Risk propagation and its impact on performance in food processing supply chain : A fuzzy interpretive structural modeling based approach

Purpose The purpose of this paper is to identify various risk drivers which affect a food processing supply chain and to create a map of how those risk drivers propagate risks through the supply chain and impact important performance measures. Design/methodology/approach This study involves experts from food processing companies to elucidate the contextual relationships among the risk drivers and between risk drivers and performance measures. This is used to quantify the relationships and to determine the indirect and overall relationships applying Fuzzy Interpretive Structural Modeling. Findings Three categories of risk drivers which Indian food processing companies need to pay maximum attention to minimize risks are identified. These are supplier dependency and contracting, supplier variability, visibility and traceability and manufacturing disruptions. Analysis shows that collaborating with suppliers and logistics service providers, developing mutually beneficial contracts with them while ensuring that adequate technology investments are made can significantly mitigate risks and consequently improve margins and lead to revenue growth. Research limitations/implications This study has been carried out with experts from large food processing companies in India, and hence, the results cannot be generalized across other types of food processing companies. Practical implications The proposed methodology can help understand the interrelationships between supply chain risks and between those risks and performance measures. Thus, it can help a food processing company to create business cases for specific supply chain risk mitigation projects. Originality/value This study is one of the earliest to create a comprehensive risk propagation map for food processing companies which helps in quantifying the impact the risk drivers have on each other and on performance measures.