Connected Factory
Improve enterprise reliability and lower the cost of unexpected breakdowns.
How SAS® Enables the Connected Factory
Connect disparate data sources in the era of Industry 4.0. Apply a combination of monitoring, diagnostics and predictive maintenance techniques to improve asset and product reliability, as well as lower the cost of unexpected downtime.
A holistic view of operations
- Remove barriers imposed by siloed operational systems and gain full visibility into what's happening on the shop floor and out in the field.
- Bring together the voices of the process, product and customer for a single, comprehensive view of both process and product quality.
- Integrate structured and unstructured operational data from sources across your business.
Predictive maintenance
- Minimize unplanned downtime and increase the availability of key assets.
- Maximize overall equipment effectiveness (OEE).
- Increase return on assets (ROA) while reducing maintenance costs by shortening the mean time to repair (MTTR).
- Avoid rush parts deliveries, repair overtime payments and high buffer stocks.
Early warning of potential issues
- Apply quality-specific data models and patented analytics to drive early warning of emerging quality and reliability issues, data-driven root cause analysis and deeper process understanding.
- Help predict potentially catastrophic equipment and process incidents.
- Quickly identify design and production defects before they become widespread.
Flexible implementation
- Start small from one use case for a specific asset and grow in analytics maturity, number of use cases applied and scale of deployment for a smarter, more connected factory.
Our objective is to improve production, not spend time producing or collecting data.Makoto Miyamori Senior Manager of the No. 2 Manufacturing Innovation Section
SAS® Software Used
How does one of the world’s largest hard disk drive suppliers identify potential failures early in the production process and avoid yield excursions?
SAS helped Western Digital:
- Monitor equipment sensors and tag machine-to-machine data to identify hidden patterns that predict failures.
- Predict yield excursions and reduce losses caused by the production of defective devices.
- Lower the overall number of returned units, which in turn boosted customer loyalty and trust.
SAS® Software Used
How does one of the world's leading companies in health care use analytics to improve uptime by automating laboratories?
SAS and partner mayato helped Siemens Healthineers:
- Develop a predictive service and maintenance solution based on the SAS Platform to increase system availability and reduce service costs.
- Optimize workflow and clinical performance by transporting samples along a bidirectional magnetic conveyer belt and tracking every sample with a multicamera vision system.
- Ensure that throughput is uninterrupted and productivity remains as high as possible.
SAS® Software Used
How does the OEM of C-130 aircraft use machine learning to transform predictive maintenance for complex equipment?
SAS helped Lockheed Martin:
- Use artificial intelligence, IoT and advanced analytics to predict when parts will fail, keeping more aircraft airborne for vital missions worldwide.
- Reduce data cleanup times by 95%, allowing the company to quickly produce results that it believes will reduce downtime and cost for customers.
- Learn from its collective maintenance history to form a real-time best practice for aircraft maintenance.
Related Products & Solutions
-
SAS® Analytics for IoTDrive innovation, efficiencies and results by putting powerful IoT analytics with embedded AI and industry-leading streaming capabilities in users' hands
-
SAS® Asset Performance AnalyticsHarness M2M and sensor data to boost uptime, performance and productivity while lowering maintenance costs and reducing your risk of revenue loss.
-
SAS® Field Quality AnalyticsDetect emerging issues and perform root-cause analysis to improve product quality and brand reputation.
