EvoTruck
Increasing port terminal productivity with smart trucks.
Overview
As part of an IoT graduate course, I consulted RingCo, a truck and vehicle manufacturer, alongside two team members. Upon project kickoff, RingCo was interested in producing an autonomous tow truck to counteract the nationwide shortage of truck drivers that has decreased productivity for logistics centers and the entire supply chain. I dived into the logistics industry, targeting shipping ports, to suggest a new experience that RingCo could use as a stepping-stone to releasing fully autonomous trucks in the future.
For this project, I...
- assessed the port industry and noted the requirements for automation
- identified personas for port stakeholders
- built artifacts like user journey maps and wireframes
- validated the prototype
My team followed the double-diamond framework over a period of 10 weeks, progressing through the four key phases of discover, define, develop, and deliver.
Environmental Analysis
I first investigated the port industry using the four teardrop framework for business model environments. Major trends and influences were categorized by market forces, industry forces, key trends, and macroeconomic forces; the most pressing insights were the significant increase (and continual increase) in imports post-COVID, the low percentage of US ports implementing automation, and the lack of communication within the port terminal hierarchy.
Secondary Research
To pinpoint precise problems, I tuned into industry podcasts, scoured trade journals and articles, and scrubbed YouTube videos created by port crane operators and truck drivers. Afterwards, my team coded our findings into six categories that correspond with different challenges and goals. These categories were: lack of resources, lack of coordination, port organization, executive goals, backlogged processes, and port reputation.
To empathize with port stakeholders, I composed two user stories for crane operators and terminal managers, while a teammate worked on a truck driver's routine. These stories summarize the daily jobs, goals, and frustrations of prevalent user groups.
Narrowing the Scope
Zoning in on RingCo's future goal of developing an autonomous tow truck, my team selected truck drivers as our target users. We searched further into their daily routines, transport schedules, and communications with management. I questioned a FedEx delivery driver about his thoughts on scheduling and learned how FedEx promotes efficient operations. These findings were integrated with the user story to introduce our yard truck driver persona, Jordan.
In an effort the clearly communicate Jordan's key pain points, I constructed a user journey map.
Problem Statement: How might we help increase productivity of yard truck drivers while reducing cognitive load?
Prototyping and Testing
When ideating, my team listed key features to build out the minimum viable product (MVP). At the end of brainstorming, we had three distinct ideas and pitched them to RingCo. In two weeks, we had narrowed our idea down to a smart system centered around five capabilities:
A team member and I produced wireframes that were presented to a truck driver and a team of designers for critique. All testing participants shed light upon areas of redundancy and questioned whether the tablet and software would divert attention from driving. The main takeaways from solution validation were:
Job/Task View
- Only show one job at a time to reduce cognitive load
- Narrow down on job details
- Remove stop and start buttons
Yard Navigation
- GPS would not be needed for experienced drivers; it could be distracting for those not needing direction
- Map could display location of other drivers, key locations around the yard, or container locations
- GPS design should promote memorization of locations
Proximity Detection
- Camera view should be full screen
- Drivers would benefit from better environmental input, like sensors around the yard to update where containers are stacked
Solution
After ten weeks of research, design, and iteration, EvoTruck was pitched as a solution to communication, scheduling, and navigation problems for yard truck drivers.
Its capabilities are possible through an IoT ecosystem, consisting of cameras, RFIDs, NFC tags, and proximity sensors. A standard yard truck can be transformed into an EvoTruck by equipping it with two cameras (one on the back of the truck and one on the chassis), two RFID sensors on the truck's backend, two NFC tags on the front of the chassis, and four proximity sensors around the truck's cab.
Inside the truck sits a tablet with the EvoTruck software pre-deployed; this software is the crux of the solution, as it assigns drivers to jobs, displays navigation, notifies about changes in the schedule, and shows the live camera feed. Our client has an ongoing partnership with a software development company, however these details will not be shared in regard for their privacy.
Results
In short, EvoTruck transforms regular tow trucks into smarter vehicles and alleviates key truck driver pains.
The EvoTruck solution does more for ports than just increase efficiency; it betters communications and container transparency, collects data for yard organization, allows ports to make more money each day, and acts as a stepping stone for terminal ports to advance to Industry 4.0, where more machinery and processes will be automated.
Future Considerations
If I had more time to iterate and further develop EvoTruck, the areas I would explore are...
- Container data: Is using location tracking for container transparency enough to keep the Yard Management System updated? What other data would the EvoTruck system need to collect?
- Driver experience: Would experienced drivers want the GPS navigation? How can this be turned off? What customization options will drivers of varying experience levels be offered?
- Integration: What other automated processes could EvoTruck pair with? Is there an opportunity to implement a similar ecosystem in cranes and drayage trucks?
A special thank you to my teammates, Anurag Harishchandrakar and Sagar Mhatre, mentors, Terri Wada and Scott Kiekbush, and clients, Tricia and Chad Ringer.
Freeze Release
