. In the face of these problems, food supply chain have shown tolerable resilience while supermarket shelves were refilled with the disappearance of hoarding behaviour and the demand growth response to supply chains [ 15 ].

Although several articles were published and included the aspect of COVID -19 impact on the food supply chain but are primarily concerned with the focus on the subject. Direct studies were related to bibliometric analysis to know the trend and scenario of research were not known. Thus, it is crucial to provide information with a clear direction of the present research status and future trends in the COVID-19 food supply chain. Evidence from several cohort studies in the bibliometric analysis was conducted on food safety governance [ 16 ], food supply chain safety research [ 17 ], and agri-food value chain [ 18 ]. As far as we know, there are no bibliometric analyses conducted related to COVID-19 food supply chain pre, during and post-pandemic.

After identifying this gap, this study tries to fill the gap by developing a detailed analysis of COVID-19 food supply chain studies pre, during and post-pandemic. As a quantitative analysis method, a bibliometric technique is adopted to develop a trend in various domains and utilized to uncover the present status [ 19 , 20 , 21 ]. In bibliometric analysis, researchers can define fields of research, looking at the further direction of research, and getting involved with other institutes and countries [ 22 ]. Hence, this study specifically focused to answer the pertaining research questions to present the trend of the previous studies on COVID-19 and the food supply chain with the global development of the field.

What are the dynamics and trends of COVID-19 food supply chain literature?

What are the highly-cited documents in COVID-19 food supply chain research through time?

What are the most productive authors, countries, institutions, and source titles in terms of publication numbers?

What are the more productive keywords in the COVID-19 supply chain research pre, during and post-pandemic?

What is the current knowledge formation status relating to co-occurrence, collaboration and co-authorship linkage in COVID-19 food supply chain research?

Which are the most productive research themes, and how did they evolve through time?

This study adopted a bibliometric technique to analyse the publication of COVID-19 on food supply chain research extracted from the Scopus database to provide a functional overview of the current trend of predictable research throughout the world. Scopus is considered the largest cited and referenced abstract of literature containing a wide range of subject areas. Employing Scopus is, therefore, an attempt to comprise more subjects which are not explored in WoS [ 23 , 24 ]. This study will help researchers, policymakers and individuals to support food supply chain research trends and to explore the possibilities and opportunities for future study.

To answer the research questions, the paper is organized into the following sections. “Introduction” Section discusses a brief introduction to the topic including the current knowledge on the impact of COVID-19 on the food supply chain, the research gap, and the purpose of the study. “Literature review” Section discusses the literature review in the field of the COVID-19 food supply chain in general. “Bibliometric analysis and methods” Section describes the methodology used in this study which includes Bibliometric, Biblioshiny, and presents the flowchart and data analysis structure. “Results” Section covers the discussion to answer the above research questions and future research directions. “Conclusion” Section describes the conclusion that covers the contributions, limitations and future research scopes.

Literature review

The COVID-19 pandemic triggered disruptions to the food supply chains which included purchaser concern buying attitude for key items, as well as a rapid change in consumption trends, which shifts away from the food service industry and toward meals ready and consumed at home [ 25 ]. A similar study was focused on the supply chain and the food industry is no exception. Due to a decrease in demand, the closure of food production services, and financial constrain, the businesses are unable to continue supplying their products to stores[ 26 ], and the current status of the COVID-19 pandemic has put unprecedented pressure on food supply chains. These include bottlenecks in transportation, logistics, farm labour, and food processing [ 15 ]. This behaviour of the food supply chain (FSC) is identified a main reason worldwide [ 27 ]. The scenario is that the food supply chain is disrupted with a potential breakdown in food freight borders, increasing business flexibility and social capital through real-time business communication [ 28 ]. Because of emerging COVID-19 pandemic issues, there are major concerns about food production, manufacture, delivery, and consumption in the food supply chain [ 2 ]. Therefore, a lack of consumer access to food poses the ultimate threat to food security. There is also a massive rise in global food issue with the appearance of COVID-19 and the advent of numerous difficulties in all sectors of the food supply chain (such as production, distribution, and transportation), and this problem has taken on added significance [ 29 ].

COVID-19 pandemic has had a significant effect on the global food supply chain, raising public awareness about the constancy of the global food network and the significant interruption to food availability [ 30 ]. It is proven the COVID-19 pandemic has an effect on the agricultural and food supply chain from two angles, which are "food supply and food demand" [ 31 , 32 ]. In current months, the COVID -19 pandemic had threaten the global food supply chain creating economic instability, constraints to food accessibility, restrictions on farm commodity shipping, limitations on food production, difficulties in food product transit, evolving consumer demand, food production facility closures, shortages of farm workers to harvest vegetables, farm worker travel restrictions, and fruit deficiencies [ 33 , 35 , 35 ]. Therefore, numerous countries adopted diverse strategies to reduce the impact of COVID -19 on the food supply chain [ 34 ]. However, a potential problem still exists that need to be addressed to gradually resolve the present crisis. Hence, the global food supply chain is facing many issues resulting from the continuing COVID-19 pandemic around the world, which has prompted serious issues about food supply, distribution, processing, and demand [ 35 ]. Accordingly, the COVID -19 pandemic has increased disruption and damage to the global food supply chain in the following areas, which are (i) logistics (ii) harvest (iii) processing (iv) sourcing, and (v) go-to-market [ 36 ].

Bibliometric analysis and methods

Bibliometric analysis.

Bibliometric studies provide a wide range of options for understanding the significance of all studies. A quantitative and qualitative technique of bibliometric analysis is used for the publication of journals and articles, including their corresponding citations over time [ 37 ]. It differentiates the present status of research by measuring the scientific outcome of a country and institution and has played a major role in the past in influencing policymaking and improving the knowledge of science [ 38 , 39 ]. This also allows researchers to identify and help them to determine the scope of study topics, and plan their focused mind and projection of trends [ 40 ]. This method can provide a statistical output for calculating and estimating the number and development trends of a particular field [ 41 , 42 ]. Several studies have explored the food supply chain using the bibliometric technique [ 43 , 45 , 45 ]. This research provides a quantitative literature review by drawing connections between various keywords related to the food supply chain. It is a standardized technique for calculating and assessing written communication among authors [ 46 ], quantifying the trend and characteristics of a certain research area based on several measures [ 47 , 48 ], and focusing on research titles, keywords, affiliations, authors, and article publication [ 49 , 50 ], network and countries [ 51 ], co-authorship links, co-citation links, bibliographic coupling links that may be used in citation mapping to visualize a cluster or theme [ 51 ], and supply chain management [ 52 ]. In this study, Scopus databases are considered to extract necessary information. It is chosen as the source of the largest abstract indexing database and it is recommended by the previous studies that would cover a wide range of areas and provide comprehensive search options to help researchers develop search strings with accurate results, especially in broad areas of the research [ 44 , 53 ].

Thematic evaluation

Thematic evolution is a new research technique that is currently the widest accepted method for using many disciplines to measure the topic growth, evolution, and flow of a specific research area over time, supporting scholars in understanding the growth of a particular research area more methodically. This study used Biblioshiny, a shiny application for the Bibliometrix R package, to performthe thematic evaluation mapping [ 38 , 54 ]. To analyze the evaluation theme, the proportion of total authors' keywords indicated by drawing the range of the subject direction on the coordinate axis. The growth and decrease of the alluvial area represent the change in scale over time.

Data collection

This study has chosen the online Scopus database from 1995 to 6 November 2022 in food supply chain because it is the world's largest citation and abstract database of scholarly works from international publishers which provides a one-stop platform for scientific scholars [ 55 ]. Especially, compared to other databases like Web of Science (WoS), Google Scholar (GS), and PubMed (PubMed), Scopus has a wider variety of publications and helps with both keyword searches and bibliographic analysis [ 56 ]. Scopus has 20% higher coverage than WoS in terms of citation analysis, however, Google Scholar produces inconsistent results. PubMed is a database that is commonly utilised in scientific research [ 56 ]. Figure  1 shows the search strategy and detailed steps for the data collection for this study.

Flow diagram of article searching strategy of food supply chain documents

Search strategy

In a bibliometric study, it is important to choose the appropriate keywords. Based on the research questions, this study limited the search to two main title keywords: “food” and “supply chain”. Therefore, this research encompassed two possible combination strings of keywords that are relevant to the study’s topic. The title of an article should incorporate information that can be used to capture the attention of readers since it is the first element that readers will observe first [ 57 ]. Finally, this study comprises two search query strings TITLE (“food”) AND TITLE (“supply chain”). A total of 2523 research documents between 1995 and 6 November 2022 were obtained from the Scopus database (Additional file 1 ). There were no excluded methods applied during the search of the document as shown in Fig.  1 .

Tools and data analysis

Numerous disciplines had adopted VOSviewer to perform bibliometric analysis, e.g., social media in knowledge management [ 58 ], supply chain and logistics [ 59 ], presumption [ 58 ], business intelligence [ 60 ], health [ 61 ], and brand personality analysis [ 62 ]. To achieve the research objectives and research questions, this study adopted VOSviewer software to visualize the geographical distribution, authorship, citations, keywords, collaboration among countries specifically on COVID-19 food supply chain topics. The VOSviewer visualises bibliometric maps in different methods to present various features of literature structure. The VOSviewer employs an integrated approach to mapping and clustering that is constructed on the normalised term co-occurrence matrix and a similarity measure that determines the intensity of association between terms [ 51 , 63 ]. Based on citations and bibliographic coupling links, the VOSviewer creates clusters of authors' keywords, countries, and organizations. These clusters indicate the compactness of articles, keywords, countries, and organizations in specific research. In addition, Microsoft Excel 2013 software tools were used to analyze the primary data collected from Scopus (CSV format). Finally, R studio explores the evolutionary themes of COVID-19 food supply chain research topics pre, during and post-pandemic. Figure  2 portrays the different steps and analyses performed in this study. To address the research questions, the study is divided into two parts: descriptive analysis and network analysis.

Framework for bibliometric analysis

Descriptive analysis

This section explores the COVID-19 food supply chain research profile from 1995 to 2022, these include all current publication information, research trends, prolific authors, highly cited papers, publication sources, most productive institutions and countries, and the authors’ keywords as shown in Table 1 .

Yearly publication trend

The total publication, total citation, citation per article, and citation per year of the articles published between 1995 and 2022 were used to analyzed the yearly publication trends. Table 2 and Fig.  3 describe the yearly publication trend on the food supply chain pre, during and post COVID-19. In general, the number of publications on the COVID-19 food supply chain significantly increased from 217 articles in 2019 to 419 articles in 2021. The rate of development after 2020 was rather drastic and the number of publications increased to almost double that of the previous year. The growth of published articles indicates that the topic is beginning a stage of development. As a result, as of November 2022, 345 articles were published that have undertaken and explored new related topics attributed to the worldwide pandemic issues which simultaneously disrupted the supply chain.

The trend of publications per year of COVID-19 food supply chain

Most productive authors

The number of total publications, total citations, and h- index are analyzed to understand the most influential authors in COVID-19 food supply chain research domain. There are 5839 single authors devoting to food supply chain research from 1995 to 2022. Table 3 shows the twenty most prolific authors and found that Van Der Vorst, J.G.A.J has received the highest number of publications at 26 publications, 1945 total citations and h - index of 18 in this domain. Results revealed that Kumar, A. and Li, D are among the most prominent authors in the COVID-19 food supply chain field.

Highly cited papers

Table 4 shows associated information (authors, article title, total citations, and citations per year) of the top 20 most productive journals. The paper titled “Food waste within food supply chains: Quantification and potential for change to 2050” had 1699 total citations and 141.58 citations per year, followed by “Understanding alternative food networks: Exploring the role of short food supply chains in rural development” with a total citation of 1033 and 54.37 citations per year, and “An agri-food supply chain traceability system for China based on RFID & blockchain technology” had a total citation of 751 and 125.17 citations per year, respectively. In addition, the core journals in COVID-19 food supply chain studies are multidisciplinary, referring to traceability, corporate social responsibilities, modelling approach, sustainability, bioavailability and human health, blockchain technology, fresh food quality etc.

Most productive source titles

There are 2523 articles published in different journals . Table 5 shows the top twenty source titles that published ten or more documents from 1995 to 2022. Sustainability (Switzerland), Journal of Cleaner Production and British Food Journal are the top three publishers with a total publication of 102, 67, and 52 on the COVID -19 food supply chain and total citations of 1571, 2669 and 1394, respectively.

Most productive countries

According to the Scopus database, COVID-19 food supply chain documents were extracted from 127 countries. Table 6 shows the top thirty most prolific countries with at least 25 papers published. Among the highest thirty countries, the United Kingdom is the most productive country with a leading publication of 400 articles, accounting for 15.85% followed by China with 327 articles (12.96%) ranked the second position, United States ranks third with 272 articles (10.78%), Italy ranks fourth with 234 articles (9.27%) and India ranks fifth with 230 articles (9.12%), respectively. As indicated, these productive countries have a greater concern about COVID-19 food supply chain research pre, during and post-pandemic than other countries.

Most prolific institutions

Table 7 lists the top ten most prolific institutions for the 327 articles studied accounting for 12.95% of total documents relating to COVID–19 food supply chain research. Wageningen University & amp; Research, Alma Mater Studiorum Università di Bologna and Cranfield University are the core contributor to this research domain. These institutes have published 183 articles, which interprets for 7.27% of the entire publications. The results indicate that the productive documents are extremely intense among a few institutes only.

Top frequent authors’ keywords

Table 8 presents the top frequently used authors' keywords in the food supply chain before the COVID-19 pandemic. There are 34 occurrences of the food supply chain put in the first place, followed by 24 and 10 occurrences in supply chain and supply chain management, respectively.

In bibliometric analysis, a word cloud of author's keywords is a visual representation of the most commonly used words in an article's list of keywords to identify the most common themes in an author's work. The size of each word in the cloud represents its frequency in the list of keywords [ 84 ]. Figure  4 shows the word cloud map of the top author’s keywords before the COVID-19 pandemic. In the map, cloud-found food supply chain, supply chain, supply chain management, food industry, food safety, and traceability are the always core analyse research topics. Based on this analysis, it was found that a relationship was established linking food safety, agri-food supply chain and sustainability. This proves the importance of research in linking these three keywords and their impact on the COVID-19 food supply chain are interconnected.

Word cloud of top author’s keywords (BEFORE COVID-19)

In addition, this study used Multiple Correspondence Analysis (MCA) with the R package bibliometrix to investigate the author's keywords. The MCA is a data analysis method that could be applied to the graphical analysis of categorical data [ 38 ]. This study chose MCA because this analysis can identify the underlying themes established on the author’s keywords. Using the MCA method, related keywords are grouped, providing a hierarchical display of how frequently used terms are typically employed [ 83 ]. If two separate terms (like food and supply chain) appear in the same number of articles, then the two terms can be grouped [ 38 ].

Figure  5 maps the authors’ keywords conceptual structure associated with the “food” and “supply chain” publications domain before the COVID-19 pandemic. This map shows that the publications included in the analysis are categorized into two major groups which are red and blue. In each group, some words are connected. The red cluster shows more different words to which many research publications connect the words organized in this field. The conceptual structure is appeared in the keyword ‘co-occurrence’. For the red cluster, food is linked to food safety, organic food, short food supply, agri-food supply chain, and supply chain management. For the blue cluster, the supply chain is linked to food industry, sustainability, green supply chain management. Future research focuses on service oriented architecture and traceability, which follow the food supply chain stability to protect the disruption.

Conceptual structure map based on author’s keywords (BEFORE COVID-19)

Table 9 shows the most commonly used author’s keywords in food supply chain after COVID-19. Food supply chain appeared is in first place with 61 occurrences, followed by the supply chain, and sustainability with 44 and 31 occurrences, respectively. Following Fig.  6 , Table 9 indicates the top ranked keywords based on their co-occurrence.

Word cloud of author’s keywords (AFTER COVID-19)

Figure  6 shows the word cloud map of the author’s keywords after COVID-19 pandemic. Among the top word, cloud focused food supply chain, supply chain, blockchain, sustainability, short food supply chain, supply chain management, food industry, and agri-food supply chain are the core analyse research topics. Based on this analysis, it was found that a relationship has been established linking food safety, food security, food waste, traceability, and resilience. These top words are not expected and not to consider the searching string. However, these associations clearly indicated that there are severe influences on food supply chain as a whole after COVID-19 pandemic.

Figure  7 presents the author’s keywords conceptual structure involved with the food supply chain research domain after COVID-19 pandemic. The figure explores that the publications evolved in the analysis are clustered into two key groups, which directs the logical construct of food supply chain studies. For the red group, food is linked to food system, food loss and waste, short food supply chain, and food supply chain performance. For the blue group, supply chain is linked to total interpretative structuring modelling, digital technology and internet of things. Future research focuses on food supply chain study related to blockchain, traceability, resilience, innovation, sustainable development, consumer behaviour and crucial economy, which is the way of comprehensive understanding of food supply chain research trends.

Conceptual structure map based on author’s keywords (AFTER COVID-19)

Based on the analysis of word clouds and conceptual structure maps, it has been found that certain words may appear frequently in a word cloud of an author's keywords but not be represented in the author's conceptual structure of keywords. This is because a word cloud simply represents the frequency of occurrence of individual words or phrases, while an author's keywords conceptual structure focuses on the relationships between those words and phrases. For example, a word like "food industry” may appear frequently in an author's list of keywords and thus have a comparatively large size in the word cloud, but it may not be a central concept in the author's research, and therefore may not have a visible position in the author's keywords conceptual structure. On the other hand, a less frequently occurring word like "consumer behaviour” may have a more central position in the author's conceptual structure, even though it appears less frequently in the word cloud. Overall, both tools serve different purposes in bibliometric analysis and provide complementary insights into an author's research focus [ 50 ].

Bibliometric mapping analysis of COVID-19 food supply chain

A common application of bibliometric mapping analysis is to recognize particular research fields to gain an outline of the topology of the study area, its themes, topics, and terms, and how they connect closely [ 85 ]. Furthermore, to visualise the output of bibliometric mapping, a worldwide mapping analysis method is visualization of similarities (VOS) [ 46 , 51 , 83 ] has been adopted through a computer aided program called VOSviewer (Leiden University, Netherlands) [ 63 ]. The VOSviewer visualizes bibliometric maps in a range of methods in accordance with emphasising unique factors regarding the literature production. VOSviewer applied a combined method for both clustering and mapping or it is mainly created on the standardised term co-occurrence which estimates relationship strength between terms and is also an effective tool for conducting network analysis [ 86 ]. Furthermore, VOSviewer Version 1.6.2 [ 63 ] allows the construction of sceneries in which terms are coloured based on the year of their first presence in scientific publication. The size of the font and the enclosing rectangle indicates the popularity of a term; bigger rectangles and fonts indicate more productive terms. This study used VOSviewer to visualise co-authorship and collaboration networks and R studio to visualise co-occurrence of keywords and thematic evaluation of COVID-19 food supply chain topics.

Co-authorship analysis

Co-authorship network visualisation revealed knowledge domain maps of major authors groups in the COVID-19 food supply chain research. Figure  8 , each node shows an author, and the size of the nodes indicates the number of published articles. The link connecting two nodes represents the collaborative relationship between two authors, and the thickness of the link indicates the degree of association. Based on the knowledge domain maps of the co-authorship network, potential authors can deliver important information for a research institute to improve collaboration groups, for individual researchers to seek collaboration scopes and for the publisher to gather editorial teams to publish special issues in journals or books. It can be seen from Fig.  8 , the cooperation among prolific authors is intense. Co-authorship network formed several groups, such as the yellow group comprising Van Der Vorst (documents 26, links 26), followed by Kumar (documents 19, links 36) in the green group, and Liu (documents 16, links 16) in the yellow group as the core. Based on the results among the research groups, most productive authors mainly work independent or in collaboration inside the same organization, but the scale of such collaboration is small and not firm resulting to a lack of effective international exchange and cooperation.

Co-authorship network among productive authors

Countries collaboration network

This study established a collaboration network among countries through VOSviewer software in the research domain knowledge map in the field of COVID-19 on the food supply chain. A network visualisation map is presented in Fig.  9 . The co-authorship collaboration was established among 127 countries whereas articles were contributed from 67 countries (minimum threshold document 5). The thickness of the line indicates with each country can be determined by the frequency of co-authorship. This map indicates that a satisfactory collaboration network was established between the United Kingdom, China, United States, Italy, India, Netherlands, Germany, and France. More importantly, the United Kingdom collaboration developed with the European, Asian and North American countries and received the highest citations of 14712, links 301 and 400 documents. However, Singapore, Peru, Israil, Algeria, Romania, Lativia, Ukraine and Qatar have had less cooperation with other countries. In our opinion, there are two significances of the high proportion of articles in countries. First, it contributes to the delivery of more detailed study topics, and secondly, provides a window of opportunity for the collaboration of new countries; in other words, it enables the collaboration of other researchers and institutions in these fields.

Co-authorship collaboration network among countries

Keyword co-occurrence network

Keywords are the major content of publications, and the purpose of keyword analysis is to identify important research compositions in COVID-19 food supply chain. A co-occurrence network of author’s keywords was used to highlight research topics in the field. The method refers to the most commonly used keywords represented by the font size and larger circles [ 61 ]. The lines between the keywords reflect their correlation strength [ 20 , 61 ]. For a better understanding, the related keywords are commonly listed, as indicated by the same colour. Keywords without lines between them indicate that no connection has been developed. Considering that the closer to the centre of the network map terms appear, the more co-occurrence together. A closer connection indicates a stronger association.

To explore associated keywords to COVID-19 food supply chain research, the results in Fig.  10 indicate that two thematic clusters have been identified such as (i) food supply chain, and (ii) Supply chain and each cluster is denoted in a different colour. As shown in Fig.  10 , food supply chain covers the network centre, demonstrated by the greater cluster theme studied by previous scholars. The keyword ‘food supply chain’ is superposed on sustainability, food waste, COVID-19, circular economy, resilience, small and medium enterprise and food loose representing the closeness between them, further, COVID-19 impact on the food supply chain cannot be separated. Though in the second network of the supply chain, the linked keywords are supply chain management, blockchain, food industry, traceability, food safety, agri-food supply chain and short food supply chain. These highlight the COVID-19 pandemic impact and implies the rising attraction in this research field.

Author’s keywords co-occurrence analysis

This study used Biblioshiny software to analyse the author's keywords pre, during and post-COVID-19 food supply chain research to draw the evaluation of core research themes from 1995 to 2022. Figure  11 shows a Sankey diagram to analyse the journal's thematic evolution for the readers related to COVID-19 food supply chain. A Sankey diagram is used to demonstrate how various themes are related and have evolved in the past [ 54 ]. Each box on the map represents a theme, and the size of the boxes is relational to the frequency with which the theme occurs [ 87 ]. The flows connect each box, displaying the theme's evolution traces, and the thicker the connecting line, the stronger the link between the two themes. Overall, themes in COVID-19 supply chain are becoming more diverse over time, probably because more scholars from various fields are attracted to this theme. These indicated that the COVID-19 food supply chain gradually intersects with various fields such as e-government, traceability, information sharing, risk assessment, food waste, and blockchain. As shown in Fig.  11 , COVID-19 research in the food supply chain has evolved into six new themes from 1995 to 2019 and three themes from 2020 to 2022. Furthermore, it shows how the six themes exhibited with three themes before and after the COVID-19 pandemic.

Thematic evaluation based on authors’ keywords (Pre and Post COVID-19)

While Fig.  12 shows the thematic map before the COVID-19 pandemic mainly the upper-right quadrant reveals the motor theme which is high centrality and density because these themes are well developed and significant to the structure of a research field. The lower-right quadrant shows the basic themes. They are categorized by moderate centrality, which is resilience and the main focus in COVID-19 food supply chain research. This quadrant consists both transversal and general themes. The upper-left quadrant shows the niche themes which are strongly interconnected among themselves and have strong centrality to outside research. These concerns are extremely broader and significant. The theme related in the lower-left shows the growing or decreasing themes which are minimal and underdeveloped. The themes in this quadrant have a low density and centrality and they commonly reveal new theme.

The strategic thematic map of the author’s keywords (BEFORE COVID-19)

Figure  13 provides an overview and future trends of academic research on the food supply chain after the COVID-19 pandemic through the themes presented in the four quadrants. The motor themes have been developed extensively in both food supply and supply chain management. Food waste and catering services are well developed and isolated, and they occupy the basic theme. This is practically after COVID-19 pandemic, these themes gained importance for the food supply chain. Relatively emerging theme have currently been discussed comprehensively the subject of blockchain and food safety included in the third quadrant of cointegration has been on the boundary between basic and emerging themes, which is high centrality and density to the structure of a research field. Finally, the fourth quadrant is the food chain and food contamination themes are relatively wider and have a strong connection with the food supply chain. The most important rising topics in this period were the blockchain and food safety, which is on the border between basic and emerging or declining themes. Strongly interconnected niche theme is food contamination as a new problem in the food supply chain.

The strategic thematic map of the author’s keywords (Post COVID-19)

Discussion and future research direction

The descriptive analysis simplifies the present trend of research on the food supply chain by analysing pre, before and -post-COVID-19 data. It shows growing interest in COVID-19 on the food supply chain from the scientific community, which is an attractive issue in research, with a significant publication. Still, the COVID-19 pandemic led to high incidents in the food supply chain and the investigation and development strategies might be the result of this interest. It is proven that the current trend of COVID-19 impact is a major challenge to food supply chain analysis research and the pertaining term in the food supply chain has been encouraged in terms of COVID-19 impact reduction. By analysing the top keywords, many prospects for future study that have been disclosed by the COVID-19 pandemic. The COVID-19 has exposed supply chain vulnerability, confirming the importance of optimization and simulation. Implementing new technologies can improve efficiency, save costs, and increase customer satisfaction, allowing businesses to remain competitive. Similarly, Keywords on short food supply address these difficulties, there may be a shift towards shorter, more localized food supply chains can improve resilience and reduce the risk of disruptions caused by global crises or natural disasters. This approach may also provide potential synergy between sustainability practices and the need for more robust and sustainable food supply systems [ 88 , 89 ]. The use of blockchain technology can enhance traceability in supply chains, reducing the risk of food fraud and improving food safety. Blockchain can also support the adoption of sustainable and effective supply chain management strategies, particularly for short food supply chains. However, further research is necessary to develop new models for direct sales and blockchain-based systems that can validate the sustainability of food products. To minimize waste and increase efficiency, it is essential to integrate blockchain technology into food supply chains in a way that considers both economic and environmental impacts [ 91 ]. Finally, the Covid-19 outbreak has emphasized the significance of collaboration and innovation in the food industry. In the coming years, there may be a surge in investments in research and development, along with increased partnerships between industry players to discover new ways to address the issues affecting the food sector. Through these efforts, the industry can build a more resilient, sustainable, and secure food system for the future [ 92 ]. This also might lead to different directions for research, which include exploring new related topics and exploring less studied fields through a new framework. It might also be beneficial to provide new collaboration opportunities to widen the scope of the study.

Then, this study found the most productive countries, institutes and productive authors. The results explored that the United Kingdom is the most emerging country, but at the growing stages, China, the United States and Italy are also effective contributors in this field. The Wageningen University & amp; Research and Alma Mater Studiorum Università di Bologna have been the most productive institutes with one hundred twenty (120) and thirty-three (33) publications. Looking at the prolific authors, Van Der Vorst received the highest citation with 1945 citations. These countries and institutions have produced more in-depth and critical research in this area. The results of this analysis help governments, institutions, and authors work together, share knowledge of the COVID -19 food supply chain, and employing comprehensive strategies and efficient methods to address supply chain-related issues.

To develop an in-depth understanding of the results, this study used bibliometric mapping to provide a more comprehensive visualization of the results. The author’s keywords and co-occurrence (or co-word) analysis demonstrated that more research was focused on COVID-19 food supply chain and its impact on food security, food safety, traceability, food industry and sustainability which is closer to the circle. This finding addresses the issue of the COVID-19 food supply chain is linked to agriculture production and policy for food sustainability and its outcome is rational on the implication of food safety and food security to protect regular demand [ 87 , 88 ]. Another important issue suggests that creating more supply chain resilience may initiate a better initiative to managing and reducing challenges and risks faced pre and post COVID-19 pandemic [ 89 , 90 ]. Meanwhile, the co-authorship network shows that comparatively less collaboration occurs among authors on COVID-19 food supply chain research. This implies that the findings of Van Der Vorst and Kumar play an important role in the collaboration network. Although having a low level of relationship act as knowledge breakers among groups. The findings of the study also show that Li, D. Manning, L. and Accorsi, R have the same number of publications but few relational ties. This outcome could be influenced by limited collaborations within a closed group. Therefore, effective collaboration among scholars is required to widen the scope of COVID-19 food supply chain research.

By analysing thematic evaluation using the author’s keywords and compiling the results that postulate further direction that the research prolific topics of COVID-19 food supply chain are mainly connected to the food processing industry, traceability enterprise performance, information sharing and dissemination, decision making, risk assessment, food waste, food loose, and food traceability system. These eight primary directions are captured with each other in the evolutionary process. Research on the COVID-19 food supply chain still has significant potential for development because the integration, intensity, and reorganization of themes are more pronounced, showing that the articles are closely related and the degree of diversity is not high. This interdisciplinary research indicates that the COVID -19 food supply chain has an impact on food security and triggered disruption to respond to the current pandemic food security and caused disruption to food supply chain and suggests developing a framework for smooth resilience in the food supply chain system [ 33 ].

Furthermore, this study develops a basic structure for the most affected themes within the global food chain created by the post-COVID-19 pandemic. Generally, the global food chain combines production, processing, distribution, and consumption on a large scale [ 15 , 92 ]. The intensity of the impact may vary among sectors and goods at various stages of the supply chain for various products. There are four significant issues raised in the thematic analysis that the global food chain should address in the post-COVID-19 pandemic. The following diagram (Fig. 14 ) presents the basic structure of the global food chain that can help the food industry adapt to the post-pandemic situations caused by COVID-19.

Proposed framework for building robust and resilience global food supply chain in the post-COVID-19 pandemic

Source: Author(s): Author own construction

Firstly, COVID-19 has highlighted the crucial importance of food safety in the global food chain. Ensuring safe food from farm to fork is now a top priority for governments, food manufacturers, and consumers worldwide. Therefore a robust food safety system (e.g., processing, distribution and preparation activities, consumption, and delivery) and a strong food safety culture would safeguard public health and prevent future pandemics [ 93 ]. Secondly, COVID-19 disruptions in transportation, distribution, and consumption have led to increased food waste, resulting in significant losses in food supply chains. These relevancy have encouraged the implementation of Industry 4.0 technologies and practices, aimed at addressing the widely recognized issue of food waste and loss. Specifically, advanced technologies such as Internet of Things (IoT) platforms, BIG Data, artificial intelligence, and information and communication technologies (ICTs) can be leveraged to obtain up-to-date information, enhance communication between suppliers and buyers, and rationalise the distribution of food supply chain [ 90 ]. Thirdly, the COVID-19 pandemic has disrupted the food supply chain, raising concerns about the need for increased transparency, efficiency, and safety in the food industry. Consequently, there is a growing interest in employing blockchain technology to resolve these issues. Blockchain provides a secure and transparent way to monitor food products from the farm to the table, thereby reducing food waste and ensuring food safety and quality. With its potential to improve supply chain management and food safety, blockchain is anticipated to play a crucial role in the post-COVID-19 pandemic food industry [ 94 ]. Fourthly, the COVID-19 pandemic has resulted in an increase in pressure on natural resources and a decline in biodiversity. The post-COVID-19 pandemic presents an opportunity to construct more resilient and sustainable food systems that promote both human welfare and biodiversity conservation. By creating a secure and transparent record of food production and distribution, stakeholders will be able to better comprehend the environmental impact of food systems and work towards more sustainable practices. In addition, the use of blockchain technology in the food supply chain can help conserve biodiversity by encouraging sustainable agricultural practises and reducing food waste [ 95 ].

The findings of the global food supply chain's main themes of the post-COVID-19 pandemic emphasise food safety, the reduction of food waste, and the conservation of biodiversity. Tracking food products throughout the supply chain, blockchain technology has emerged as an effective instrument for enhancing food safety, quality, and transparency. It is essential to manage the food supply chain sustainably in order to safeguard public health, the environment, and social and economic development [ 96 ].

Another important issue relevant to COVID-19 food supply chain is the gap of strategic intervention in the study. Specifically, the role of the government and country policies in controlling the disruption and ensuring an effective food supply chain that is raised on lean strategic principles is to be considered extensively. For example, only the country's national policy for the food sector to re-design and re-shape their food supply chains before and after the COVID-19 resilience strategy and how it helps to build a smooth food supply chain that is capable of managing the further pandemics. However, the previous study on bibliometric analysis focused on food supply chain safety [ 17 ], and agri-food value chain, this study establishes the novel analysis of the prior study on the COVID-19 food supply chain.

In this study, a total of 2523 papers were published in the field of the COVID-19 food supply chain, and the evolution of the current state of trend during and after the COVID-19 pandemic was scientifically mapped. The COVID-19 food supply chain bibliometric mapping trend was conducted using Vosviewer software and thematic evolution trend analysis using R software to measure the most rising subjects topic. In bibliometric analysis, this study explored key information such as yearly publication trends, article sources and document contents, prolific authors, highly cited papers, most productive institutions, most productive countries, most productive source titles, top authors keyword, co-occurrence, co-authorship network, and country collaboration network. Next, this study highlighted the thematic evaluation, and thematic maps provide an opportunity in specific areas related to the COVID-19 food supply chain pre, during and post-pandemic, thus building subject knowledge importance and how its various aspects have been used before and post COVID-19 pandemic. Thus, this contribution to more empirical policy-related research is encouraged to robust the food supply chain and reduce the impact of the COVID-19 pandemic connected to food safety, agriculture production, resilience solution, mitigate supply chain disruptions, and improve sustainability as shown in Figs.  12 and 13 . Another important contribution, this study revealed specific research gaps in the present literature and proposes a scope for the specific research areas to fill these gaps. Finally, a co-occurrence analysis with the author’s keywords is conducted to stipulate the trend of research pre, before and post-COVID-19 pandemic.

One of the limitations of this study is considering Scopus online database which is the main source for bibliometric analysis. In this case, the selection of data sources may limit the search strategy. Future studies are encouraged for considering other sectors to know how the food supply chain of those sectors is impacted by the COVID-19 pandemic by linking articles from other data sources such as the Google Scholar and Web of Science.

As the pandemic has a massive impact on the world's food supply chain, the study is expected to provide new insight by evolving all the related documents published in this field with a systematic review method. Scholars and policymakers can use this research to know current developments in the food supply chain, and adopt various resilience strategies as discussed to mitigate the impact. However, the pandemic has a severe impact on the global food supply chain. The study is expected to provide more insights by compiling all relevant literature published in this domain using bibliometric analysis. Therefore, this study will be helpful for scholars and policymakers to understand what is happening in the food supply chain pre, during and post pandemic, and policymakers may apply different development strategies as explored to reduce the COVID-19 pandemic impact.

Availability of data and materials

All data presented in this manuscript are available on Scopus database using the search query highlighted in the “Methodology” section. Raw data are attached to the manuscript.

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Supplementary Information

Additional file 1: table s1..

Overview of the bibliographic information’s for the food supply chain domain recovered from Scopus database. Table S2. Trends in yearly publications. Table S3. Most productive authors that published 10 and more publications in the food supply chain. Table S4. Top twenty highly cited documents published in the COVID-19 food supply chain domain. Table S5. Most productive source title. Table S6. Most productive countries published twenty-five and more documents. Table S7. Top ten most prolific institutions. Table S8. Top frequent author’s keywords (BEFORE COVID-19). Table S9. Top author’s keywords (AFTER COVID-19). Figure S1. Flow diagram of article searching strategy of food supply chain documents. Figure S2. Framework for bibliometric analysis. Figure S3. The trend of publications per year of COVID - 19 food supply chain. Figure S4. Word cloud of top author’s keywords (BEFORE COVID-19). Figure S5. Conceptual structure map based on author’s keywords (BEFORE COVID-19). Figure S6. Word cloud of author’s keywords (AFTER COVID-19). Figure S7. Conceptual structure map based on author’s keywords (AFTER COVID-19). Figure S8. Co-authorship network among productive authors. Figure S9. Co-authorship collaboration network among countries. Figure S10. Author’s keywords co-occurrence analysis. Figure S11. Thematic evaluation based on authors’ keywords (Pre and Post COVID-19). Figure S12. The strategic thematic map of the author’s keywords (BEFORE COVID-19). Figure S13. The strategic thematic map of the author’s keywords (Post COVID-19). Figure S14. Proposed framework for building robust and resilience global food supply chain in the post-COVID-19 pandemic.

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Kafi, A., Zainuddin, N., Saifudin, A. et al. Meta-analysis of food supply chain: pre, during and post COVID-19 pandemic. Agric & Food Secur 12 , 27 (2023). https://doi.org/10.1186/s40066-023-00425-5

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• COVID-19 pandemic
• Food supply chain
• Bibliometric mapping
• Biblioshiny

Agriculture & Food Security

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Food supply chain management: systems, implementations, and future research

Industrial Management & Data Systems

ISSN : 0263-5577

Article publication date: 16 October 2017

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be useful for academia and industrial practitioners in the future.

Design/methodology/approach

A systematical and hierarchical framework is proposed in this paper to review the literature. Categorizations and classifications are identified to organize this paper.

This paper reviews total 192 articles related to the data-driven systems for FSCM. Currently, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, research on this topic can be expected to increase in the future.

Research limitations/implications

This paper only selected limited number of papers which are published in leading journals or with high citations. For simplicity without generality, key findings and observations are significant from this research.

Practical implications

Some ideas from this paper could be expanded into other possible domains so that involved parties are able to be inspired for enriching the FSCM. Future implementations are useful for practitioners to conduct IT-based solutions for FSCM.

Social implications

As the increasing of digital devices in FSCM, large number of data will be used for decision-makings. Data-driven systems for FSCM will be the future for a more sustainable food supply chain.

Originality/value

This is the first attempt to provide a comprehensive review on FSCM from the view of data-driven IT systems.

• Case studies
• Food supply chain management
• Data-driven systems
• Implementations

Zhong, R. , Xu, X. and Wang, L. (2017), "Food supply chain management: systems, implementations, and future research", Industrial Management & Data Systems , Vol. 117 No. 9, pp. 2085-2114. https://doi.org/10.1108/IMDS-09-2016-0391

Emerald Publishing Limited

Copyright © 2017, Ray Zhong, Xun Xu and Lihui Wang

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Food industry plays an important role in providing basics and necessities for supporting various human activities and behaviors ( Cooper and Ellram, 1993 ). Once harvested or produced, the food should be stored, delivered, and retailed so that they could reach to the final customers by due date. It was reported that about one-third of the produced food has been abandoned or wasted yearly (approximately 1.3 billion tons) ( Manning et al. , 2006 ). Two-third of the wasted food (about 1 billion tons) is occurred in supply chain like harvesting, shipping and storage ( Fritz and Schiefer, 2008 ). Take fruit and vegetables for example, such perishable food was wasted by 492 million tons worldwide in 2011 due to the inefficient and ineffective food supply chain management (FSCM) ( Gustavsson et al. , 2011 ). Therefore, FSCM is significant to save our food.

FSCM has been coined to depict the activities or operations from production, distribution, and consumption so as to keep the safety and quality of various food under efficient and effective modes ( Marsden et al. , 2000 ; Blandon et al. , 2009 ). The differences of FSCM from other supply chains such as furniture logistics and supply chain management are the importance reflected by factors like food quality, safety, and freshness within limited time, which make the underlying supply chain more complex and difficult to manage ( La Scalia et al. , 2016 ). The complexities are significant in the case of perishable products where their traversal time through FSCM and the use warehouses or buffers against demand and transportation variability are severely limited. Additionally, as the coordination from worldwide scale, the complexities have been compounded, thus, the focus from a single echelon such as food production was shifted to the efficiency and effectiveness of holistic supply chain. That means the resources like trucks, warehouse facilities, transportation routes, and workers within the food supply chain will be used efficiently so as to ensure the food quality and safety through effective efforts such as optimization decisions ( Wu, Liao, Tseng and Chiu, 2016 ).

As the development of cutting-edge technologies, FSCM has been widely recognized both by practitioners and academia. Information technology (IT) has brought dramatic improvements to FSCM in terms of automatic food processing like cleansing and packing as well as freshness storage ( King and Phumpiu, 1996 ; Caswell et al. , 1998 ; Wang et al. , 2015 ). However, the discipline of FSCM is still incapable of addressing many practical real-life challenges satisfactorily. The reasons for the inadequacy are attributed to low operational levels from farmers ( Folkerts and Koehorst, 1997 ), information obstacle among different stakeholders ( Caswell et al. , 1998 ), and inefficient decision-making systems/models ( Ahumada and Villalobos, 2009 ). Strategic decision-makers require comprehensive models to increase total profitability while data input into those models are usually ignored in most of traditional myopic models. In order to address current challenges, it is necessary to investigate better approaches to accommodate emerging global situations after taking a critical look at the current FSCM practices and conditions.

This paper selects total 192 articles from 1993 to 2017 by searching the key word “FSCM” in Google Scholar (until November 2016). Special concentration is placed upon the data-driven IT systems which are used for facilitating the FSCM with particular aims of re-designing and re-rationalizing current supply chain to a globally integrated fashion for food industry. Among these articles, there are seven reports from website, 25 papers are case studies, and the others are typical research papers related to FSCM. Most of these reviewed papers are from leading journals such as International Journal of Production Economics (19), European Journal of Operational Research (4), Journal of Cleaner Production (10), Food Control (13), Supply Chain Management: An International Journal (7), Journal of Operations Management (3), British Food Journal (4), etc. Figure 1 presents the selected papers in a yearly view. As demonstrated, there is only a few studies about data-driven IT systems in FSCM in early 1990s. Then, the related papers are fluctuated slightly from 2000 to 2014. Currently, as showing from the prediction curve, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, the quantity can be expected to increase in the future.

This paper categorizes related topics in a hierarchical organization. Figure 2 presents the scope of the review that each focus is dissected to organize this paper. Section 2 talks about the supply chain management for food industry that covers three themes such as frameworks, models, and worldwide movement. Section 3 presents two major IT systems – traceability systems and decision-making systems for FSCM. Section 4 demonstrates FSCM implementations in terms of reported cases and data-driven applications. Section 5 summarizes the current challenges and future perspectives in four aspects: supply chain network structure, data collection, decision-making models, and implementations. Section 6 concludes this paper through identifying some insights and lessons from this investigation.

2. Supply chain management for food industry

2.1 frameworks.

A framework for FSCM is a basis for manufacturing, processing, and transforming raw materials and semi-finished products coming from major activities such as forestry, agriculture, zootechnics, finishing, and so on ( Dubey et al. , 2017 ). In order to identify the relationships among different items, interpretive structural modeling (ISM) was used to establish a hierarchical framework ( Faisal and Talib, 2016 ). This framework helps users to understand the interactions among logistics operators in a food supply chain. ISM-enabled framework was also used to support risk management in identifying and interpreting interdependences among food supply chain risks at different levels such as first-tier supplier, third-party logistics (3PL), etc. ( Colin et al. , 2011 ). It is observed that this framework was proven as a useful method to structure risks in FSCM through a step-by-step process on several manufacturing stages. Information plays an important role in making FSCM more efficient. In order to assess the information risks management, an ISM based framework was proposed by twining graph theory to quantify information risks and ISM to understand the interrelationships in FSCM ( Nishat Faisal et al. , 2007 ). As the global FSCM is emerging with international collaborations, ISM-enabled framework confines to explain causal relationships or transitive links among various involved parties. A total interpretive structural modeling was then introduced to analyze some enablers and barriers of FSCM ( Shibin et al. , 2016 ). In this paper, ten enablers and eight barriers are examined by separate frameworks to further understand the interactions within a dynamic era of globalization FSCM.

Value chains play a critical role in FSCM to benefit the producers and consumers. Stevenson and Pirog (2008) introduced a value chain framework for strategic alliances between food production, processing and distribution which seek to create more value in the supply chain. The proposed framework concerns about food supply chain economic performance that correspond to the organization, structure, and practices of a whole supply chain. Food traceability has been widely used in the last few decades with large number applications. However, frameworks for a general or common implementation are scarcely reported. To label whether a framework with respect to food traceability application, Karlsen et al. (2013) observed that with a common framework, traceability is prone to be similar and implementation processes are more goal-oriented and efficient. Thus, Regattieri et al. (2007) presented a general framework and used experimental evidence to analyze legal and regulatory aspects on food traceability. They designed an effective traceability system architecture to analyze assessment criteria from alphanumerical codes, bar codes, and radio frequency identification (RFID). By integrating alphanumerical codes and RFID technology, the framework has been applied for both cheese producers and consumers.

Currently, coordination in the food supply chain from production to consumption is significant to ensure the safety and quality of various food. Take agri-food supply chain for example, Hobbs and Young (2000) depicted a conceptual framework to achieve closer vertical collaboration in FSCM using of contracting approaches. This work has critical impacts on transaction cost economics by developing a closer vertical coordination. In an international food supply chain, Folkerts and Koehorst (1997) talked about a framework which integrates the chain reversal and chain management model to make vertical coordination. In their framework, an analytical service designed particularly for benchmarking food supply chain projects is used so that an interconnected system of high performance and effectiveness are achieved as an integrated supply chain. Facing a global FSCM, strategic decision-making is important since the profitability of an entire chain could be increased by the holistic efforts from an efficient framework. To this end, Georgiadis et al. (2005) presented a system dynamics modeling framework for the FSCM. In this framework, end-users are able to determine the optimal network configuration, inventory management policy, supply chain integration, as well as outsourcing and procurement strategies. Collaboration is becoming more of a necessity than an option despite some barriers which deteriorate coordination among enterprises in food industry all over the world. Doukidis et al. (2007) provided a framework to analyze supply chain collaboration in order to explore a conceptual landmark in agri-food industry for further empirical research. It is observed that, from this framework, supply chain collaboration is of critical importance and some constraints such as time and uncertainties arise due to the nature of agri-food industry.

Globalization of food production, logistics and consumption have resulted in an interconnected system for FSCM whose models play crucial role in ensuring food products of high and consistent safety and quality ( Choi et al. , 2016 ). In this section, we present related work using various models for considering five major aspects like food quality, supply chain efficiency, food waste, food safety, and value chain analysis. An incomplete list of the leading authors covering these five aspects is shown in Table I . In order to better demonstrate the literature, key contributions for each paper are highlighted at the last column.

From Table I , it could be observed that food quality, supply chain efficiency and food safety are more concerned in these models. And multi-objectives are commonly considered, for example, food quality and safety are integrated in the decision models. However, food waste is specifically looked at without twining with other aspects. Recently, supply chain efficiency and value chain analysis are placed special emphasis since the global FSCM is becoming more and more significant.

2.3 Worldwide movement

Current movements on FSCM from major districts are presented in this section which covers Europe, North America, and Asia Pacific.

2.3.1 Europe

The food industry is the EU’s largest sector in terms of employed people and value added. From one report about the data and trends of EU food and drink industry 2014-2015, the employment is 4.2 million people with 1.8 percent of EU gross value added and the turnover is €1,244 billion ( FoodDrinkEurope, 2015 ). The turnover is increased by 22.32 percent compared with that from the year 2011 (€1,017 billion). Despite the significant increase of turnover, European Commission recently pointed out that the EU food industry is facing a decrease in competitiveness caused by a lack of transparency in food supply chain ( European Commission, 2016 ). In order to enhance global competitiveness, in November 2011, 11 EU organizations like AIM, FoodDrinkEurope, European Retail Round Table (ERRT), CEJA, EuroCommerce, Euro Coop, Copa Cogeca, etc. signed a Supply Chain Initiative document which is based on a set of principles of good practice. After two years, seven EU level associations agreed to implement the principles which have been converted into 23 languages.

Retailers play important roles in FSCM since they are selling thousands of different products each of which has its own supply chain with distinct features and complexities. ERRT, an organization including the CEO’s of Europe’s leading international retail companies conducted a framework of the EU High Level Forum for a better food supply chain that often involves large number of business partners. Under the framework, leading retailers are going to build up a well-functioning and competitive supply chain in maintaining good relationships with their suppliers so as to bring the best and most innovative foods and drinks to the customers ( ERRT, 2013 ). Retailers in EU are also aware that it is their environmental responsibilities to delivery of foods via a more sustainable model by contacting with consumers and suppliers. Thus, in March 2009, in response to the European Commission’s Action Plan on Sustainable Consumption and Production, ERRT set up the Retailers Environmental Action Programme (REAP) which aims to reduce environmental footprint in the food supply chain. REAP not only facilitates the sustainability dialogue with food supply chain key stakeholders, but also stimulates retailers to adopt new FSCM models ( European Commission, 2015 ).

Logistics is a bridge between food retailers and manufacturers. It was reported that, in 2012, there were 24 million people employed in the food supply chain and 21 percent of the employment comes from logistics-related companies ( European Commission, 2016 ). European Logistics Association (ELA) is a federation with over 30 organizations from Central and Western Europe. Recently, in order to achieve green logistics, ELA developed a sustainable supply chain scheme for FSCM ( ELA, 2012 ). From economic, environmental, and social perspectives, this scheme focuses on realistic financial structure, sustainable FSCM, and successful cases implementation which are should be truly sustainable. Take European Logistics Hub, Limburg – a province in the south of the Netherlands for example, high developed logistics facilities and modern logistics infrastructures offer an advanced logistics with lowest supply chain costs and environment impacts ( Hemert and Iske, 2015 ).

Food production as source of FSCM is extremely important in Europe since about 9.12 million people were employed in agricultural industry including planting, harvesting, and so on. There are approximately 1,700 food manufacturers from 13 European countries. European Federation of Associations of Health Product Manufacturers (EHPM) aims to develop a sort of regulatory frameworks throughout the EU for health and natural food. Recently, EHPM is in support of producing the harmonization of health, safety, and qualified aspects for food supplements through an optimization of positive economic impacts on Food Supplements sector in the EU market ( EHPM, 2013 ). Advanced technologies bring large benefit to food industry globally. A food Tech innovation Portal was launched by European Commission to apply innovative technology, such as biotechnology, nanotechnology and information and communications technology (ICT) to help food manufacturers to provide more health, safe, and natural foods ( European Commission, 2014 ).

2.3.2 North America

North America is the second largest food industry in the world with a turnover of about €650 billion in 2013. Take USA for example, from an incomplete report in 2013, there were 40,229 grocery stores with $634.2 billion in revenues, 154,373 convenience stores with $165.6 billion annual sales, and 55,683 non-traditional food sellers with $450 billion turnover ( Global Strategy, 2013 ). Consisting of multi-tiered food supply chains in North America, FSCM is both large and complex so that innovations are highlighted in food industry to meet the steady growing rate of 2.9 percent yearly.

Companies from North America are aggressively viewing new food market with large numbers of potential consumers. Thus, a far reaching and more sophisticated food supply chain is prone to risks caused by disrupted disasters, oil prices’ fluctuations, and political upheavals, which greatly influence food production and transportation ( Lan et al. , 2016 ). Using advanced technologies such as bio-tech and ICT, food production and harvesting are innovatively improved ( Fraser et al. , 2016 ). Genetically modified organisms for instance with higher productivity and stronger anti-viruses are used in plants, mammals, fish, etc. ( Hemphill and Banerjee, 2015 ).

For innovative warehousing of food, robotics and automation have been adopted in North America in food and beverage supply chain. Given the improved efficiencies in terms of sorting, packing, and processing, funding sources, in recent years, have invested in warehouse automation significantly. In 2012, the US Government granted $50 million to research institutes and universities for robotics aligning with creation of the next generation of collaborative robots from the Obama administration’s National Robotics Initiative ( Pransky, 2015 ). With the assistance from robots, warehouses for food and beverage are the most technologically advanced for facilitating FSCM.

Logistics and transportation are innovatively improved from improving the railroad, flight routes, marine and land roads. North America has the comprehensive and satisfactory logistics network. Currently, Genesee & Wyoming Inc. agreed to acquire Providence and Worcester Railroad Company (P&W) for approximately $126 million to meet customary closing conditions following the receipt of P&W shareholder approval in the fourth quarter of 2016 ( BusinessWire, 2016 ). 3PL plays a major role in food supply chain. The top 3PL and cold storage providers in 2016 are AFN, Niles, Ill., Allen Lund Company, La Canada, Calif., and Americold, Atalanta, Ga. who are the top listed companies using latest technologies in transportation management systems, warehouse management systems (WMSs), and logistics scrutiny systems for a better food supply chain services.

2.3.3 Asia Pacific

China, as the third food and drink producer has a turnover of €767 billion in 2011 which is the largest food entity in this area ( European Commission, 2016 ). As the biggest country in Asia pacific, China has around 400,000 food-related companies. Japan with €466 billion turnover between 2012 and 2013 employs 1.4 million workers. India, Australia, South Korea, and New Zealand, as major food producers in this area, their turnovers (2012-2013) are 95, 62, 32, and 27 billion Euro, respectively. It is no debate that this area is the most important food and beverage supplier from its enormous turnovers. However, FSCM in this area is mainly based on sacrificing manpower, for example China used 6.74 billion employees to achieve the total turnover, which is one-third more people than that in the EU.

With small margins attainable in most links of food supply chain in Asia Pacific, consolidation across various food categories and levels of the FSCM was necessary to reduce cost and maximize profits. To this end, a robust logistics and FSCM network program was initiated to enhanced focus on food availability and growing number of organized retail outlets for food supply chain development ( Simatupang and Sridharan, 2002 ). Take India for example, the government proposed a multi-tiered network design plan which upgrade current city/urban and rural supply chain to hyper/mega centers, urban, semi-urban, and rural structure in 2025 by full use of automation, verticalization, and lean principles as well as 3PL innovations ( Venkatesh et al. , 2015 ). Thus, organizations in India are going to rethink their mega food center supply chain models so as to handle higher variety and faster transitions within food supply chain. Yeole and Curran (2016) used tomato post-harvest loses from Nashik district of India for example to demonstrate reduced intermediaries in the supply chain network will save the losses. Additionally, supply chain operations like improper packaging techniques and lack of cold storage facilitates are need to be improved for the network.

Chinese-made food products are prone to be low price, low quality, and low safety ( Roth et al. , 2008 ). The main reason is the weak management in food supply chain. Despite China has the largest number of food companies, most of them are small and medium-sized enterprises (SMEs) which are extremely difficulty for the government to manage. Currently, Chinese Government proposed a set of regulations for ensuring the food safety from various aspects such as GB (Guo Biao – a national standard). Moreover, after some significant food scandals, Chinese Government put more efforts on the supervision of the food manufacturing and distribution ( Lam et al. , 2013 ). Food logistics facilities are also concentrated on from both government and companies since China’s connections to global food markets have important effects on food supply. Unfortunately, weak implementations are needed to be improved although the government has depicted to strength regulation, establish scrutiny systems, reform laws, and increase investment on basic infrastructures in FSCM. It is still far to say Chinese foods are low price, high quality and high safety.

Japan and South Korea always follow the strict monitoring within the total FSCM because they believe that their foods represent their culture. Thus, a food-obsessed country like Japan or South Korea uses national natural cuisine uniquely to reflect the pure environment. Since global integration of food supply chain, companies from both countries adopted supply chain strategies to improve relationship between diversification and a firm’s competitive performance ( Narasimhan and Kim, 2002 ). Food supply chain facilitates from both countries in production, warehouse, and distribution maybe the best in Asia Pacific. Take Japan for example, fishing industry plays an extremely crucial role in Japanese culture. Due to limited space for refrigerators and food storage spaces, its fish supply chain uses time-constraint multiple-layered supply chain network to guarantee freshness and quality ( Watanabe et al. , 2003 ). Recently, these countries moved into a smart FSCM using advanced technologies such as Internet of Things (IoT). Different types of sensors are used to facilitate various operations within entire food supply chain ( Park et al. , 2016 ).

Australia and New Zealand, as major food suppliers for the world, have mature FSCM in terms of consolidation of food industry partners and supply chain integrations. Australia proposed a green supply network where the consumers are able to seek to secure food ( Smith et al. , 2010 ). Recently, the Commonwealth Scientific and Industrial Research Organization launched a digital agriculture plan to help Australian farmers and food industry parties to improve productivity and sustainability. Smart solutions for modern farming and FSCM are placed on specific attention by developing information systems which are used for ingesting, processing, summarizing, and analyzing data from multiple sensor systems ( Devin and Richards, 2016 ). New Zealand with its clean waters, fertile land, and excellent climate is a heaven for producing quality foods. This country is famous for its highly skilled workforce who is generating thousands of foods for the whole world with high standards in food quality and freshness ( Campbell et al. , 2006 ). Besides skilled workers, efficient and effective FSCM also makes the great success of food industry which is the largest manufacturing sector in New Zealand. The Ministry for Primary Industries is the primary food safety regulating authority in New Zealand, aiming to ensure food quality, safety, and reduce risks. Currently, New Zealand planned to take the leading role in global food security by adopting cutting-edge technologies such as Auto-ID which is a key technology of IoT for tracking and tracing animal products like cows and sheep ( Ghosh, 2016 ). As a result, food products from this country could be monitored from sources to consumption phase, which makes real total lifecycle management for each food.

3. IT systems for FSCM

It is no debate that IT systems are essential for FSCM where so many things can go wrong such as trucks, food suppliers, data entry, etc. This section takes the traceability and decision-making systems for FSCM as examples to review the state-of-the-art situations that are useful for practitioners when they are implementing IT-based solutions.

3.1 Traceability systems

Traceability of a food refers to a data trail which follows the food physical trial through various statuses ( Smith et al. , 2005 ). As earlier as two decades ago, US food industry has developed, implemented, and maintained traceability systems to improve FSCM, differentiate foods with subtle quality attributes, and facilitate tracking for food safety ( Golan et al. , 2004a ). Some systems deeply track food from retailer back to the sources like farm and some only focus on key points in a supply chain. Some traceability systems only collect data for tracking foods to the minute of production or logistics trajectory, while others track only cursory information like in a large geographical area ( Dickinson and Bailey, 2002 ).

This section analyzes total 19 key papers published from 2003 to 2017. Table II presents a categorized analysis in terms of tracing objects, technology, district, and features.

From Table II , it could be observed that food traceability is paid much attention from EU where people do care more about the food safety and quality. Associated technologies are developing fast so that cutting-edge techniques are widely used for various food tracing and tracking. Take RFID for example, 73.68 percent of the reviewed papers adopt this Auto-ID technology for food traceability. Moreover, agri-foods are placed special attention to trace and track because as the most important perishable products, their freshness and quality are eyed by the consumers.

3.2 Decision-making systems

Besides the traceability systems in FSCM, other decision-makings such as integration/collaboration, planning/scheduling, fleet management, and WMS are also widely used in food industry. This section presents a review of total 26 papers which are related to the above topics. Table III reveals these papers from 2005 to 2007 with specific decisions, countries/area (identified by the corresponding author), used technologies, and features.

We selected two typical publications in each year for forming Table III from which several observations could be achieved. First, European countries are prone to be more use of systems to assist decision-makings in FSCM. Second, systems used in earlier stage are based on internet solutions. Currently, model-based systems using advanced technologies are widely reported in FSCM decision-makings. Third, focuses of decision-making shift from supply chain integration in earlier years to sustainable and specific problem solving cases in recent years.

4. Implementation of FSCM

4.1 reported cases.

Case studies from implementing various IT systems in FSCM are significant to get some lessons and insights, which are meaningful for industry practitioners and research academia. This section reports several cases using different systems for facilitating their operations or decision-makings in food supply chain from 2007 to 2017. They are categorized in the following Table IV which includes key information like company name, district, system, and improvement.

From the reported cases, it could be observed that, European countries have much more successful cases on using various IT support systems in FSCM. While, cases from Australia, China, etc. are scarcely presented. Another interesting finding is that before 2010, IT systems are used for optimization or supply chain coordination decision-makings. However, currently, companies are more concentrating on the sustainability and environmental performance in the food supply chain. For example, environmental influences like CO 2 emissions and waste reduction are widely considered.

4.2 Data-driven implementations

Data, usually used for decision-makings, have been considered in FSCM for various purposes. Data-driven implementation in FSCM is categorized into two dimensions in this paper. First is the simulation-based modeling which focuses on adopting different data for FSCM optimization or decision-making. The other is data collection from practical implementations for supporting IT systems for various purposes such as traceability, risk assessment, and so on.

For simulation-based modeling, studies mainly focus on establishing various simulation models which adopt different types of data such as product quality, customer demand for different decision-makings and predictions. In order to meet increasing demand on food attributes such as integrity and diversity, Vorst et al. (2009) proposed a simulation model which is based on an integrated approach to foresee food quality and sustainability issues. This model enables effective and efficient decision support on food supply chain design. FSCM is becoming more complex and dynamic due to the food proliferation to meet diversifying and globalizing markets. To make a transparent food supply chain, Trienekens et al. (2012) simulated typical dynamics like demand, environmental impacts, and social aspects to enhance the information sharing and exchanging. It is found that food supply chain actors should provide differentiated information to meet the dynamic and diversified demands for transparency information. As a wide application of Auto-ID technology for tracking and tracing various items ( Zhong, Dai, Qu, Hu and Huang, 2013 ; Zhong, Li, Pang, Pan, Qu and Huang, 2013 ; Qiu et al. , 2014 ; Guo et al. , 2015 ; Scherhaufl et al. , 2015 ), traceability data plays an important role in supporting FSCM. Folinas et al. (2006) introduced a model which uses the traceability data for simulating the act guideline for all food entities in a supply chain. The assessment of information underlines that traceability data enabled by information flow is significant for various involved parties in food supply chain to ensure food safety. Wong et al. (2011) used a model to evaluate the postponement as an option to strengthen food supply chain performance in a soluble coffee manufacturer. The simulation model shows that cost savings including reduction of cycle stock are obtained by delaying the labeling and packaging processes. Bajželj et al. (2014) simulated the food demand to examine the impacts of food supply chain on climate mitigation. This paper proposes a transparent and data-driven model for showing that improved diets and reduced food waste are critical to deliver emissions reductions. Trkman et al. (2010) used a structural equation model based on data from 130 companies worldwide to examine the relationship between analytical capabilities in FSCM. It is observed that the information support is stronger than the effect of business process orientation in food supply chain. Data-driven model was also proposed by developing a measure of the captured business external and internal data for food productivity, and supply chain value ( Brynjolfsson et al. , 2011 ). This paper obtains 179 firms’ data from USA where 5-6 percent increase in their output and productivity by using IT solutions. Low and Vogel (2011) used a national representative data on local food market to evaluate the food supply chain where small and medium-sized farms dominate the market. This paper finds that direct-to-consumer sales of food are greatly affected by climate and topography which favor perishable food production. Akhtar et al. (2016) presented a model by using data collected from agri-food supply chains to examine adaptive leadership performance in FSCM. This paper thus depicts that how global food supply chain leaders can use data-driven approach to create financial and non-financial sustainability. Hasuike et al. (2014) demonstrated a model to simulate uncertain crop productions and consumers’ demands so as to optimize the food supply chain profit. This simulation model is based on stochastic programming that accommodates surplus foods among stores in a local area. Manning et al. (2016) used a quantitative benchmarking model to drive sustainability in food supply chain. Li and Wang (2015) based on networked sensor data worked out a dynamic supply chain model to improve food tracking. Recently, Big Data is emerging as a crucial IT for instructing decisions in food supply chain. In order to differentiate and identify final food products, Ahearn et al. (2016) simulated environmental sustainability and food safety to improve food supply chain by using the consumer demands big data. This paper features a sustainability metric in agricultural production.

For practical data-driven system, various data are captured and collected to decision-makings in FSCM. Papathanasiou and Kenward (2014) produced a top level environmental decision support system by using the data collected from European food supply chain. It is found that socio-economic aspects have more influences on effective environmental decision support than technical aspects. Martins et al. (2008) introduced a shelf-life dating complex systems using sensor data to monitor, diagnose and control food quality. As the increasing focus on healthy diet, food composition and dietary assessment systems are significant for nutrition professionals. Therefore, Pennington et al. (2007) developed a system using the appropriateness of data for the intended audience. Most food and nutrition professionals will be beneficial from educating themselves about the database system. Perrot et al. (2011) presented an analysis of the complex food systems which are using various data such as supply chain dynamics, knowledge, and real-time information to make different decisions in FSCM. Tatonetti et al. (2012) illustrated a data-driven prediction system which is used for drug effects and interactions that US Food and Drug Administration has put great effects on improving the detection and prediction. Ahn et al. (2011) , given increasing availability of information from food preparation, studied a data-driven system for flavor network and food pairing principles. Jacxsens et al. (2010) using actual microbiological food safety performance data designed a food safety management system to systematically detect food quality. The diagnosis is achieved in quantitative to get insight in the food businesses in nine European companies. Karaman et al. (2012) presented a food safety system by full using of data from plants where white cheese, fermented milk products and butter are produced. A case study from a Turkish dairy industry is demonstrated the feasibility and practicality of the presented system. In order to assess the lifecycle for sewage sludge and food waste, a system based on anaerobic codigestion of the organic fraction of municipal solid waste and dewatered sewage sludge was introduced ( Righi et al. , 2013 ). Environmental performances of various scenarios in the NE Italy case studies are evaluated to show energy saving using the data-driven system. Jacxsens et al. (2011) introduced a sort of tools for the performance examination and improvement of food safety management system by the support of food business data. These tools are able to help various end-users to selection process, to improve food safety, and to enhance performance. Food safety management systems usually use traceability and status data to examine food quality and freshness. Tomašević et al. (2013) took the Serbian meat industry for example to report food safety management systems implementation from 77 producers. Laux and Hurburgh (2012) reported a quality management system using food traceability data like maintain records for the grain scrutiny. A traceability index is used to quantify a lot size of grain in an elevator in this paper. Herrero et al. (2010) introduced a revisiting mixed crop-livestock system using farms’ data to achieve a smart investment in sustainable food production. By carefully consider the inputs of fertilizer, water, and feed, waste and environmental impacts are minimized to support farmers to intensify production. Tzamalis et al. (2016) presented a food safety and quality management system used in 75 SME by using the production data from the fresh-cut producing sector. This paper provides a best practice score for the assessment to ensure food quality and safety.

5. Current challenges and future perspectives

This section summarizes current challenges and highlights future perspectives in supply chain network structure, data collection, decision-making models, and implementations.

5.1 Supply chain network structure

Food quality and safety heavily rely on an efficient and effective supply chain network structure. As the increasing globalization demands for more healthy and nutritious food, current structure is facing several challenges. First, the concentration of design and development of a food supply chain network structure is placed upon a sole distribution system or a WMS. Mixed-integer linear programming models are widely used to suggest proper locations and distribution network configurations ( Manzini and Accorsi, 2013 ). An entire and global structure is necessary. Second, optimizations are always considered within a network structure. However, the common considerations are planning, scheduling, profit and cost. Environmental impacts and sustainable performance are omitted. As increasing consumptions of various resources, a sustainable supply chain network structure considering waste reduction and greenhouse gas emissions is needed. Third, with the development of advanced technologies such as IoT, traditional network structure is no longer suitable for facilitating the food supply chain operations because large number of digital devices, sensors, and robots are equipped along the supply chain. Thus, an innovative and open structure for FSCM is required.

An integrated global architecture: the final goal of this architecture is to control global food chain in both optimal and interdependent levels to make involved stakeholders for a closed-loop management and scrutiny. For achieving this purpose, new conceptual frameworks, effective supporting tools, integrated models, and enabled technologies are needed further investigation ( MacCarthy et al. , 2016 ; Talaei et al. , 2016 ).

Sustainable food supply chain: in the future, sustainable business in food industry can be harvested by reducing the environmental impacts, enhancing food waste recycling, and strengthening facilities sharing. New mechanisms and coordinated development along with other industries like manufacturing and economy are basic supports for achieving the sustainability ( Green et al. , 2012 ; Irani and Sharif, 2016 ; Lan and Zhong, 2016 ).

Physical internet (PI) for FSCM: PI is an open global logistics system by using encapsulation, interfaces, and protocols to convert physical objects into digital items to achieve operational interconnectivity ( Montreuil, 2011 ). Using the PI principle, FSCM for food handling, movement, storage, and delivery could be transformed toward global logistics efficiency and sustainability.

5.2 Data collection

Data-enabled decision-making plays an important role in FSCM so that without an approachable data collection method, it is difficult to carry out data-based analytics. Despite wide adoption of data collection approaches used in food supply chain, several challenges still exist so that data-driven decision-makings are confined. In the first place, manual and paper-based operations are common in food supply chain, especially in agri-food logistics. Data from these approaches are usually prone to be inaccurate and incomplete. As a result, decisions based on such data are unreasonable ( Zhong et al. , 2016 ). Moreover, various data collection devices such as sensors, smart phones, and GPS have different data formats that are usually unstructured and heterogeneous. Integration and sharing of these data among the food supply chain are extremely difficult ( Pang et al. , 2015 ). Finally, current data collection system cannot deal with huge number of data capturing in a simultaneous fashion. Due to the limited central calculation capacity and signal transmission methods, data collisions and jams could be happened occasionally.

IoT-enabled smart data collector: this type of data collection method is based on IoT technologies like smart Auto-ID and smart sensors which are designed with multi-functional ability. They are able to collect data under different situations such as temperature-sensitive condition for perishable products or wines. Thus, they are designed in a wearable or flexible way to be easily deployed and operated ( Wu, Yue, Jin and Yen, 2016 ). A certain learnable ability is built upon each collector which is central managed and controlled by a knowledge-enabled super computer that works as human brain to coordinate vast number of collectors.

Adaptive smart robot: these data collectors are specially designed by twining robotics and smart sensors so that they are able to fulfill some operations and capture data in parallel. They are useful in some extremely hazardous environment like super low temperature for ice cream or frozen seafood. Such adaptive smart robot is based on advanced technologies which make it to perform like a human ( Zhong et al. , 2016 ). It can sense environment and adaptively make decisions based on real-time data from the environmental variations.

5.3 Decision-making models

As more and more data aggregated in food supply chain, decision-making models require associated knowledge from such data for more precise and systematic resolutions. Traditional approaches packaged or embedded into decision-making models are not able to deal with Big Data challenges. First of all, decision-making models in FSCM need various data for different purposes such as optimization of planning and scheduling, reduction of waste, etc. However, computational time will be so long that immense data are input into these models. Second, data-driven decision models used for food supply chain optimization do not have evaluation criteria to validate their effectiveness since numerical studies are commonly used in literature ( Meneghetti and Monti, 2015 ). Such approaches may not be suitable under Big Data era. Third, current models are focusing on a specific problem driven by a single company or a particular food supply chain. Multi-functional models are scarcely reported. By making full use of food industry Big Data, multi-objective and generic models could be achieved.

Multi-functional models: these models are able to make full use of Big Data from food supply chain. Some advanced and intelligent models or algorithms like deep machine learning will be integrated into these models so that multi-objectives could be defined ( Balaji and Arshinder, 2016 ). They are capable of selecting associated data for different objective functions through training, learning, and calculating.

Smart decision models: future decision models can work collaboratively in a smart way. With the intelligent learning capability based on Big Data, a number of models will be created to perform smart decisions on real-time basis ( Zhong et al. , 2015 ). Advanced hierarchical or parallel frameworks for these models are required, thus, smart models are able to invite other models for seamless co-operation.

5.4 Implementations

FSCM implementations from real-life industries are based on cutting-edge technologies which are used for addressing some issues faced by food supply chain. Reported cases from literature mainly concentrated on verifying some hypothesis and presenting the improvements after using an IT system ( Canavari et al. , 2010 ; Soto-Silva et al. , 2016 ). Few studies highlighted the natural characteristics of food supply chain or generic issues summarized from a set of companies so that the essence of FSCM could be figured out. After that, suitable technologies can be picked up to work out the solutions for the company or involved parties in food supply chain. Regarding the complexity of food supply chain, some important issues involving waste, re-use of resources, facility sharing, greenhouse gas emissions, and holistic lifecycle management are still unaddressed ( Genovese et al. , 2017 ). Take food waste for example, about 40 percent of total food produced in the USA goes as waste yearly which is equivalent of $165 billion ( Pandey et al. , 2016 ). Such vast wasted food not only physically influences our environment by polluting the water, but also significantly increases the CO 2 emission since large number of pollution will be generated when they are deteriorating. Thus, reduction of food waste requires the actions at different echelons within food supply chain like food production, delivery, storage, retailing, and recycling. Regarding different echelons, associated solutions such as food production management system, WMS, logistics management system, etc. should be highly integrated in terms of data sharing and seamless synchronization.

Emerging cutting-edge techniques may contribute to system integration in the near future. First, Cloud technology has been used to integrate segregated sector using minimum resources. It allows involved stakeholders to access various services via software as a service, platform as a service, and infrastructure as a service ( Singh et al. , 2015 ). Through Cloud-enabled solution, the information sharing and collaborative working principle could be achieved by using basic computing and internet equipment. Second, IoT technologies like Auto-ID and smart sensors have been widely implemented in manufacturing and aerospace industry ( Zhong, Li, Pang, Pan, Qu and Huang, 2013 ; Whitmore et al. , 2015 ). IoT-based solutions for FSCM are able to provide an entire product lifecycle management via real-time data capturing, logistics visibility, and quality traceability. Additionally, within an IoT-based environment, every objects with sensing, networking and calculating ability can detect and interact with each other to facilitate logistics operations and decision-making in a fashion that is ubiquitous, real-time, and intelligent. Third, Big Data Analytics for FSCM has received increasing attention since it is able to deal with immense data generated from food supply chain. Big Data Analytics can help food companies to make graphical decisions with more accurate data input by excavating hidden and invaluable information or knowledge which could be used for their daily operations. With such information, ultimate sustainable food supply chain could be realized by optimal decisions.

In the future implementation, giant companies play important roles in leading the food supply chain toward a green and sustainable direction. To this end, collaborations with green relationships could lead to a win-win situation that large companies will get the economic benefits, and in turn the food supply chain members like SMEs could also be benefited. That green relationship is based on the joint value creation by using new business models in terms of internal and external green integration which will be enabled by advanced technologies ( Chiou et al. , 2011 ; Gunasekaran et al. , 2015 ). So these companies may take initial actions to be equipped by advanced IT systems, while up-stream and down-stream parties within food supply chain can follow up for a green future.

Finally, the implementations need the involvement of government bodies which are going to work out strategic plans for guiding and supporting various enterprises toward a better future. Thus, Big Data Analytics is extremely important for these bodies to figure out up-to-data statistics report, current status of a food supply chain, and industrial feedbacks. Further to identify the strategies, they can use advanced prediction models or data-driven decision-making systems for assisting deeper analysis. As a result, each individual end-user could be beneficial from future implementation.

6. Conclusions

As the increasing awareness of food quality, safety, and freshness, FSCM is facing ever pressure to meet these requirements. How to upgrade and transform current FSCM to suit the ever increasing demands in the future? This paper presents a state-of-the-art review in FSCM from systems, implementations, and worldwide movements. Current challenges and future perspectives from supply chain network structure, data collection, decision-making models, and implementations are highlighted.

advanced technologies like Big Data Analytics, Cloud Computing, and IoT will be employed to transforming and upgrading FSCM to a smart future;

data-driven decision-makings for FSCM would be adopted for achieving more sustainable and adaptive food supply chain; and

FSCM implementations will be facilitated by the cutting-edge technologies-enabled solutions with more user friendliness and customization.

Number of articles in a yearly view

Organization of this review paper

List of models for FSCM

Traceability systems for FSCM

Decision-making systems for FSCM

Reported cases using IT systems in FSCM

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Food supply chain transformation through technology and future research directions—a systematic review.

1. Introduction

2. literature review, 2.1. rubrics of food supply chain, 2.2. effect of pandemic disruptions on food supply chain, 2.3. conventional food supply chain and issues, 2.4. application of internet of things (iot), big data & blockchain in fsc, 2.5. blockchain in fsc, 2.6. artificial intelligence (ai) and machine learning (ml) in fsc, 2.7. digital twins & cyber-physical systems in fsc, 3. methodology, 5. bibliometric analysis of food safety, quality, and sustainability using keyword coupling, indexed keyword coupling, 6. discussion, 6.1. effect of current pandemic on fsc, 6.2. technology and food sustainability, 6.3. scope for circularity in food supply chain and waste management, 6.4. technological adoption in fsc and challenges, 6.5. role of technology in food relationship strategies, 6.6. food supply transformations through technology, 7. future research on technological inclusions for food supply-chain transformation and innovation, 8. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Abideen, A.Z.; Sundram, V.P.K.; Pyeman, J.; Othman, A.K.; Sorooshian, S. Food Supply Chain Transformation through Technology and Future Research Directions—A Systematic Review. Logistics 2021 , 5 , 83. https://doi.org/10.3390/logistics5040083

Abideen AZ, Sundram VPK, Pyeman J, Othman AK, Sorooshian S. Food Supply Chain Transformation through Technology and Future Research Directions—A Systematic Review. Logistics . 2021; 5(4):83. https://doi.org/10.3390/logistics5040083

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• Published: 10 October 2022

Transforming agrifood production systems and supply chains with digital twins

• Asaf Tzachor   ORCID: orcid.org/0000-0002-4032-4996 1 , 2 ,
• Catherine E. Richards 1 , 3 &
• Scott Jeen 3 , 4  

npj Science of Food volume  6 , Article number:  47 ( 2022 ) Cite this article

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• Agriculture
• Engineering

Digital twins can transform agricultural production systems and supply chains, curbing greenhouse gas emissions, food waste and malnutrition. However, the potential of these advanced virtualization technologies is yet to be realized. Here, we consider the promise of digital twins across six typical agrifood supply chain steps and emphasize key implementation barriers.

Agrifood production systems and supply chains are currently not on track to meet the sustainable development goals. They are wasteful and polluting, breach several of the so-called planetary boundaries, and fail on their most basic premise to provide an expanding global population with safe and nutritious diets, leaving some 900 million people undernourished 1 .

As a response, transformation through digital technological innovation is often proposed 2 , 3 . In such proposals, computer-enabled technologies, including smart sensors, artificial intelligence (AI) and other embedded systems, are fundamental. Here, we consider the promise of digital twin (DT) technology, which despite its potency and increasing diffusion across industrial domains has not been considered for the purpose of improving agrifood sector sustainability, namely through mitigating malnutrition and undernutrition, reducing greenhouse gas (GHG) emissions and preventing food waste. We then discuss enabling and disabling factors for achieving this yet-to-be-realized potential of virtualized agrifood value chains.

Advantages of virtualized agrifood systems and supply chains

DTs are virtual representations of living or non-living physical entities. Enabled by improvements in computing capabilities, they exist in silico, that is, as computer-simulated models 4 . Deployment of sensors that detect biological, chemical, and physical properties of objects in real-time, ensures that the digital counterparts of these measured objects are accurate and ‘live’ 5 . In such cyber-physical architectures, changes that occur in the physical system are modifying its virtual twin simultaneously and continuously.

With origin in experimental designs of satellites, spacecrafts, city infrastructures 6 , and civil engineering writ large, in recent years, DTs have been re-purposed to address predicaments such as climate change and extreme weather, in complex natural environments 7 , 8 .

By simulating the state of physical systems, DTs can be queried using advanced modelling techniques to uncover optimal behaviour. Reinforcement Learning (RL), a subfield of AI that enables autonomous agents to make decisions in complex systems 9 , can be deployed in DTs to advise optimal control strategies to the physical counterpart. RL agents take the current state of a system as input, and predict future action sequences that optimize system behaviour. DTs allow agents to simulate many control sequences to determine which aligns best with the control objective before advising the physical system.

Combining virtual replicas with such advanced decision-making technologies will have profound transformative implications for the agrifood sector 10 , offering possible remedies to the problems of malnutrition, GHG emissions, and food waste. To appreciate these prospects, we acknowledge potential applications across six supply chain steps: (a) agricultural inputs, (b) primary agricultural production, (c) storage and transportation, (d) food processing, (e) distribution and retail, and (f) consumption (Fig. 1 ).

This diagram indicates potential or possible 17 benefits of digital twins in reducing greenhouse gas emissions, food waste and malnutrition, spanning six steps of a typical agrifood supply chain, as presented and discussed in this paper.

Inputs for agricultural production

Agricultural inputs commonly refer to agro-chemicals, such as nitrogen (N) and phosphorous (P) fertilizers, pesticides, and crop seeds, which are essential for yield productivity. The carbon footprint involved in the manufacture of these inputs is considerable. For example, CO 2 emissions of N fertilizer production in China is estimated at 452 Tg CO 2 -eq, constituting 7% of total GHG emissions from the Chinese economy. Measures to improve heat conversion efficiency in power plants supporting N fertilizer manufacturing are recognized as an essential intervention to lower carbon intensity 11 . In this context, ‘virtual power plants’ could be developed and used by RL agents to find control policies that maximize electricity generation whilst minimizing CO 2 emissions 12 .

DTs proven to operate at the molecular, cell, tissue and organ levels 5 can enable precise simulations of crops. New ‘virtual crops’ could be rapidly stress-tested in computer laboratories under alternate conditions, including precipitation, temperature and salinity, to discover desirable traits and risk factors. While genetically modified organisms (GMO) are currently precluded in some jurisdictions, including the European Union, in the face of a shifting climate niche 13 such laboratories could prove useful in supporting seed improvements for climate-resilient staples.

Primary agricultural production

Beyond the organ level, virtualization of entire farming systems that replicate atmospheric factors, geomorphological processes and edaphic conditions, including soil microbiology, would support precision agriculture at unprecedented scales. Such DTs are likely to use cameras and sensors to sample humidity, moisture content, temperature, irradiance, irrigation and nutrient supply as often as every minute. The digitalisation of agricultural production has the potential to revolutionise problems in animal health, farming resource efficiency and biodiversity loss 14 , 15 , 16 .

DTs can be used to actively monitor livestock well-being using facial recognition technology that infers emotion from ear positions and pupil dilation 17 . Others can track soil water content, solar irradiance, and weather conditions, then be used to predict the nitrogen response rate (NRR) of pasture dry matter and monitor soil conditions 18 .

RL agents could use these DTs to generate synthetic data for training, then find policies that recommend irrigation, lighting and nutrient dissemination to minimize resource-use whilst maximizing crop yield 19 .

Moreover, DTs may promote rewilding, sediment trapping and additional nature-based solutions for land management and restoration 20 , through rapid experimentation in ‘virtual farms’. In silico ‘what-if’ simulations could elicit further benefits, such as testing and identifying pathways to increase carbon sequestration in croplands and pastures, or using agro-forestry techniques, such as integrated green belts for wildfire prevention.

As in other domains, including water and electricity infrastructure, DTs can support predictive maintenance 21 , for instance, of irrigation systems in plantations to minimize food losses. In intensive controlled environment agriculture (CEA), such as commercial aeroponic greenhouses and hydroponic systems, DTs may be used in structure design and operations to suggest optimum light intensity, humidity, temperatures, CO 2 concentrations and water-nutrient recycling.

Storage and transportation

Commodity chains that connect local produce to markets typically involve transit in freight trains and bulk carriers as well as temporary storage in terminal elevators. In rail, road and sea vessels, and in storage silos, cargos of grain are susceptible to mold, mustiness and early germination.

Ventilation management is essential to prevent dampness and fungal infestation, such as Aspergillus and Penicillium that frequently deteriorate the quality of cereal bulks 22 . DTs already employed for improved HVAC systems design 23 could be re-purposed to this end. In addition, real-time replicas of stationary elevators and vessels on voyage could track ventilation periods and moisture content of cargo as well as provide early warning of mycotoxin contamination that warrants fumigation.

DTs can monitor fruit quality during inter-continental shipping 24 . Combining live temperature measurements with mechanistic models, such DTs can predict parts of the fruit that will perish before delivery. Amalgamated insights from many of these DTs can provide insights into transportation conditions that reduce food quality, informing new delivery strategies that limit food waste.

In cold chains of perishable produce, where fruit, vegetable, dairy, meat and seafood products are pre-cooled and provisionally stored in refrigerated facilities, computer simulations may advise on energy efficiency measures to reduce carbon emissions. Synchronized DTs can monitor food temperatures, humidity, delivery schedules, respiratory behaviour, and grid carbon intensity; RL agents can then optimize the control of cooling equipment to draw power from the grid when carbon intensity is lowest to minimize emissions whilst maintaining food quality.

Food processing

Paired with sensing technologies, DTs can be integrated across food processing and packaging facilities that convert agricultural commodities, such as corn or cattle, to ingredients and end-user food products, including tinned vegetables, meat cuts, ready meals and confectionery 25 .

Food loss and waste in this echelon are prevalent in both developed and developing regions, with implications for food security and the environment. In the UK, for example, food waste in this echelon stands at five megatonnes each year 26 .

Here, DTs can support industrial ecology approaches to prevent food loss, in the same way they have been used to enhance circular economy applications in construction manufacturing 27 . DTs can be deployed in smart manufacturing plants to monitor ingredient delivery schedules, plant throughput, ingredient wastage, operator work schedules and demand forecasts. RL models can then be trained to manipulate manufacturing equipment to match food processing to expected demand whilst minimizing waste 28 .

Distribution and retail

Food distribution networks are significant contributors to global GHG emissions, with food retail alone responsible for ~0.3 gigatonnes of CO 2 annually 29 . Food discarded in this echelon is considerable too, for example, with estimates suggesting 366 kilotonnes of food waste per year in the UK 26 . These losses are attributed to inefficient warehousing, hypermarkets and supermarkets operations including shelf management and failure to monitor and measure food waste 30 .

DTs that track construction-site logistics 31 could be repurposed to mimic food distribution systems, and used to optimize delivery schedules minimizing carbon emissions and food wastage. Such DTs could monitor the location of delivery vehicles across the road network, food inventory in retail stores, food embodied emissions traffic, weather and shelf-life of food in transit.

DTs can model the cold chain end-to-end to provide retailers with a better understanding of food quality when it arrives in-store 32 . Here, live temperature readings inform physics-based food models to track quality throughout distribution. By performing sensitivity analyses on such models, and inferring optimised shipping conditions fruit shelf life can lengthen.

Given this state representation, RL agents used to optimise supply chain distribution to maximise producer profit could be repurposed to maximise resource efficiency 33 . Agents could synthesise policies that minimize food wastage, and thus system-level emissions, by sending food to a retailer further from the distribution centre with low inventory levels, rather than a closer store more likely to incur wastage. Recent reviews suggest these simulations could further predict delays in supply chains, signs of food spoilage and potential food losses as well as recommend preventative measures 34 .

Where discard of food surplus is expected, the expansion of DTs to encompass networks of food re-distribution, such as community soup kitchens, can aid in waste mitigation and improving the nutritional security of vulnerable populations. Such expansion may also include growers to more effectively apportion and dispense unharvested produce.

Consumption

Malnutrition, which currently afflicts over two billion people, arises from deficient, excessive or imbalanced consumption of macro- and micro-nutrients. Insufficient intake of iodine and iron, for instance, may lead to anaemia. Overconsumption of carbohydrates, for example, can result in increased risk of cardiovascular diseases.

One recent and emerging approach to the predicament of malnutrition is nutrigenetics. This field of research proposes that individuals’ genetic profile and microbiome determines their metabolism, nutrient requirements, predisposition to nutrition-related diseases such as type 2 diabetes, and response to dietary interventions 35 . To the extent that DTs could, in the future, simulate individual persons 36 , by combining omics data, including nutrigenomics and metabolomics, and drawing on medical and lifestyle records, including via IoT wearable devices, virtual representations of humans could generate scenarios on the health effects of their food choices, customize dietary interventions and transform preventive healthcare thereby reducing malnutrition.

Enabling and disabling factors for virtualized agrifood value chains

‘Live’ DTs offer comprehensive computational ecosystems for simulating crops, farms, agricultural equipment, storage facilities, processing factories, and distribution networks. Nevertheless, agrifood stakeholders must be cognizant of at least four techno-economic limitations currently associated with the deployment of DTs.

First, robust virtual replicas rely on two elements: (a) appropriate sensor coverage and (b) model uncertainty quantification. For advanced decision-making systems to recommend optimal control strategies using a DT, its sensors must be sufficiently predictive of the agent’s objectives. For example, a DT of an agrifood storage facility could only be used to predict food spoilage if it monitors correlating variables, like temperature, food type and product age. Even with sufficient sensor coverage, the DT can only ever be an approximation of the physical system meaning its state representation and future predictions are uncertain. In response, several authors recommend building DTs using Bayesian methods, but robust methods for dealing with DT uncertainty and decision making remains an open challenge 37 . Deploying DTs that capture uncertainty explicitly is crucial to mitigating these issues.

In the same vein, setting ‘live’ replicas of entire supply chains that encompass re-distribution centres, such as food banks and soup kitchens in lower-income communities, would require hefty investments in data architectures, including cloud computing and on-premise sensors.

However, it is likely that private firms at the forefront of DTs research and development would lack incentive to invest in cyber-physical systems that promote ecological and humanitarian causes, such as agro-biodiversity and food rescue, but yield no direct financial returns. This may stifle the dissemination of DTs for agrifood sector transformation, particularly in areas where digital innovation is needed the most.

Second, current DT technologies rely on low-latency, temporally consistent data streams to inform the model. In practice, sensors fail, or do not log data for periods of time, violating the design assumptions of the DT. If agents are selecting control actions using a model with erroneous sensor data, unsafe behaviour is likely. Designing DTs that are robust to periods when sensor data is inaccessible requires technical innovation and is an important barrier to scaled deployment.

Third, modelling flaws may be introduced in design, through human error in coding or merging error-free but discordant algorithms or data. A small notational error in the code of a computational model used for predictive maintenance of an irrigation system, for instance, could result in ill-informed decisions leading to crop yield failures and produce loss 38 .

Fourth, the lack of common modelling standards for DTs might create compatibility difficulties in integrating separately created models 5 . For example, patching a virtual representation of a new piece of cooling equipment in cold chains, programmed by the manufacturer to monitor temperature in degrees Fahrenheit, into an existing cold chain that regulates temperature in degrees Celsius will result in immediate food spoilage.

Lifting barriers

The barriers currently limiting sizeable and meaningful implementation of DTs across the food sector globally are considerable. In particular, the expertise, methods and infrastructure involved preclude the utilization of DTs in lower-middle income economies—where the greatest number of smallholders operate, rural credit markets are immature, agricultural productivity is low, food spoilage and waste are widespread, and malnutrition is prevalent—much in the same way, Green Revolution technologies have bypassed the most vulnerable 39 .

A concentrated, and inclusive, effort by international and public institutions is essential for the deployment of DTs outside of their origin context in civil and mechanical engineering to fulfil their promise in agrifood sector transformation. Multidisciplinary collaborations involving computer science, agriculture, food and nutrition experts must be initiated.

Nonprofit international research centres, such as CGIAR with its Platform for Big Data in Agriculture, ought to be financed to promote open-access and standardized datasets that could support DTs from molecular to landscape levels, including of orphan crops and indigenous agro-ecologies as well as to develop open-source and secured platforms for agricultural DTs initiatives. Public institutions should further invest in underlying standards and data architectures along value chain echelons, deploy bio-physical and bio-chemical smart sensors, telecommunication networks, and cloud computing to meet the data storage and processing demands of DTs.

Once leading centres have established the fundamental knowledge, skills and methods required, collaborations should then expand with the consultation of diverse stakeholders to facilitate spill-over of DTs across agrifood disciplines, domains and geographies. For instance, it will be essential to develop tailored technical and vocational education and training (TVET) programs, including designated syllabi and simulation software, to build computer science literacy among actors involved in the agrifood sector in different socioeconomic contexts.

Finally, the DTs that already inform scientists and engineers in other domains should be continuously studied to enable agile cross-sector adaptation and robust governance of the technology to achieve agrifood production system and supply chain sustainability. These limitations must be addressed before any promised transformation of the agrifood sector with DTs can be realized successfully and at scale.

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This paper was made possible through the support of a grant from Templeton World Charity Foundation, Inc. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of Templeton World Charity Foundation, Inc.

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Tzachor, A., Richards, C.E. & Jeen, S. Transforming agrifood production systems and supply chains with digital twins. npj Sci Food 6 , 47 (2022). https://doi.org/10.1038/s41538-022-00162-2

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