XOPYops

Inspiration

Most industrial teams do not buy parts because they enjoy shopping; instead, they have to buy new parts because something is about to fail. Industrial downtime is costly. Companies like Grainger focus on keeping operations running, not just selling inventory. We were inspired by this idea: what if teams could anticipate failures and act before downtime occurs?

What it does

XOPYops is a real-time equipment lifecycle simulation and decision support dashboard.

It simulates six months of equipment operation, showing how much machines wear over time, how failure risk increases, and when action is required. As usage approaches critical thresholds, the system clearly indicates whether a machine is healthy, at risk, or in need of immediate repair.

In addition to serving as a simulator, XOPYops offers live dashboards that allow industrial companies to view the health of their tools in real time.

How we built it

We built XOPYops as a front-end-first simulation experience focused on clarity and real-time behavior. For the frontend, we used React and Typescript for a modular, predictable UI. For the backend, we used Python and FastAPI. To host the data, we used PostgreSQL

Challenges we ran into

One of the biggest challenges was balancing professionalism with an easy-to-understand user interface. With the large volume of data in industrial systems, it is difficult to understand what the data represents and how to present it in a way that is easier for people to understand. Maintaining clarity while making the application's interface understandable was very difficult.

Accomplishments that we're proud of

We are proud to maintain complex data with an easy-to-understand user interface. Beyond the application's user interface, we built a fully controllable six-month simulation with real-time speed controls, created a clear machine lifecycle with states of healthy, warning, and failure, and designed a user interface that explains itself without documentation. Furthermore, we created a live dashboard that monitors the health of all the tools.

What we learned

We learned that clear thresholds, simple language, and visual feedback matter more than complex models in high-pressure environments. We also learned that customers and users value an environment that is controlled, predictable, and understandable, which we had implemented in our application.

What's next for XOPYops

We can integrate real sensor and maintenance data for the live dashboard. Furthermore, we can support multiple facilities and production lines. Finally, we could also connect directly to the interface to purchase the industrial technologies immediately to replace the broken ones.