Case of Study
OneMine
Mining Industry UX (2015 - 2017)
Context
Mining in Chile contributes 15% to the country's GDP and 60% of total exports, with copper being a major mineral.
At Mining TAG, where I worked, we had a simple presence detection system in underground mines, including the El Teniente mine, located in the mountains near the city of Rancagua.
Challenge
Underground mining is very complicated. As people are working extracting the dirt, rock and minerals from tunnels that can collapse at any minute if the daily operations are not being managed correctly by not having the updated information at all times.
The mine needs to be exploded uniformly across all its extension in order to avoid landslides and rock burst. This is why it's so important to have real-time data in these kind of environments.
Goal
At El Teniente mine, the company had already deployed an underground presence detection system (for people and machines) using tags and antenna's portals as the main hardware. Later, the same devices were implemented into production zones, to track the movements and work of the heavy machines in there.
The mission was to turn the raw data we were getting into a powerful tool that would enhance operations, improve data visualization, and serve the diverse needs of our users (who we were still getting to know).
My Role
My job was to help make sure that the company's projects were successful by using my skills in two different teams: development and innovation.
As part of the Innovation team, I conducted user-centered research (onsite observations, interviews, physical surveys) to discover user pains, goals, demography profile and other relevant details which we would later use to build role based user stories.
On the UI-development side, I created low fidelity mockups to iterate with users and clientes as well as high fidelity mockups to test them and bring designs to life. These visual aids led to polished end-products and improve human-machine interaction.
Process
In creating the system, the team had some knowledge of their users, but further research was crucial due to the innovative nature of the data analysis approach. We started with field visits, observing existing procedures, studying the workforce, and collecting information about their tools.
Our research involved on-site observations, interviews with various personnel, surveys, and card sorting activities. Additionally, we conducted meetings where we presented mockups to volunteer users from different roles, enabling validation and iterative refinement before implementation.
Research Results
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Outcomes
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What I learned
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