Food supply network disruption and mitigation: an integrated perspective of traceability technology and network structure

Abstract

The 2019 coronavirus disease (COVID-19) epidemic has caused serious disruptions in food supply networks. Based on the case of the remerging epidemic in China, this paper aims to investigate food supply network disruption and its mitigation from technical and structural perspectives. To solve the optimal policy choice problem that how to improve mitigation capability of food supply networks by using traceability technology and adjusting network structure, the occurrence mechanism of food supply network disruptions is revealed through a case study of the remerging COVID-19 outbreak in Beijing’s Xinfadi market. Five typical traceability solutions are proposed to mitigate network disruptions and their technical attributes are analyzed to establish disruption mitigation models. The structure of food supply networks is also controlled to mitigate disruptions. The structural attributes of three fundamental networks are extracted to adjust the network connections pattern in disruption mitigation models. Next, simulation experiments involving the disruption mitigation models are carried out to explore the independent and joint effects of traceability technology and network structure on mitigation capability. The findings suggest that accuracy makes a more positive effect on the mitigation capability of food supply networks than timeliness due to the various technical compositions behind them; the difference between these effects determines the choice decision of supply networks on traceability solution types. Likewise, betweenness centralization makes a positive effect but degree centralization makes a negative effect on mitigation capability because intermediary firms and focal firms in food supply networks have different behavior characteristics; these effects are both regulated by supply network types and exhibit different sensitivities. As for the joint effect of technical and structural attributes on mitigation capability, the joint effect of accuracy and betweenness centralization is bigger than the independent effects but smaller than their sum; the joint effect of timeliness and betweenness centralization depends on networks type; while the positive effect of accuracy or timeliness on mitigation capability is greater than the negative effect of degree centralization; theses joint effects are caused by the complicated interactive effects between technical composition and behaviors of intermediary firms or focal firms. These findings contribute to disruption management and decision-making theories and practices.

Appendix

The average degree is the average number of arcs for all nodes in a network. The metric reflects the complexity of a network as they focus on the number of interactions in the network.

Before giving the notation of betweenness centralization, we must introduce node-level betweenness centrality $C_{B} \left( {n_{i} } \right)$. This item calculates how often the node $n_{i}$ lies on the shortest path in a network and is thus defined as:

$$C_{B} \left( {n_{i} } \right) = \frac{{g_{k,h} \left( {n_{i} } \right)}}{{g_{k,h} }},$$

(1)

where $g_{k,h} \left( {n_{i} } \right)$ is the number of shortest paths between the source and sink nodes and through the node $n_{i}$, and $g_{k,h}$ is the total number of shortest paths from the source node to the sink node in a network.

Betweenness centralization represents the betweenness centrality from an entire network perspective. This metric is determined by calculating the average frequency of each node lying on the shortest path in a network:

$$C_{B} = \frac{{\sum\nolimits_{i = 1}^{n} {\left[ {C_{B} \left( {n^{*} } \right) - C_{B} \left( {n_{i} } \right)} \right]} }}{n - 1}.$$

(2)

Similarly, degree centralization is based on node-level degree centrality $C_{D} \left( {n_{i} } \right)$.

$$C_{D} \left( {n_{i} } \right) = \frac{{d\left( {n_{i} } \right)}}{n - 1},$$

(3)

where $d\left( {n_{i} } \right)$ is the number of neighboring nodes of the node $n_{i}$.

While degree centralization describes the degree centrality of an entire network, which is denoted as:

$$C_{D} = \frac{{\sum\nolimits_{i = 1}^{n} {\left[ {C_{D} \left( {n^{*} } \right) - C_{D} \left( {n_{i} } \right)} \right]} }}{n - 2},$$

(4)

where $C_{D} \left( {n^{*} } \right)$ is the maximum value of $C_{D} \left( {n_{i} } \right)$.