Modernizing Massachusetts' Emergency Response: The TTSP Overhaul

The TTSP system, critical for tracking emergency response trailers and their contents (oil spill booms, medical kits, etc.), was built on an old, insecure version of Django that could not be upgraded. The client, MassDEP, was facing a potential system failure and had no way to analyze historical data to forecast budget needs for equipment repairs or supply restocking which are critical to coastal emergency response events. Furthermore, the application's UI was inefficient, did not match the field operators' real-world inspection workflow, and required 3-4 times the necessary clicks and navigation shifts to complete tasks.

The project required a partner who could not only fix the immediate issues but also provide a long-term strategic vision. The key objectives were:

• Provide a clear, expert recommendation on the "upgrade vs. rebuild" dilemma for the legacy system.

• Conduct a deep discovery and design process with field operations teams to redesign the UI and data model to match their real-world workflows.

• Build a new, secure application from scratch that enabled historical data analysis, predictive modeling for equipment failure and supply needs, and streamlined reporting.

• Implement a modern, scalable cloud infrastructure with a CI/CD pipeline for reliable updates.

Our engagement began with a critical advisory phase. After analyzing the legacy Django application, we strongly recommended against an expensive and risky patching process, providing an estimate that demonstrated rebuilding from scratch would be a better long-term investment.

Once funded, we initiated a deep discovery process. By directly engaging with field operations teams, we identified critical flaws in their existing inventory reports caused by erroneous SQL statements and mapped out their real-world inspection process. This research became the foundation for a complete redesign of the data model and UI.

To demonstrate the value of a data-driven approach, we developed an interactive analytics prototype. This powerful tool allowed stakeholders, for the first time, to visualize historical trends, view geolocation data, and see the potential of predictive models for forecasting rust and rodent damage, as well as equipment resupply rates.

With buy-in secured, we executed a full-stack rebuild of the platform. The new system featured the streamlined UI, a new incident reporting system for repairs and restocks visible to all user levels, and the powerful analytics capabilities from the prototype. We managed the entire deployment to Google Cloud, complete with a modern CI/CD pipeline for easy maintenance and future updates.