Shipping, Cryptocurrency, and Data Forecasting

Today, we had several meetings and lectures at the University of Nicosia (UNIC). First, we met with Fleet Management Limited, a large company that specializes in shipping around the world through managing the ships of their customers. They fall in the supply chain at an area between production and consumer. Fleet Management Limited works to send completed products to their final customer destination, natural resources to companies to refine them, and anywhere in between. Additionally, we learned about the major ports and canals around the world and how they impact the shipping industry. Some fun facts include Shanghai being the largest port in the world, and the Panama Canal cutting the travel from New York City to San Francisco by around 8,000 miles at sea.

Next, we received a lecture about blockchain and cryptocurrency. I must admit that before this lecture, I knew next to nothing on this topic. However, Dr. Polemitis did a fantastic job of explaining a future world involving cryptocurrency such as Bitcoin. Instead of relying on other people to approve exchanges, people can trade their cryptocurrencies themselves without a third party backing, such as banks. Additionally, I found the argument about twitter becoming non-private company, and instead operating in a manner similar to email very interesting. Dr. Polemitis talked about how Twitter is a monopoly for its business, with the opportunity to exclude any individual from the world of tweets in an instant, such as Donald Trump. Should one person, or a board of people, have the power to decide who can use Twitter, a huge part of today’s culture? Or should it operate similar to email, where anyone can sign up and choose from a variety of services?

Lastly, we received a lecture about data forecasting from Dr. Makridakis. Data forecasting is an important part of the supply chain, for this science predicts the behaviors of customers to allow companies to be better prepared for future demand. For example, Walmart needs to have a good prediction of how many people will be buying milk in their Chicago stores during the first week of June in order to have full shelves for buyers. However, they do not want to overstock and have the milk spoil due to surplus, as that is a waste of both money and resources. Dr. Makridakis explained how he created a series of competitions to incentivize teams to work on improving data forecasting for a variety of situations in order to maximize correctness of predictions along with a good estimate of their calculation errors.

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