Design and Development of Data-Driven Risk Assessment Through the Integration of Federal Acquisition Data with Open, Internet Sources

Abstract

In acquisition planning and management, all information needs to be considered to ensure the successful execution of a project. Key pieces of information, with high data quality, must be used for accurate risk management that requires a good understanding and quick identification of the potential factors that might affect a program. This paper investigates effective data science techniques for a quick identification of high risks posed by natural disasters to a supply/production chain. The paper proposes a tool for helping a project manager analyze the integrity of a supply chain and find alternative potential contractors in lower risk areas. We focus on assessing the risks related to natural disasters at a contractor’s location, business type, and business relationships and networks. The system described here seeks to first identify critical contractors based on the natural disaster risk of the places they perform their main activity for a contract and the uniqueness of their business types. Second, we design a risk assessment framework and a visualization framework that enable an acquisition expert to assess a project’s risk by identifying contractors that may become critical and require additional redundancy. The visualization tool also can be used to determine additional potential subcontractors that are located in lower risks areas of the country.