In manufacturing, you're under pressure to continuously improve quality while reducing costs and increasing productivity. You also strive to right-size inventory and boost profitability while driving year-over-year cost improvements. Finding new ways to extract value from the deluge of sensor and IoT data would enable you to move from a reactive to a proactive approach to minimizing unplanned downtime, reducing scrap and rework, and developing innovative new revenue streams.
Managing the unexpected is a constant challenge. Traditional approaches – Six Sigma, line-level reporting, MES systems – are no longer sufficient for gaining insights from data to improve decision making. Finding new ways to harness the value of industrial data is essential to enabling modern manufacturers to manage today's data volume, velocity and variety.
How AI Can Help
Advances in AI enable us to automate complicated tasks and find useful signals in data that was previously too large or complex to tackle. From quality and equipment performance, to supply chain and spare parts optimization, to service improvements and monetization of IoT data, AI techniques can unlock new insights across the spectrum of manufacturing data, enabling you to:
- Find early indicators of potential quality issues. AI capabilities go far beyond what simple rule-based systems can do, continuously learning to automatically detect patterns in data that a human would likely never see.
- Avoid costly scrap and rework. Use image recognition to identify flaws during the manufacturing process so you can address them promptly.
- Identify areas for improvement. Text analytics, including natural language processing, lets you link customer sentiment, service comments and other written records to quality and production variables to identify areas for improvement.
- Improve yield. Apply deep learning in industrial operations to optimize product composition and production techniques, combining audio, video, text and other data at efficiency levels that were previously unimaginable.
Why choose SAS for AI solutions?
As the leader in advanced analytics, SAS understands that a carefully designed and well-implemented analytics strategy enables manufacturers to meet their production and profitability goals more efficiently and effectively. It’s not just about getting the technology right; it’s about using data to manage complexity, reduce risk, improve margins and even create new sources of revenue.
That's why we embedded AI capabilities in our software – from our powerful analytics platform to solutions that help manufacturers confidently detect, resolve, predict and prevent quality and reliability issues. SAS simplifies data integration from diverse systems, extracts deeper insights from data to drive productivity improvements, and can be deployed wherever and whenever you need the insights in your operations – on-machine or across the enterprise.
- Customer Story Analytics turns service repair data into cost savings American Honda turns to SAS to help make sense of warranty and service data
- White Paper Artificial Intelligence for Executives Integrating AI Into Your Organization
- White Paper Text Analytics for Executives What Can Text Analytics Do for Your Organization?
- E-Book Making Sense of AI
- White Paper The Evolution of Analytics Opportunities and Challenges for Machine Learning in Business
- White Paper The Next Analytics Age: Machine Learning A Harvard Business Review Insight Center Collection
AI Solutions for Manufacturing
- 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® Event Stream ProcessingGet immediate analytic insights from real-time big data streaming into your organization.
- SAS® Field Quality AnalyticsDetect emerging issues and perform root-cause analysis to improve product quality and brand reputation.
- SAS Forecast Server快速自動產生大量預測以提升規劃與決策工作。
- SAS® Production Quality AnalyticsGain a holistic view of quality across the enterprise and throughout the entire supply chain.
- SAS® Quality Analytic SuiteIdentify issues earlier, find root causes faster and greatly reduce costs associated with recalls and brand reputation erosion.
- SAS Text Miner使用監督、半監督及非監督技巧來挖掘非結構化資料來源，探索整個文件庫中的主題與模式。
- SAS® 視覺資料探勘與機器學習利用單一整合的 In-Memory 環境，快速解決最複雜的問題。
- SAS® Visual Forecasting在開放環境中，自動執行大規模且可信賴的預測。
- SAS® Visual Text Analytics整合自然語言處理、機器學習及語言學規則等技術，協助發掘在文字資料內的隱含洞察。