A recent study from Virginia Tech describes how unstructured textual information from online discussion forums and other social media can be mined to unearth vehicle defects. This, in turn, helps quality management professionals to predict impending problems, make decisions and proactively manage safety and quality. This is an area that merits further exploration.
There are two important vectors of possible future innovation here. First, portfolio information from social media, albeit being highly variable and unpredictable at individual level, contains valuable insights to aid decision support. One could imagine similar techniques to predict disease outbreaks and terrorist activities. It has been known that health and law professionals have been taking advantage of social media to support decisions. With improved technologies in unstructured text mining, these concepts can be further developed. In the long run, one has to believe that such technologies will become widely available to the consumers as well.
Second, it also showcases the importance of product quality for manufacturers as any lack of it will be internalized not just by the immediate customer but the populace at large, very quickly. For example, for automobile manufacturers, it is increasingly important that any possible quality issue is identified and corrected before the user of the vehicle knows and blogs about it. In essence, they have to build smart cars, able to self-diagnose problems and communicate them to the manufacturer. Additionally, the manufacturer has to be able to aggregate information at the fleet level to identify any looming quality issue before it affects the user adversely. We may be fast approaching the establishment of social channels for machine-to-machine communications. If manufacturers do not get ahead of this developing wave, they will be consumed by those who do.
It took thousands of years for humans to invent technologies to connect them all together, allowing the emergence of social intelligence. It will take less time for machines to do the same.
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