Our team was stunned when we learned just how manual the logistics and port industry is. As members of the tech industry, we've been heavily exposed to automation and data-driven processes, and knew there was a wealth of opportunities to bring our knowledge into this untapped space. Specifically, we heard of a process where the same data was manually entered by four different stakeholders who conversed through email and Excel, and thought it was a frustrating, costly, and liability-inducing process we would like to take a crack at.

What it does

Our service provides transparency, across the container supply chain. We've created a tool that allows you to automate the communication processes required during the transportation of containers. We built the tool to support the consumption of the CSV and JSON data format. Our tool can also support XML, but we did not create any scenarios that revolved around the consumption of EDI-like formats.

In addition to this automation process, we track how long it takes for a container to clear specific processes, e.g., how long does it take for a container to actually clear customs? We keep track of this data, and provide analytics, such as the average time this process takes over a given time period i.e., weeks, months, years.

Efficiency starts with shining a spotlight and identifying waste. Our service takes a two-pronged approach by helping with both the automation process (automating manual communication) and the identification process (analyzing process time).

How we built it

Our backend services are built on-top of Azuqua, which is a platform for rapidly building and orchestrating microservices. We were able to expose our backend services as API endpoints, which the client-side

We leveraged Firebase for our database component. Client side, we made extensive use of JavaScript and Node. We used Moment.js for date management and SendGrid for mail management.

Challenges we ran into

The challenge our team faced the most in this hackathon was our complete lack of knowledge in the port and shipping space. Choosing a problem and application was the part we faltered over the most, as we found ourselves either getting to caught up in details and all the research that went with that, or getting too broad because we didn't realize how many parts of a process there was. We were fortunate to have a lot of experts around to help, but this was definitely a learning experience.

Accomplishments that we're proud of

We were really proud of how much functionality and how many integrations we were able to fit into this time. We were able to put together a fully functional tool that demonstrates the extensibilty of our idea and product, despite being a bit focusless in the beginning of this project. In the end, we are most proud to have found a problem space and interesting solution in a space we knew little of prior to this experience.

What we learned

We learned a ton about ports and logistics, of course! Specifically, we learned a lot about the complexities of a large l system that relies almost entirely on manual processes/transactions.

We learned it was very important to not only consider whether or not you could link technologies together to create cool solutions, but whether or not you should. Finding the applicability in an idea can be a challenge.

What's next for Automated Container Insights

The core of our platform is solid and extensible. If we were to work on Automated Container Insights in the future, our focus would be on further productization of the service. The intersection of automation and integration is a great place to be; how these two things intersect with applicability can be trickier. We believe we have a solid base solution, but until put into practice, it's impossible to tell.

Share this project: