How Social Data Are Transforming Quality Management

Untapped information can help identify quality issues, improve customer experience

Published: Tuesday, November 12, 2013 - 12:19

Editor’s note: A webinar on this topic will held on Nov. 19, 2013, at 11 a.m. Pacific, 2 p.m. Eastern. Register here.

And previewing the webinar, Matthew Littlefield will be a guest on Quality Digest Live this Friday, Nov. 15, 2013, at 11 a.m. Pacific, 2 p.m. Eastern.

Unless you’ve been hiding under a rock that doesn't get Wi-Fi for the past two decades, you know that social media has significantly affected both consumer behavior and the way businesses interact with consumers. But what does that mean for quality management?

In contrast to several decades ago, today’s microblogging capabilities and short-form content provide everyone who has an opinion and access to an Internet connection with the option to publicly communicate his thoughts at the click of a button. Impressively, across social media channels, the number of such instances has grown exponentially, and it doesn’t seem to be slowing down.

Although many have chalked Twitter chatter and the like up to an overwhelming and unstructured mess of information, some innovators see it as a goldmine for product quality data. These business leaders are building on other social-data monitoring initiatives to identify quality intelligence, but there’s still work to be done.

The question remains: How do you use these data to identify and resolve quality issues and improve the overall customer experience? Through the lens of the closed-loop quality management model, we’ll examine just that.

The role of social data in closed-loop quality

Many companies have achieved closed-loop quality management to some extent, which is the concept of downstream quality-process content and product data being fed back upstream to improve quality processes and resolve quality issues. The idea is that the earlier a quality nonconformance or defect can be identified and resolved, the less negative effect it will have to the bottom line, profitability, and so on.

An often-cited example of this is the connection between customer-complaint management and failure mode and effects analysis (FMEA). In this case, complaints observed by the service department are recorded in customer-complaint modules and then routed to the design team’s product life-cycle management (PLM) software, where engineers and continuous improvement professionals can mine the data to initiate and aid existing FMEA processes.

Enterprise quality management software (EQMS) can facilitate this connection, and even help to further integrate those data with other quality processes, such as quality risk management.

The challenge is that a customer submitting a formal complaint is a process that many people won’t follow through. They may take a product in to get serviced, but that doesn’t necessarily mean that they or the company providing the service will notify the OEM, especially if the issue is not something that affects the public’s well-being. Likewise, government agencies or industry associations don’t typically get involved unless there’s a threat to consumer well-being. This is where making use of social media monitoring could be very useful.

Where exactly could the data come from?

It should be noted up front that depending on the source and the individual’s own privacy settings, there will be varying degrees of publicly accessible information. Additionally, there are essentially no guidelines or standards regarding what or how people can share information, so the quality of the content and information will always be a concern.

There are, of course, major social media networks such as LinkedIn, Twitter, and Facebook (more complete list here). Any user of social media knows that on these platforms it’s typical for people to share opinions and experiences, consult peers as well as strangers, and ask questions.

There are also a number of sites dedicated to answering people’s questions, such as Quora, Yahoo! Answers, and a bunch more (more complete list here). And finally, there are general, industry-specific, and even product-specific forums providing a platform for people to start and participate in conversations. These forums vary from sites like Reddit to the more-focused JeepForum.com.

The range of potential sources for product quality data is vast (and growing). However, identifying which mediums are relevant and worth monitoring is the easy part. Using these data to create actionable insights for your design and manufacturing teams is the real challenge.

Creating actionable insights with social media data

During the past decade, we’ve seen the rise of reputation management, sentiment analysis, and other forms of social monitoring, but there’s been little connection to quality. At a high level, the data needed for identifying quality improvement areas are there but not being used for other reasons like responding to customer complaints, influencing the buying process, identifying consumer behavior, understanding how brands are perceived, and so on.

Mining social data for product quality information will be particularly useful for companies that do business in consumer markets. Such industries may include consumer products (e.g., consumer electronics and home appliances) and automotive. In fact, we’re already seeing several companies take advantage of this data source. In home appliances Whirlpool is investing in a solution to bridge this gap, and in automotive, Toyota is starting a similar initiative.

In a recent article on CIO.com, Toyota’s CIO Zack Hicks discussed how his teams use social media monitoring and analysis across sales, service, quality, marketing, and product development. For quality specifically, he said, “We can look for information like whether new-car owners are hearing a slight rattle and pass that on to our quality engineers.”

Social media is a continuously evolving and dynamic space that’s not going anywhere. It’s understandable that companies are still trying to identify ways to capitalize on its wealth of information for quality purposes but also in other functional areas. As more companies develop solutions, the challenge won’t be collecting the data; it will be mining it in a way that’s contextually relevant and then connecting those data to current quality initiatives. Down the road, we expect to see EQMS play a role in this connection.

If you’re interested in learning more about how today’s emerging trends are affecting quality management, you might be interested in attending a webinar with Quality Digest’s editor-in-chief, Dirk Dusharme, and LNS Research’s president and principal analyst, Matthew Littlefield. The two will discuss closed-loop quality management and the role of cloud, machine-to-machine, the Internet of Everything, mobility, social, and big data analytics. More information about the webinar can be found here.

About The Author

Mike Roberts

Mike Roberts is a research associate with LNS Research based in Cambridge, Massachusetts. LNS Research provides executives a platform for accessing unbiased research and benchmark data to improve business performance. Roberts writes research papers, case studies, and contributes regularly to the LNS Research blog, where he covers topics including enterprise quality management software, manufacturing operations management, asset performance management, sustainability, and industrial automation 2.0.