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Improve manufacturing quality with advanced analytics across the entire product life cycle
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"Improve manufacturing quality with advanced analytics across the entire product life cycle"

Disconnected view of enterprise quality, failing to achieve yield and throughput goals, and excessive scrap and rework are all challenges that can be addressed with IoT and analytics.

Today’s manufacturers face countless issues surrounding product quality and production in general, for example:

  • Integrating data from disparate systems and isolated sources.
  • Obtaining visibility into and understanding of multiple operational processes.
  • The cost of poor quality goods, rework and scrap.
  • Improving overall manufacturing yields.

A lack of visibility across operational processes hampers a manufacturer’s ability to react to changes in product quality and operational performance. Without this information, it’s difficult to make fact-based business decisions, leaving manufacturers to rely on employee intuition and guesswork. This can be very expensive if the decisions made are wrong or based on incomplete information.

Downstream quality issues can also lead to significantly reduced customer satisfaction rates. This is especially true when problems appear after the organization manufactures and sells the product. If companies can’t integrate both manufacturing and post-sales quality data, they don’t know where problems are occurring or how to fix them.


Offered Free by: Pinnacle Solutions Incorporated
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