SAS Production Quality Analytics vs. Raven.ai
A side-by-side comparison based on G2 review data.
| Metric | SAS Production Quality Analytics | Raven.ai |
|---|---|---|
| Overall rating | 4.8 | 4.3 |
| Review count | 10 | 20 |
| Ease of use | 9.6 | 8.9 |
| Ease of setup | 9.4 | 8.7 |
| Quality of support | 9.9 | 8.9 |
| Meets requirements | 9.4 | 8.8 |
| NPS | 90 | 55 |
| Small Business | 0.0% | 57.9% |
| Mid-Market | 60.0% | 36.8% |
| Enterprise | 40.0% | 5.3% |
| Total page views | 29 | 145 |
| Total LLM citations | 14 | 511 |
SAS Production Quality Analytics
Get a comprehensive view of quality and operational performance across the enterprise and throughout your entire supply chain. Achieve better Industrial Internet of Things support with advanced data models. By combining the power of data integration, automation and analytics, SAS enables you to fully understand operational processes so you can make sustainable improvements and lower associated costs.
- Data Analysis
- Understanding
- Ease of Use
- Data Visualization
- Data Accuracy
- Manufacturing Integration
Raven.ai
Raven.ai empowers manufacturers to drive continuous improvement by combining machine data with frontline insights to deliver complete, contextualized visibility into production. Unlike traditional tools, Raven.ai accounts for 100% of production time — automatically capturing downtime events, losses, and output in a single, easy-to-use timeline. This enables teams across operations, engineering, and leadership to quickly identify bottlenecks, reduce manual tasks, and make confident, data-backed decisions.
- Data Analysis
- Efficiency
- Manufacturing Integration
- Process Improvement
- Insights
- Automation