MRO360

MRO360

Verdantis
+
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About

DataQu is a web platform made up of an artificial intelligence engine that generates predictive mathematical models, thus allowing the occurrence of unexpected events to be anticipated. It predicts the failure of a machine component so that the component can be replaced, based on a plan, just before it fails. Generate crop prediction models to reduce the vulnerability of the agricultural sector to climate change. Respond quickly to rapid changes in customer needs and demand, supplier failures or production failures must be taken into account in reality. Customer segmentation is a useful area of ​​predictive analytics for any organization. Monitor activities, coordinate resources and keep your company up to date with your sales processes. It is used in various industries to enable companies and organizations to make better business decisions. It allows generating better customer management and thus having a correct vision of the future.

About

MRO360 is Verdantis’ AI-native Enterprise Asset Management (EAM) suite built to optimize maintenance, spare parts, and reliability operations across asset-intensive industries. Designed on a modular architecture, the platform combines Spare Parts Management, predictive maintenance, work order planning, and inventory optimization within a unified operational environment. The platform enables criticality assessment, obsolescence detection, multi-tier parts classification, and optimized reorder point management to help organizations reduce stockouts and control excess inventory. Work order planning prioritizes maintenance tasks based on asset failure risk, safety requirements, and material availability. Predictive maintenance analyzes failure patterns, spare parts consumption, and work order history to anticipate equipment breakdowns. Context-aware AI agents continuously improve inventory visibility, maintenance planning, and operational decision-making across multi-site operations

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Companies and businesses seeking an AI web platform to generate predictive mathematical models

Audience

Maintenance Managers / Engineers, Reliability Engineers, Inventory Managers / Planners, Procurement / Sourcing Managers, Operations Directors

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

$0
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 4.7 / 5
ease 4.0 / 5
features 5.0 / 5
design 4.3 / 5
support 5.0 / 5

Pros & Cons from Real Users

Pros

  • Multi-site inventory visibility prevents reordering parts sitting on shelves at nearby plants. It connects critical spare parts availability directly to scheduled PMs, eliminating unscheduled equipment downtime and streamlining our maintenance planning.
  • The predictive demand modeling replaces static min/max levels with real machine learning reorder points based on asset criticality and wear patterns. Multi-site inventory pooling allows us to fulfill shortages internally rather than placing costly emergency purchase orders.
  • What I value most is how effectively the platform connects our day-to-day production planning with the facility maintenance team's schedule. In a high-volume food manufacturing environment, tracking equipment reliability is critical. The interface makes it incredibly simple to log operational issues, issue immediate maintenance work orders, and coordinate spare parts availability so our mechanics can execute repairs without disrupting our primary baking and packaging schedules. It gives excellent visibility into vendor coordination and ongoing asset health.

Cons

  • Initial data scrubbing of legacy part descriptions took significant effort during onboarding. Also, the mobile interface for plant-floor inventory check-ins can feel slightly clunky on older tablets.
  • Initial onboarding generates a huge volume of discrepancy reports at once, requiring careful prioritization during early setup weeks.
  • While the maintenance workflow and purchase order generation are highly efficient, aligning the system's internal tracking with external 3PL logistics and multi-location inventory transfers takes some manual effort. It requires a bit of an adaptation period to get the freight and vendor scheduling elements perfectly synchronized with real-time warehouse counts.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

DataQu
Chile
dataqu.ai/

Company Information

Verdantis
Founded: 2004
United States
www.verdantis.com

Alternatives

Alternatives

SmartMaintenance

SmartMaintenance

Ctrl2GO Global
Star EAM

Star EAM

Star Information Systems
Vigilo

Vigilo

Scry AI
Zed-Assets

Zed-Assets

Zed-Axis

Categories

Categories

EAM

EAM Features

CMMS
Energy Management
Equipment Management
Facility Management
Inventory Management
IT Asset Management
Maintenance Management
Parts Management
Preventive Maintenance Scheduling
Software License Management
Warranty Management
Work Order Management

Integrations

Google Sheets
Infor CRM
Infor PLM
JAGGAER One
Odoo

Integrations

Google Sheets
Infor CRM
Infor PLM
JAGGAER One
Odoo
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