  Integrating Internet of Things (IoT) technology inside the cold supply chain
can enhance transparency, efficiency, and quality, optimizing operating
procedures and increasing productivity. The integration of IoT in this
complicated setting is hindered by specific barriers that need a thorough
examination. Prominent barriers to IoT implementation in the cold supply chain
are identified using a two-stage model. After reviewing the available
literature on the topic of IoT implementation, a total of 13 barriers were
found. The survey data was cross-validated for quality, and Cronbach's alpha
test was employed to ensure validity. This research applies the interpretative
structural modeling technique in the first phase to identify the main barriers.
Among those barriers, "regularity compliance" and "cold chain networks" are key
drivers for IoT adoption strategies. MICMAC's driving and dependence power
element categorization helps evaluate the barrier interactions. In the second
phase of this research, a decision-making trial and evaluation laboratory
methodology was employed to identify causal relationships between barriers and
evaluate them according to their relative importance. Each cause is a potential
drive, and if its efficiency can be enhanced, the system as a whole benefits.
The research findings provide industry stakeholders, governments, and
organizations with significant drivers of IoT adoption to overcome these
barriers and optimize the utilization of IoT technology to improve the
effectiveness and reliability of the cold supply chain.
