TREAS · BEP-25 · record R2770

Sub-Asset Maintenance Model

Department of the Treasury · Bureau of Engraving and Printing (BEP)

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: This use case is not high-impact because it focuses on a narrow operational task with limited enterprise-wide decision value or strategic impact. It proactively predict the mileage until the next maintenance will be needed on a sub asset machine.
Start date: 2024-04-24 (day precision)Source: 4/24/2024
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Procurement and Financial Management
Operational functions: Forecasting and prediction Derived by keyword rules; see methodology

Problem the AI is intended to solve

This model helps reduce downtime by forecasting when sub-asset machines will need maintenance, enabling proactive servicing and improved asset reliability.

Expected benefits

SAMM model helps forecast when sub-asset machines will require maintenance, reducing unexpected breakdowns, improving asset reliability, and optimizing maintenance schedules to support mission continuity.

System outputs

SAMM model predicts the mileage until the next maintenance will be needed on a sub asset machine

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Yes
Public source code: Not reported

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: Used [name(s) removed] Data
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported