Cold Chain Management Using Model Based Design, Machine Learning Algorithms and Data Analytics

2018-01-1201

04/03/2018

Event
WCX World Congress Experience
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-1201
Pages
6
Citation
Khanuja, 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.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-1201
Content Type
Technical Paper
Language
English