DOE · DOE-726 · record R3584

Machine Learning to support Operational Efficiency

Department of Energy · SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: The AI use case focuses on capturing operational metrics and institutional knowledge and does not significantly affect legal, material, or critical access to services rights.
Start date: 2024-10-01 (day precision)Source: 2024-10-01T00:00:00
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Administrative Functions
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Reducing risk of losing institutional knowledge associated with an aging workforce. Ensure time to proficiency for junior operators is minimized through storing and accessing institutional knowledge to understand how operational efficiency can be im

Expected benefits

Less unanticipated downtime, improved decision making and improved manufacturing processes with optimized output.

System outputs

The system will evaluate process output, and seek to provide recommendations to realize the desired output along with the associated logic for why the change will yield the projected output.

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: Not reported
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
Vendor (as reported): SRNS - OT In House Staff
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: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
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