Why Traditional QC Is Obsolete – and What to Do Instead

Lean methodologies, Six Sigma, Statistical Process Control and other techniques can help high-tech manufacturers identify areas to improve yield. But only artificial intelligence, machine learning and advanced analytics help manufacturers find additional, incremental opportunities for yield improvement.

Unpacking the Top Challenges in High-Tech Manufacturing

  • Process maturity.
  • Supply chain flexibility.
  • Quality and consistency of raw materials.
  • Operational consistency.
  • Equipment performance.
  • Modernizing operations and processes.
  • Analysis of disparate data from multiple sources.
  • Finding time, resources or talent for analytics.

The SAS® Difference in High-Tech Manufacturing

High-tech manufacturers can use analytics, AI, predictive modeling and more to unlock new insights across the spectrum of industrial IoT data:

AI and Machine Learning


Find early indicators of potential quality issues.

Increase quality, reliability and yield.

Streaming Analytics


Identify areas for improvement.

Optimize material consumption.

Big Data Management


Improve asset utilization.

Intelligent Edge


Reimagine supply chains and operations.


High-Tech Manufacturing Case Study

A leading global electronics manufacturer needed to increase yield and improve processes without completely revamping long-standing manufacturing practices.


Focus on improving production and quality by quickly and easily integrating data that straddles different processes.


Judging ROI on analytics isn’t easy, but the company’s senior manager of manufacturing acknowledges that even modest gains lead to substantial benefits. He estimates that just a 1% yield gain results in a SAVINGS OF $50 MILLION.


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