Main Article Content


Although banana is an important fruit in Sri Lanka that gives a good income to farmers. Researchers have indicated that the majority of the crop is damaged from the farm gate to the consumer table. Accordingly, this research was addressed ‘to find the risks of the banana supply chain in Sri Lanka. The Interpretive Structural Modeling (ISM) approach was used in this research to find the risks in the banana supply chain of Sri Lanka. According to the literature survey and data analysis, several risks such as human and personal, production, financial, land prices, biological and environmental, information, political, weather-related, management and operational, agricultural policy, logistics and infrastructure, country’s economic cycle, price or market and input risks can be encountered in the banana supply chain. These risks were categorized into different levels according to the influence they provide to the supply chain. Identifying these risks on a hierarchical model was significant to the administrators, managers and farmers to minimize them effectively to manage the supply chain. The analysis in this study further indicated that production, financial, biological and environmental, weather-related events, political situations, and logistics and infrastructure were the most significant risks. Dealing with these risks can minimize the effect of other risks, improve the income of the farmers and maximize consumer choice.
Keywords: Agriculture, Banana, Interpretive Structural Modeling (ISM), Risks, Sri Lanka, Supply Chain.


Agriculture Banana Interpretive Structural Modeling (ISM) Risks Sri Lanka Supply Chain.

Article Details

Author Biographies

Shanaka Rajakaruna, Dalian Maritime University

Transport Engineering and Logistics Management, Researcher

Alawathuge Wijeratne, Sabaragamuwa University of Sri Lanka

Agribusiness Management, Senior Lecturer
How to Cite
Rajakaruna, S., & Wijeratne, A. (2021). Risks in the Sri Lankan Banana Supply Chain: Analysis through an Interpretive Structural Modeling. Journal of Agricultural and Marine Sciences [JAMS], 27(1), 99–111.


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