CC21 - Space Apps Challenge

CC21| An Integrated Assessment

An Integrated Assessment

Your challenge is to integrate various Earth Observation-derived features with available socio-economic data in order to discover or enhance our understanding of COVID-19 impacts.

Supply Chain Risk-meter

Summary

The COVID-19 pandemic has been considered as the black swan event and forced companies, industries, and organizations to transform their global supply chain model. We wonder how companies/public organizations can manage the level of supply chain risk and make decisions based on these related factors.

How We Addressed This Challenge

Through three approaches:

Firstly, by combining automated data, such as GPS, oceanic data (related to ocean water level, waves by NASA), air traffic data (by IATA) or maritime AIS data (provided by NOAA), the optimal transport route could be calculated.

Secondly, emissions could be estimated based on the average historical meteorological data. In this sense, companies could try to avoid heavy-emission sections/transportation modes, which means congestion and higher fuel consumption.

Thirdly, the cost display. Since the commercial transportation cost is fluctuating constantly based on the demand, most of the time it is better to choose closer suppliers. In the near future, we expect the cost would be more transparent and customers can know the cost of different transport providers and compare them.

The final output would be a dashboard, where time, environment impact, potential risk/bottleneck section and cost would be included. Hence, companies and organizations could make more cost-efficient decisions based on this information.

How We Developed This Project

Two of our team members have been volunteering in healthcare product supply for Spanish hospitals during the COVID19 outbreak. We discovered the current hospital purchasing model is passively depending on the public bidding process. Therefore, we need better decision making parameters for the hospitals and companies that can supply healthcare products.

When we started the process, we discovered that the supply problem was not only for hospitals but also for manufacturing or other kinds of industry. The sudden closing of the factories or national borders fragmented severely the overall supply chain. We then started looking for influential factors for companies while making decisions of purchasing. We observed pre-COVID time, most of them are using the just-in-time model of supply, while this is not working anymore. They should learn new models such as multi-shoring or next-shoring.

We know that NASA and many other organizations provide important data related to emission and oceanic data. Through emission data, we could deduce facts related to traffic density at different times and the commonly used routes. High emission usually stands for heavy-combustion, and thus dense traffic.

We used Slack and Zoom for communication, G-suite for data visualization, and Photoshop for presentation.

In addition to navigating through the NASA database, we also experienced the hunting of open-data in the different parts of the world. We learned a lot of problems related to the supply chain and how to relate traffic to emissions.