SBIR/STTR Research Award

Tablet Based Repair Management with Artificial Intelligence (AI)

Award identifier 99977cfed2556d7145f3280192a67952666054329683351412e77972a9be4f52Awarding agency Department of Defense · Navy

Award amount

$199,694

Program and phase

SBIR · Phase I

Award year

2020

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Award details

Awarding agency
Department of Defense
Branch
Navy
Recipient company
MURANO CORPORATION
Proposal award date
2020-05-18
Contract end date
2020-09-18
UEI
LNV9DL3K7HE5
Solicitation number
20.1
Topic code
N201-X02

Research abstract

Murano Corporation is proposing a highly secure tablet-based Cloud Artificial Intelligence (AI) application for work instructions and orders that: Can be used by both platform mechanics and operators to potentially establish a closed loop feedback system Integrates Technical Manuals (TMs) from platform manufacturers and internally developed instructions and available in digital and potentially graphical formats including engineering drawings and part numbers Can be used to easily capture and digitize “tribal” knowledge using digital media (audio, videos, photos etc.) Has the ability to create and update work orders by both platform operators and mechanics Has “Google” type search engine for troubleshooting tips, repair instructions, work order history, repair parts orders, platform performance, depot details, service history, operator, mechanics history, engineering drawings etc. Has “YouTube” like platform to display repair instruction videos Has a question and answer site for mechanics and operators for repair related topics (like Stack Overflow | crowd-sourcing) Can predict future platform repairs based on several factors (operators, missions, mechanics, depots, repair parts used, locations, weather etc.) versus the current basic repair prediction models Has the ability to alert naval decision-makers and depots of potential issues when platform operators record issues using their tablets in near real-time Can generate invaluable and detailed “last-mile” consumption data for repair parts both by the operators at sea and mechanics at depots during overhauls which can aid in sophisticated repair parts demand planning to reduce repair delays and improve operational readiness Can reduce repair parts ordering errors by mechanics by cross-referencing the TMs (compatibility check) Can be integrated with depot work order applications (beyond Phase III) to minimize duplicate data entry. These tablets will have sophisticated “store and forward” (“offline”) capability allowing it to operate even in areas without network connectivity and will be encrypted and use 2-factor authentication for enhanced security. This AI app will be powered by Murano Corporation’s advanced, modern, secure, and proven multi-tenant cloud Supply Chain Manager platform (used in Honda ecosystem) with integrated “middleware” technology that can fuse data from multiple sources. Supply Chain Manager can display operational, supply chain, financial information, and Big Data (environmental) from multiple sources created using different technologies like Google can display information from multiple sources created using different technologies (Word, PowerPoint, PDF, html, Excel etc.). And if Word or PowerPoint or Excel is unavailable, then Google Docs or Slides or Sheets is a perfectly capable replacement. Similarly, if an organization is using an obsolete maintenance or spare parts inventory application, they can use Supply Chain Manager’s work order & inventory management features.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

4 SBIR/STTR records share this UEI (2020–2024). Reported award amounts total $4,075,900. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: Department of Defense

Agency intelligence

Department of Defense

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