<p>This paper primarily focuses on grading barriers that hinder internet-of-things (IoTs) adoption, which eventually promotes sustainable supply chain execution. As countries globally plan to combat climate change, supply chain sustainability is substantial. Digital technology, such as IoT, supports sustainability within supply chains. Still, studies infer that the adoption could be more direct and involve barriers that must be graded for efficient implementation and planning. Previous barrier grading models (i) did not accept natural language-based ratings; (ii) subjective orientation of experts’ weights is not well explored; (iii) hybrid determination of attributes’ weights is lacking; and (iv) personalized grades for barriers are also unexplored. Motivated by these gaps, this article develops an integrated model by considering preferences in the natural language form via double hierarchy fuzzy data (DHFD). Later, the rank sum (RS) approach is presented for determining the weights of experts, and the RS-Cronbach factor is put forward for the hybrid weight calculation of attributes. An algorithm to grade barriers is proposed based on WISP formulation combined with the Copeland method. Finally, a case example from Coimbatore is presented to understand the framework’s usefulness, and sensitivity/comparison reveals the pros and cons of the framework.</p>