TREAS · BEP-41 · record R2771

Ink and Paper Consumption Forecast (IPCF)

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

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: IPCF predictive analytics model focuses on forecasting operational inputs (ink and paper), which are supporting resources. While useful for inventory planning, the model does not directly influence strategic decisions or mission-critical outcomes.
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

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

Problem the AI is intended to solve

The Ink and Paper Consumption Forecast (IPCF) AI model is intended to predict: 1. Ink mileage predictions based on ink type and associated denom; 2. Ink consumption predictions based on number of sheets; 3. Paper consumption predictions given a time period; 4. Ink consumption predictions given a time period

Expected benefits

The IPCF Predictive Analytics Model contributes to cost efficiency and supply chain resilience by predicting ink mileage based on ink type and denomination, as well as forecasting ink and paper consumption over time and by sheet volume. The model helps ensure accurate procurement, reduce material waste, and support uninterrupted production schedules.

System outputs

The IPCF Predictive Analytics Model generates predictive outputs that support operational planning. The model produces: 1. Ink mileage estimates based on ink type and associated currency denomination; 2. Ink consumption forecasts tied to the number of sheets scheduled for production; 3. Paper consumption projections over defined time periods; 4. Ink usage predictions across time intervals to support procurement and inventory decisions"

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: Not reported
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: 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