Deep Learning Based Cold Chain Distribution Model with Blockchain for Supply Chain Management

Transportation and storage regulations are very stringent in the pharmaceutical cold chain, a subset of the logistics sector. The primary function of an integrated product supply chain manager is distribution. The pharmaceutical business places a premium on the drug distribution process because of the importance of ensuring the safe handling, storage, and distribution of medical items. False medication circulation by affiliated parties is an issue in the pharmaceutical sector. This includes data collecting for unregistered pharmaceuticals disseminated by distributors or unauthorized or unregistered distributors. Cold chain logistics for pharmaceuticals are expensive. Hence, this paper proposes a Deep Learning based Cold Chain Distribution Model (DL-CCDM) to manage the supply chain. This study presents a DL-CCDM that utilizes blockchain technology, cloud storage, and the Internet of Things (IoT). The objective is to ensure customer safety and make the whole life cycle of pharmaceutical goods trustworthyly traceable. The next step is to help with cold chain inventory management decisions by building a deep learning method using the high-quality and large-scale data generated to reduce the cost of cold chain product warehousing. This method may help the government guarantee the public's health and safety by ensuring the medications they purchase are of good quality, which improves community health and safety and trust in pharmaceuticals.