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Average Ratings 0 Ratings

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ease
features
design
support

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Description

FalconEAM, also known as Computerized Maintenance Management System (CMMS) Software, serves as a versatile, enterprise-level asset management solution that offers a comprehensive overview of an organization's asset infrastructure, including its availability, health, productivity, and the history of both periodic and preventive maintenance as well as maintenance strategies. By minimizing unexpected downtime, FalconEAM enhances asset performance and prolongs the operational lifespan of capital equipment, thereby boosting return on assets. Maintenance tasks, whether periodic, cyclic, or reactive, are efficiently scheduled and delegated to operators. The integration of Industrial IoT (Industry 4.0) sensors along with predictive analytics enables the system to issue alerts for preventive maintenance. Furthermore, it allows for the management of assets across various business units or sites while maintaining a unified view of the entire asset infrastructure within its operational framework. Additional features such as geo-tagging and regular audit updates ensure that asset availability is accurately validated at their designated locations, promoting a more organized and efficient management approach. Overall, FalconEAM stands out as a critical tool for organizations aiming to optimize their asset management practices.

Description

The PDX platform serves as a comprehensive predictive analytics tool that oversees essential assets by gathering and interpreting large volumes of data, aiming to minimize unexpected downtimes, enhance operational efficiency, and elevate product standards. To fully leverage the capabilities of an industrial operation, the initial step is to gather relevant data. The PDX DAQ software application enables users to synchronize data collection across various sources over specified timeframes. This approach leads to a focused and efficient data acquisition strategy that provides only the critical information required for analysis, generating timely and significant insights. With the innovative solutions from Predictronics, automotive suppliers and manufacturers gain the ability to foresee the wear and tear of crucial assets, such as industrial robots and stamping machines, thereby averting unexpected downtimes, prolonging and optimizing maintenance periods, eliminating hindrances in production lines, and addressing product quality concerns effectively. Additionally, this predictive capability allows businesses to make informed decisions that contribute to long-term operational success and sustainability.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Inventrax

Country

India

Website

inventrax.com/enterprise-asset-management.aspx

Vendor Details

Company Name

Predictronics

Founded

2012

Country

United States

Website

predictronics.com

Product Features

EAM

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

Product Features

Predictive Analytics

AI / Machine Learning
Benchmarking
Data Blending
Data Mining
Demand Forecasting
For Education
For Healthcare
Modeling & Simulation
Sentiment Analysis

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