NETWORK-BASED ANALYSIS OF PADDY SUPPLY CHAINS: IDENTIFYING CRITICAL NODES FOR IMPROVED EFFICIENCY
Abstract
Paddy production supply chain is a complicated multi-stakeholder system whereby any disruption in any one of the nodes can have a cascading effect on the entire system. There has been more recent interest in the supply chain resilience, although the use of weighted graph-theoretic models with agricultural networks in developing countries has been under-explored, particularly at the local and community level. This study has filled three gaps: (1) the fact that no weighted network models have been used to measure the strength of the interactions between actors in the paddy supply chain; (2) the fact that no node-level vulnerability analysis has been used based on centrality of nodes in the graph in the context of Malaysian agriculture; and (3) the fact that no node-level vulnerability analysis has been used based on the centrality of the nodes in the graph in the context of Malaysian agriculture. This work uses an artificial dataset based upon statistically grounded distributions parameterized against recorded Malaysian paddy supply chain parameters to model the paddy supply chain as a directed weighted graph G = (V, E, W). It has three weighted centrality measures: Weighted Degree Centrality (WDC), Weighted Betweenness Centrality (WBC), and Weighted Closeness Centrality (WCC). Structural statistics of networks, like density, clustering coefficient, and average path length, are also computed. The sensitivity analysis entails modeling the desired deletion of the most central node, Processing-1, to ascertain the vulnerability of the system. Results confirm that Processing-1 is the most common bottleneck in each of the three dimensions of centrality. Its removal is observed to decrease Farm-1 WDC by 66.7, making the betweenness centrality of Farm-1 decrease to 0, and reducing the average network closeness by approximately 34. These findings will offer quantitative and practical information to interested policymakers and supply chain planners who may be interested in having more resilient agricultural food systems.
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