Intelligent Cold Chain Optimization Using AI and Machine Learning

Abstract

The temperature-controlled supply chain, known as the cold chain, which is very important for retaining quality within perishables such as food or pharmaceuticals and biologicals, is considered to be the most perishable of all goods, where safety and compliance with regulatory standards are concerned. The challenges that cold chain logistics have are many within this temperature variation, and these route efficiencies are compounded by equipment failure, huge amounts of energy wasted, or invisibility all over the network. There is an increasing demand for interpretable AI models that will pass through scrutiny by regulators and win trust from stakeholders. Current high message value in AI for cold chain optimization relates to an increasingly evident global mega- trend in terms of a rising demand for temperature-sensitive products, coupled with aggravated challenges due to climate change and even pandemics. Machines are increasing in each and every aspect of the cold chain by identifying machine learning and artificial intelligence techniques. However, cold chains are undergoing more predictable, explainable, decision-driven, and adaptable, thus improving the overall quality, providing compliance, and reducing waste. The major benefits of combining all the technologies are likely to be considered as the most crucial for the upcoming needs of society. This work introduces the overall benefits which leverages the artificial intelligence in combination with machine learning algorithms. The experimental results ensure that improved efficiency and accuracy will increase the standards of regulatory compliance. These results and discussions will have a major impact on instructional and imperative techniques of artificial intelligence and other data analysis techniques in the cold chain.