Keeping quality high while lowering process costs with AI & IoT in Manufacturing

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As profit margins shrink and consumer expectation rise, it’s for manufacturers more important than ever to optimize for quality in their operation. Fortunately, quality management technology has made huge advancements in recent years. Today organizations within manufacturing and their leaders can easily embrace available AI and IoT enabled solutions. This allows to detect quality issues early in the production process. 

The cost of quality in manufacturing is rising. Even high-skilled manual inspections can be slow, ineffective, costly and potentially dangerous. More than half of the checks require a visual confirmation by someone. They see if all parts are in the correct location and are free from defects. Even when manufacturers use cameras, they are only capturing a fraction of the potential data.

Quality check

New technology can play an important role in optimizing quality. Artificial intelligence (AI) can help to check products on the basis of a library of known defects. Cameras and/or acoustic systems identify issues that are too small for the human eye or ear. They can detect changes in images or vibration that may indicate a problem.

These systems are able to classify the severity of the defect. They identify the root cause of quality issues in milliseconds. Furthermore they learn continuously from feedback. These systems recognize both good and bad parts with just a few hundred images or sound files of known defects. The solution will get smarter and improve its accuracy over time as it’s exposed to more defects. This all counts for production lines of mobile phones, soft drinks, paint cans, electronic circuit boards, engines and so on.

More accurately

Any industry where manufacturing flaws can be detected visually or acoustically is suitable for AI. IBM Visual Insights consists of AI based solutions that can resolve quality problems faster and more accurately than humans. This means a significant reduction of quality related costs. Most organizations spend as high as 15 to 20 percent of total sales revenue on quality issues. Some going as high as 40 percent of total operations.

A general rule of thumb is that costs of poor quality in a thriving company will be about 10 to 15 percent of operations. Even companies that strive to deliver the highest quality during every stage of their processes are not immune to quality issues that lead to high rates of rejected products.

Sensor data

An important enabler of applying AI in manufacturing is the internet of things (IoT). In combination they form the basis for Industry 4.0. Data derived from sensors within the production process create a goldmine of information. Companies can gather real-time data about performance and quality to avert possible issues. IoT devices can be applied to capture data on energy usage, temperature and output of machinery. Sensors can also be outfitted on checkpoints in the distribution process. They can keep tabs on parts and products when shipped from factory to warehouse and beyond. Manufacturers can also glean insights from their increasingly connected products after the sale.

With all that data manufacturing companies can use AI to analyze and see patterns to improve quality, customer service, warranty management and even product design.

Leaders in manufacturing can now make their first or following steps to take advantage of the quality-improving, cost-saving opportunities by downloading this report. Use the abundance of data to unlock hidden insights.