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Predictive Analytics and Prognostic Modeling

Active

Federal

Response Deadline11/05/2026, 12:00:00 AM UTC

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

AI Contract Overview

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This subcontract opportunity, titled Predictive Analytics and Prognostic Modeling, focuses on the development of advanced analytics for prime contractors supporting Navy NSWC Port Hueneme projects. The primary objective is to create prognostic models capable of predicting equipment failure and identifying anomalous conditions to enhance maintenance decision support. The successful contractor will be responsible for delivering validated prognostic and predictive-maintenance analytic models. Managed by the Department of Defense under the Commanding Officer, this requirement falls under NAICS code 541511. The solicitation was posted on October 22, 2026, with a response deadline set for November 5, 2026.

General Info

Subcontract for Navy NSWC Port Hueneme to develop predictive maintenance and prognostic models.

Agency

Department Of Defense → Commanding Officer

NAICS

541511 - Custom Computer Programming Services

Place of Performance

CA

Set-Aside

NONE

Documents

This scope was carved out of MATAC_27-03.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

CBM+ SOFTWARE AND ANALYTICS DEVELOPMENT (CBM+)

AI Contract Breakdown

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Timeline

PostedOct 22

subcontract

Response DeadlineNov 5

Submission deadline

Response Deadline

11/05/2026, 12:00:00 AM UTCin 13 days

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Organization & Contact Information

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AgencyDepartment Of Defense → Commanding Officer

ContactsNo contacts available

OfficeN/A

Organization / AgencyDepartment Of Defense → Commanding Officer

Office AddressN/A

ContactsNo contact information available

Full Description

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Develops predictive analytics for prime contractors on Navy NSWC Port Hueneme projects. Creates prognostic models to predict equipment failure and identifies anomalous conditions to provide maintenance decision support. Delivers validated prognostic and predictive-maintenance analytic models.

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