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Cold Chain Management Using Model Based Design, Machine Learning Algorithms and Data Analytics
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
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In the food industry, there is an increased demand for generic pharmaceutical products and perishable food without compromising with the changes in texture and taste that occur in the transit. With this demand, there is a need for better visibility of products in the logistics network, to minimize wastage, to ensure product integrity, influence productivity, transparently track the fleet and to identify pathogens before a potential outbreak. In Cold Chain Management, information is power: with potentially billions of dollars’ worth of cargo (such as food items, vaccines, serums, tests or chemicals) at stake worldwide. Hence, careful live monitoring, inspection, supervision, validation and documentation of business-critical information is essential. In this paper, we have proposed a framework for Cold Chain Management using Internet of Things (IoT) combined with other technological innovations such as: Cloud Computing, Machine Learning and Big Data Analytics to revolutionize the cold transport industry. By establishing such an architecture, we have tried to monitor, visualize, track and control various platform dependent parameters thereby providing a complete solution across the fleet cycle with assured freshness and palpability.
CitationKhanuja, G., D H, S., Nandyala, S., and Palaniyandi, B., "Cold Chain Management Using Model Based Design, Machine Learning Algorithms and Data Analytics," SAE Technical Paper 2018-01-1201, 2018, https://doi.org/10.4271/2018-01-1201.
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