Symposium PresentationApproved for public release; distribution is unlimited

Carlson, Robert

Elhance, Jinsu

Gill, David I.

Hawkins, Timothy G.

English

Calhoun, Institutional Archive of the Naval Postgraduate School

Calhoun: The NPS Institutional ArchiveDSpace RepositoryAcquisition Research Program Faculty and Researchers' Publications2022-05-06Using Data Analytics to Detect Bridge ContractsGill, David I.; Hawkins, Timothy G.; Elhance, Jinsu;Carlson, RobertMonterey, California. Naval Postgraduate Schoolhttp://hdl.handle.net/10945/70244This publication is a work of the U.S. Government as defined in Title 17, UnitedStates Code, Section 101. Copyright protection is not available for this work in theUnited States.Downloaded from NPS Archive: CalhounUsing Data Analytics to Detect Bridge ContractsDavid Gill, Tim Hawkins, Jinsu Elhance, Robert Carlson19th Annual Acquisition Research SymposiumThursday, 12 May 2021 - 1:55 pm - 3:10 pm ETThe content of this presentation is the opinion of the writers and does not necessarily represent the position of the Internal Revenue Service.May 2022IRS Office of the Chief Procurement OfficerAnalytics, Research, & Technology DivisionBridge Contract ActionsNew contract awards are sometimes delayed Long PALT; bid protests, complexity of contracting process;  budget uncertainties; lack of advance planning; workloads; inexperience, & turnover, (GAO, 2014; GAO, 2016).Bridge Contracts = “an extension to an existing contract beyond the period of performance (including option years), or a new, short-term contract awarded on a sole-source basis to an incumbent contractor to avoid a lapse in service caused by a delay in awarding a follow-on contract” (GAO, 2016, p. 4).Differing definitions – not defined in the FARWhat’s the Problem?While meeting mission needs, bridge contracts: reduce competition, increase prices, increase transaction costs (GAO, 2016), and delay or deny business opportunities to “out” suppliersUndersecretary of Defense (AT&L): 1,100 bridge contracts ($13.7B) awarded during 2015 (Longo, 2020).Sometimes, bridges are bridgedBut, really don’t know how many bridge actions there are across the federal governmentNo uniform mechanism to assess the frequency of bridge actions – not reported in FPDS-NGUsing Data Analytics to Detect Bridge Contracts2Research Purpose Develop a data-analytic methodology  – using natural language processing, graph network theory, and machine learning – to reliably identify bridge contract actions.RQ1:  How prevalent are bridge contracts?RQ2:  Are there any discernable patterns in bridge contract use?3 Using Data Analytics to Detect Bridge ContractsAssumptions:  (1) A bridge contract will have the same vendor name, funding office, and be chronologically sequential.  (2) bridge mods share a PIID and modifications can be ordered by their action date, with ties being broken by modification number.MethodologyNatural language processing (NLP) to prepare the data.  Represented actions as nodes in a network, with edges connecting related actions.2 machine learning algorithms via random forest regression to predict likely bridge actions (50 decision trees each)USASpending data: > 5M contract actions, FY10-FY22IRS contract documents (e.g., J&As) + FY 21 Navy bridge contract report were used as ground truth to assess the model.  Model precision = True Positives / (True Positives + False Positives).4 Using Data Analytics to Detect Bridge ContractsBridge Contracts - Mean Decrease in Impurity Confusion Matrix: Precision=.684, Recall=.868 5 Using Data Analytics to Detect Bridge ContractsBridge Modifications - Mean Decrease in Impurity Confusion Matrix: Precision=.667, Recall=.5 6 Using Data Analytics to Detect Bridge ContractsKey Findings & ImplicationsAdvanced analytics techniques are helpful, but not perfect, in detecting bridge contracts – only discovered 15 bridges (mostly contracts vs mods)The most predictive features of bridge contracts include (in order of predictive power):  The period of performance gap between the predecessor and bridge contract action, the base and all options value, dollars obligated, similarity in the award descriptions, a consistent value per day among the predecessor and bridge contract action, urgency, period of performance days, competition codes in FPDS, and COVID funding.  The most predictive features of bridge modifications include (in order of predictive power):  dollars obligated, potential end date, action rank, services, action type, exercise rank, six or three month extension, similar value per day, option year, knowledge-based services, and logistics and transportation services. Documents (e.g., J&As) should identify bridge actions.Contract file should report prior extensions and identify the original contract extended. 7 Using Data Analytics to Detect Bridge Contracts

Using Data Analytics to Detect Bridge Contracts

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Using Data Analytics to Detect Bridge Contracts

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Calhoun, Institutional Archive of the Naval Postgraduate School