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

Paper #:
  • 2018-01-1201

  • 2018-04-03
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 and transparently track the fleet to spot 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, Model Based Design, Machine Learning and Big Data Analytics to revolutionize the cold transport industry. We have presented a novel approach to control and monitor the GPS based Fleet Management System. Various Wireless Sensor Nodes have been integrated to acquire real-time parameter dependent values to ensure the quality of food is intact. By establishing such architecture, we have made an attempt to monitor, visualize, track and control various platform dependent parameters thereby providing a complete solution across the fleet cycle with assured freshness and palpability. A series of experiments have been conducted to compile this given set of data which are subsequently used to train using autoregressive integrated moving average model that predicts these parameters as well as future values from the current ones. The use of optimization algorithms along with predictive modeling has allowed us to put a check on these parameters thereby assuring cold chain integrity.
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