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Quality circles are incredibly consequential for manufacturers, as understanding feedback from senior management to engineers on the operation floor is a key component to generating valuable data for future product development. But the reality is, today’s quality circles are constrained. To keep up with the rising demands of today’s manufacturing facilities it’s critical that quality circles evolve from a manual process into a digitized operation that leverages social media analytics to produce actionable data for plant managers everywhere.
So, how do we apply today’s advancements in AI and social media analytics to bolster the significance of quality circles? As simple as it sounds, it begins with genuinely embracing the journey of digital transformation.
In order to embrace digital transformation, it’s crucial to understand the ways in which AI can be applied to social media analytics. AI is primarily used to automate pattern recognition, meaning there is access to a multitude of social media analytics but AI bridges the gap between semi manual analysis of reports versus AI algorithms used to sift through billions of data points. Think of it as a super data filter designed to identify commonality of paint, instrumentation, usability, quality and figment of their car to their lifestyle.
Today’s manufacturing floors cannot merely invest in one service or arbitrary piece of technology. They need to create an entire environment where social media analytics can be properly deployed to provide tangible data and give insight into future trends + end user preferences. Not surprisingly, organizations who are progressive about integrating digital services achieve a far greater impact on their bottom line than those who do not. And as part of that process, when it comes to optimizing quality circles, it’s clear that social media analytics have a monumental effect on making product development more efficient.
A “quality” quality circle is one that engages both internal and external stakeholders. By using keyword searches and categorical filters across social media, manufacturers can accumulate an unprecedented level of data from target demographics to extrapolate a heat map of activity that can show exactly where the decrease in product value exists. That kind of granular data can be used to create a strategic plan for resolving pain points, and more importantly, the data has the potential to identify any core defects, which in turn could prevent product safety recalls.
Did your team have a product recall in the last several months? Consider retracing your steps and use basic social media insights to understand what went wrong. You might find that issues are more easily identifiable. Imagine, for example, if analysis helped reveal to an automobile manufacturer that their airbags were not compliant. That’s the kind of preventative quality control that manufacturers dream about, and now it’s possible.
Social media is integral to the future of quality circles and the manufacturing floor, and by not capitalizing on the benefits, manufacturing floors are forced to rely on vague information that can’t sufficiently spot a trend. The process has no standardization, and almost any derived analysis, in all likelihood, is irrelevant. By using social media, we can truly optimize the end customer experience.