

IR-2021-102, May 5, 2021
WASHINGTON —The Internal Revenue Service's Office of the Chief Procurement Officer today announced the successful development of a web app called Projected Contract Award Date. The interactive forecast dashboard statistically predicts when contracts will be signed.
"This effort is a new trend in contract management that adjusts our business processes based on timing factors in the government contracting process, using cutting-edge data science technologies," said Shanna Webbers, IRS Chief Procurement Officer. "The web app will help us shorten the lead time in awards and save valuable time for our procurement staff as well as help contractors."
The IRS has $2.5 billion in contracts a year. 'When will a contract be signed?' is a key question for the IRS and generally for the federal government. This tool gives insight about when each request is likely to turn into a contract. The tool provides a technique other federal agencies can implement, potentially effecting $600 billion in government contracts.
The new web app provides information on requisitions for new contracts. Using historical data of contract awards, the intelligent web app forecasts the number of days to contract award for requisitions in the IRS's Integrated Financial System – Procurement for the Public Sector. Predictions can also be expressed as a projected contract award date.
The managerial implications of the new application are far-reaching. The web app with its predictive model will enable internal customers to accurately forecast needs and when they will be fulfilled, enable the IRS to adjust standards by redefining requirements – solicitation procedure, competition, dollar value and type of goods/services with commensurate realistic award lead time goals – and evenly distribute workload to contracting personnel and others.
This predictive web app is one of the results brought about by an IRS research partnership with Data and Analytic Solutions (DAS), a woman-owned, small business comprised of procurement practitioners as well as university professors and students with procurement and machine learning experience. Other research initiatives of the team include vendor risk analysis and natural language processing and clustering analysis.


