Quality matters. Many manufacturers will tell you that poor quality affects both the top and bottom line. They will tell you that the consequences of poor quality are on the rise and that social media can make quality issues devastating to an OEM’s brand. They will also tell you that they are struggling to respond. The problem? While the consequences of poor quality are rising, so too are margin pressures. Margin pressures means that budgets are tight and few can respond in traditional ways. Smart manufacturers need to think differently.
Cost of quality is on the rise
Beyond brand impact, poor quality also has a real impact to the bottom line. We must scrap or rework defective products which can eat into overall equipment effectiveness (OEE) measures and lead to plant inefficiency. Challenges only increase once a defective product leaves the factory. Growing supply chain complexity means that products are increasingly costly to find and recall.
Flex – one of the world’s largest electronics makers – estimates that for every $1 spent in product creation, they spend $100 creating resolutions to quality problems.
That is incredible. Solutions may include tracing root cause analysis to identify problems upstream with raw material inputs or downstream with the manufacturing process. It may also mean going back to product development to understand how product design contributes to quality.
Different industries; different impacts on quality
The implication of poor quality changes depending on the industry. For example, volume matters in the electronics industry. OEMs produce dozens of products per minute which means that a defect in a cell phone casing or circuit board assembly can be replicated hundreds of times before they identify and resolve the issue. That is a lot of potential scrap or rework. Smart electronics OEMs look for solutions that impact inspection speed. They need to quickly identify a quality issue, understand the root cause, and implement a resolution before hundreds of defective units are created.
Automotive companies have a different challenge. Here, OEMs are focused on manufacturing precision. Millimeters matter and a high degree of automation means that poorly calibrated equipment can cause small but meaningful variances. Sometimes these variances can be small – so small that only highly trained human inspectors with sophisticated testing tools can spot the difference. Smart automotive OEMs look for solutions that can help human inspectors identify these very small deviations with more accuracy.
Traditional inspection methods can be costly, slow, ineffective, and sometimes dangerous
Traditional manual inspections can be problematic. Quality inspection is a high-pressure job and the sole reliance on humans without new tools or methods can be slow and ineffective. Humans make mistakes. We get tired and have bad days. We require extensive training to spot defects and retraining to keep pace with new models. All this can hinder agility – a problem which intensified as our labor force ages and retires.
Some OEMs are getting smarter
Increasingly smart OEMs across a range of industries are approaching IBM about ways to get smarter with their quality inspection process. These firms are looking for help bringing technology – specifically machine learning and AI to bear on the problem. Fortunately, IBM has a number of solutions that can help.
Some firms are seeking to better understand the factors that contribute to quality. Have we exceeded quality thresholds? Does temperature or humidity play a role? What about equipment age and maintenance cycles? IBM has a statistical-based solution – called Prescriptive Quality – that dynamically weighs variables that might contribute to issues. This is a great solution when inspectors cannot identify quality based on an image or sound.
One of the hottest areas of interest from OEMs is how AI technology can identify visual or acoustic patterns related to quality defects. Can an image be used to identify a scratch on a cell phone casing or car paint job? Can acoustic sensors “hear” a poorly functioning dishwasher before the product is released from testing? The answers are yes and yes. IBM has two solutions – Visual Insights and Acoustic Insights – that use sophisticated AI to spot defects. What is even more impressive? These solutions can start with a small number of defective images or sounds and can learn over time to get smarter.
Does this mean we don’t need quality inspectors?
It is easy to position many of these AI-based solutions as replacing the jobs of quality inspectors. Yet this is rarely the case. Smart companies see these solutions as tools that help quality inspectors improve throughput and effectiveness. Put simply, technology like Visual Insights or Acoustic Insights help inspectors inspect products more quickly, with fewer misses, and fewer false positives. Rather than replacing inspectors, these technologies become important aids that help OEM respond better to the rising cost of quality without sacrificing margins.
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