Leveraging Internet of Things and Machine Learning to Optimize Transportation of Natural Gas

Abstract

With the dawn of globalization and interconnectedness around the globe, supply chains have become incredibly complex. There is often tangle of involved actors that stretches far beyond a simple peer to peer relationship. Not only are there co-dependencies across the entire chain, but there is also a plethora of intermediaries involved in physical movement of goods: Forwarders, insurers, governmental bodies like customs and many more. This complexity makes international transactions a challenging task for involved parties, however, efficient supply chain management is one of the key pillars to business success. By increasing efficiency of business processes and effectively managing the supply chain, companies can create and secure impactful competitive advantages. Unfortunately, many processes are still analog and interdependent streams between physical delivery, supporting documentation and payments are disconnected. This is exactly where technology can support and create chances for optimization and automation. Imagine a world where supply chains can be automated end-to-end, including handovers between multiple parties and the respective back office processes. One event triggers the next and they all are interconnected. The biggest challenge faced by today’s gas industries is transportation of the gas around the globe. Transporting natural gas or liquified petroleum gas (LPG), liquified natural gas (LNG) or oil over large distance can be a challenging task since much care needs to be taken due to its inflammable nature. At the same time, it is necessary to find optimized ways to increase the efficiency of supply chain process without increasing the level of insecurity over transporting the gases. This paper discusses about how Morpheus.Network offers solutions which can help improve efficiency of gas shipments with the background of a refinery based in Canada. Keywords: , , , ,, , , ,