Ranking of collusive behaviour in Indian agro-supply chain using interval 2-tuple linguistic TOPSIS method

Purpose The aim of this paper is to develop a model for the prioritization of collusive behaviours within Indian food grain supply chain (FGSC) to enable government authorities, entrusted with the task of public distribution, to address those frauds based on their priority for making an existing supply chain more sustainable. Design/methodology/approach An interval 2-tuple linguistic Technique for Order Preference by Similarity to Ideal Solution (ITL-TOPSIS) method has been used to deal with the problem of prioritization of frauds under incomplete and uncertain information. Unlike traditional methods, this methodology offers an ability to make informed decisions, without loss of information, while factoring in various ambiguities. Findings The outcome indicates that the most severe fraud is adulteration, which adversely impacts the health of a person. Bogus Ration Card comes next, as it results into the distribution of grains to non-poor, ineligible population rather than the deserving beneficiaries. Next is diversion, where diverted food grains end up being sold at much higher rates than specified subsidized rates. Theft is least severe, as this would not affect FGSC much until done on large scale. Research limitations/implications More decision-makers can be consulted to entertain more uncertainty and ambiguity. Also, a comparative study can be performed using different methodologies. Practical Implications The proposed modelling could empower various governmental and non-governmental regulatory bodies in formulation of food policies to effectively tackle the problem of inappropriate delivery of food to the unintended population and to take necessary informed decisions for ensuring food security and safety to the society at large. Originality/value There is a dearth of studies related to the prioritization of frauds within FGSC. This research bridges the gap in literature by providing a decision-making framework for prioritizing collusive behaviour under ambiguous and uncertain information.