How to enhance product quality in the Industrial IoT era

With the Industrial Internet of Things (IIoT) and via process-oriented analytics, smart manufacturing environments are positioned to make enormous gains in reducing operating costs, better uptime and improved asset performance management.

But what about the quality of the products that originate and transverse throughout this new IIoT infrastructure? In today’s fast-paced media environment, a product’s quality shortcomings can quickly become headline news, threatening financial implications for companies of all sizes, ranging from recall costs, to brand damage and to future product sales.

What value does product-oriented analytics bring in today’s semiconductor and electronics manufacturing industries? Being part of a big data analytics software company I would like to share some insights with you.

In the quest for IIoT process-based optimization, companies should also consider complementary solutions that can ensure individual product quality. Whether the final product is destined for an automotive ADAS system, a wearable heart monitor or a ubiquitous smartphone, product quality vigilance must be paramount. In the best of manufacturing environments, products may still have undetected quality issues. To harness the full value of the IIoT for OEMs, manufacturers and end users, it is becoming increasingly important to perform product analytics in addition to process analytics.

The application of product analytics enables companies to hear the “voice of their product”. It makes it possible to significantly improve product quality and at the same time enhance existing process analytics to further improve operational efficiency and product yield. Deep product analytics within the IIoT-based smart factory represents the next level in the promise of the IIoT. Let’s take a closer look at how product analytics adds unique value. What does product analytics provide that process analytics doesn’t?

The most common element to all electronic systems is the semiconductor chips that drive the functionality of the device. Analytics directed at optimizing machine performance in the manufacturing and testing of these chips does an exceptional job at ensuring peak manufacturing efficiency. However, process analytics only provides a very coarse-grained and limited assessment of product quality: good or bad.

Product analytics on the other hand, when performed on harmonized process and test data, can use the power of big data analytics to identify many nuances in manufacturing operations that are impossible to see in a binary test environment (good or bad). It is through these deep product analytics that quality, yield and productivity can be significantly improved.

I picked out some examples of product analytics insights for you that directly impact RMA reduction, yield improvement and brand protection:

One case that we see at almost every customer are devices that are shipped into the supply chain without being properly tested due to a tester “freeze”. This is a situation where multiple consecutive devices return the same parametric test result. These subsequent devices were not actually tested. But the test program recorded a result that was effectively “copied” from the prior device because of the freeze. Without a big data solution in place that can search through thousands of test results in real-time, it is next to impossible to find these test errors before the devices are shipped into the end market, resulting in a potential RMA situation.

Similar issues can happen with good devices being labeled as “bad”. A tester basically tests a device and determines if it is good or bad and then moves on to the next device. However, these machines don’t remember what the results were for a product line it was testing even a few short hours ago. In other words, a tester could be yielding devices at 95% at 10am, but then drop to a yield of 85% by 2pm.  In most cases, it isn’t because incoming product quality dropped.