FMC - Process Automation and Workflow Modernization - MRAS

The Federal Maritime Commission (FMC), through the General Services Administration (GSA), is seeking to enhance its operational efficiency by engaging in a contract focused on software engineering support. This initiative is spearheaded by the Bureau of Trade Analysis (BTA), which plays a pivotal role in promoting fair competition within the international ocean shipping industry. The BTA's mission involves monitoring maritime supply chain actors, providing economic analysis, and protecting shippers from unfair practices through compliance and education efforts.

Currently, the BTA collects data from various regulated entities using several unintegrated systems. This data includes service contracts and both structured and unstructured documents. To improve these processes, the BTA employs Python within Databricks and other Azure systems to manage these workflows. However, there is an urgent need to further automate these processes to reduce manual interventions and enhance efficiency. The goal is to develop lightweight tools that can streamline regulatory compliance monitoring, automate data verification for Form-1 submissions, and improve Servcon system workflows.

The contract seeks skilled software engineers who can design and implement solutions that will modernize FMC's workflow processes. These solutions are expected to be modular and secure, integrating seamlessly with existing FMC systems where applicable. Key objectives include automating routine compliance tasks, performing verification logic on submitted information, supporting internal access for analysis purposes, and visualizing processed data efficiently.

Overall, this project aims to develop adaptable software tools that enable internal users to automate submissions and reviews while performing validation checks on incoming data. By integrating these results with BTA's or FMC's internal analytics tools, the solutions will not only modernize compliance workflows but also ensure accuracy and reduce manual effort across various data-intensive processes.