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FactoIQ is an AI-powered predictive manufacturing platform that leverages advanced analytics and machine learning to forecast equipment failures, optimize process performance, and enhance product quality in real time. Built on AWS Cloud and IoT infrastructure, it integrates seamlessly with MES, ERP, SCADA, and control systems to deliver predictive maintenance, alarm analytics, and real-time performance insights. With adaptive learning models, customizable dashboards, and edge-to-cloud connectivity, FactoIQ enables manufacturers to minimize downtime, reduce waste, and make data-driven operational decisions. Designed for scalability, security, and continuous improvement, FactoIQ transforms industrial operations into intelligent, self-optimizing systems.
Overview
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Predictive Maintenance
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Quality Optimization
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Real-Time Insights
FactoIQ is an AI-driven predictive manufacturing platform that transforms industrial operations with real-time insights and intelligent automation. It uses advanced analytics and machine learning to forecast outcomes, detect potential equipment failures, and optimize process performance. By integrating with MES, ERP, and control systems, FactoIQ enables manufacturers to make data-driven decisions that enhance productivity, reduce downtime, and improve product quality - all within a secure, scalable AWS-powered environment.
What It Does
FactoIQ delivers predictive intelligence for modern manufacturing environments. It:
- Uses AI and analytics to identify performance trends and detect potential issues before they impact production.
- Integrates seamlessly with MES, ERP, and control systems for end-to-end operational visibility.
- Enables proactive decision-making to enhance efficiency and reduce downtime.
Why It's Different
Unlike traditional monitoring tools, FactoIQ combines predictive analytics, continuous learning, and real-time optimization in one unified SaaS platform. Built on AWS IoT and cloud infrastructure, it bridges shop-floor systems with cloud intelligence. Its adaptive machine learning models evolve with production data, improving accuracy and reliability over time. The result is a proactive manufacturing environment that minimizes disruptions, maximizes efficiency, and ensures consistent performance across sites.
Key Features
- Predictive Analytics: Leverage AI to forecast equipment failures, process inefficiencies, and quality deviations.
- Real-Time Insights: Gain live visibility into operations, alarms, and key performance indicators (OEE, quality, throughput).
- AI-Powered Alarm Management: Prioritize and filter alarms to reduce alert fatigue and improve response efficiency.
- Quality Optimization: Monitor and adjust process parameters to maintain consistent product quality and minimize waste.
- Predictive Maintenance: Schedule maintenance proactively to prevent breakdowns and extend asset lifespan.
- Continuous Learning: Enhance predictive accuracy with adaptive machine learning algorithms.
- Seamless Integration: Connect effortlessly with MES, ERP, SCADA, and process control systems.
- Custom Dashboards & Reports: Visualize performance trends and KPIs through intuitive, configurable dashboards.
Use Cases
- Predictive maintenance for critical production assets.
- Quality optimization in real-time manufacturing.
- Alarm analytics for high-volume industrial environments.
- Process performance monitoring and anomaly detection.
Customer Benefits
- Reduced Downtime: Prevent equipment failures and process disruptions before they occur.
- Enhanced Quality: Improve product consistency through real-time quality control.
- Operational Efficiency: Optimize maintenance and process parameters to increase throughput.
- Data-Driven Decisions: Empower teams with actionable insights and analytics.
- Scalability & Security: Built on AWS for global scalability, encryption, and enterprise-grade compliance.
Highlights
- AI-Powered Predictive Analytics-Forecast failures, process inefficiencies, and quality deviations using advanced machine learning.
- Real-Time Operational Intelligence-Gain visibility into OEE, alarms, and performance metrics with live dashboards and alerts.
- Seamless Integration & Scalability-Connect with MES, ERP, and control systems on AWS for secure, scalable predictive manufacturing
Details
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
FactoIQ | Tracks OEE, downtime, and production metrics for predictive insights. | $20,000.00 |
Dimensions summary
FactoIQ uses a single contract-based pricing dimension billed by the number of hosts you monitor. A host represents a connected machine or asset on your plant floor. Your cost scales with how many hosts you enroll, so you pay based on the size of your monitored equipment footprint. This dimension covers tracking of OEE, downtime, and production metrics for predictive insights. There are no separate tiers or add-ons to choose between. To expand coverage, you add more hosts under the same contract structure.
Top-of-mind questions for buyers
A host represents a connected machine or asset on your plant floor. This includes machines, motors, pumps, presses, conveyors, and critical process equipment that send condition signals like vibration, temperature, current, and runtime. Each monitored asset you enroll counts as one host toward your total.
Cost scales with the number of hosts you enroll. Adding assets increases your host count and your total under the same contract. You do not need to switch tiers or plans. The recommended rollout starts with critical assets, then expands to more equipment classes over time.
Each host supplies condition signals such as vibration, temperature, current, and runtime, plus machine-state context. These signals feed tracking of OEE, downtime, and production metrics for predictive insights. You may not need new sensors on every machine; the setup depends on your existing signal landscape.
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