How Can Big Data Enhance Quality in Manufacturing?
Ian Wright posted on May 10, 2016 |

You’ve probably heard of big data and like many of us you may be wondering whether it’s a fad, a trend or the next revolution in manufacturing. A recent report by the American Society for Quality (ASQ) attempted to answer that question.


What Is Big Data and What Can It Do?

Citing the research and technology firm Gartner, Inc., the report defines big data as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making and process automation.”

To take a simple example, the terabytes of information comprising Wikipedia constitute big data.

Visualization of daily Wikipedia edits created by IBM.
Visualization of daily Wikipedia edits created by IBM.
According to the report, when used as a part of quality and continuous improvement efforts, big data can be leveraged in a number of ways to improve organizational performance. These include:
  • Correlating performance metrics
  • Predictive modeling
  • Understanding supplier network performance
  • Forecasting
  • Developing unexpected insights
  • Analyzing real-time data for real-time decisions

However, the report also states that manufacturers are not utilizing big data as much as they could or should be. Of the 1,600 respondents to a survey conducted by Global State of Quality 2 researchers, only 20 percent believe their organization is using big data to gain a competitive advantage.

Moreover, the effectiveness of big data usage remains controversial, as illustrated by this chart.

(Image courtesy of ASQ.)
(Image courtesy of ASQ.)

Advice from Big Data Experts

In order to understand the challenges and opportunities big data affords, ASQ interviewed two experts: Elmer Corbin, directive and project executive of client success at IBM Watson & Watson Health and Silvia Veronese, director of big data solutions at Hewlett Packard Enterprise Co.

To give a sense of scale, Corbin offered the following illustration: “If you were to take a snapshot of all the data created and stored from the beginning of time until three years ago, that amount of data would pale in comparison to the amount of data that has been generated and stored over the last three years.”

Properly utilized, this staggering amount of information can be used to a quality professional’s advantage. “Quality is improved because the data analyzed can be reveal data inconsistency and conflicts coming from multiple sources,” said Veronese.

Corbin gave an example from his days at IBM’s semiconductor manufacturing business: “We used big data and analytics there to predict early indications of deviations from the standard process and potential excursions. This would also provide us with information on possible next steps if an alert was received.”

“The tools would give us that early indication that something could potentially go wrong before something actually happened, which allowed us to do proactive maintenance rather than costly and time-consuming reactive repairs after a critical error occurred,” Corbin added.

Avoiding critical errors before they occur is the dream of every quality assurance engineer, but using big data in this way is no mean feat.

For manufacturers looking into leveraging big data, Veronese offered the following advice: “Internal alignment within the organization is a key factor for success. Big data touches everything, literally everything within an organization. This is not a project that is undertaken in just one business unit.”

For more information, access the full ASQ Global State of Quality 2 Reports here.

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