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How Fujitsu Is Using Artificial Intelligence To Detect Product Flaws – Analytics India Magazine

Companies across the globe are exposed to a variety of risks. While some of them can be identified and avoided through strategic planning, others can not be even tracked. One of these dangers is a product recall, which normally occurs after a product or a service has been released, thereby adding huge costs to the company and irreversible damages for many.
Fujitsu, a Japanese firm, recently developed an AI system capable of highlighting irregularity in the product’s appearance to detect associated issues at an earlier stage, thereby providing the chance to correct them before the product is released in the market. The AI technology will be used for image inspection, which will allow for the extremely detailed identification of a wide range of external abnormalities on manufactured objects, such as scratches and production errors. 
How does it work?
The particular AI-enabled model is pre-trained on images of the products with simulated abnormalities. The company uses real images of defective goods pulled from a production line’s inspection process for the training data. 
Although many products have similar shape and appearance, the AI-based tool has the capability to correctly identify abnormalities associated with the product. For example — the frayed out threads of the carpet made of different materials or colour or defective wiring patterns on circuit boards can be identified by the AI tool with precision. The Fujitsu lab further confirmed the effectiveness of the AI-model in reducing the man-hours required to inspect the printed circuit-boards by at least 25%.

Photo Courtesy: Fujitsu Official Website
The earlier methods of training the AI model were based on the tendency to focus on individual characteristics of a product, rather than working on all characteristics of even similar looking products, to identify abnormalities with accuracy. As a result, it is essential to capture a wide range of features of a standard image while training AI to perform quality control tasks. Moreover, it will reduce the workload of the manufacturing industries and enhance productivity.
The Necessity
The first and foremost reason to have more AI-based models is to reduce the enormous cost associated with recalling goods and services. Take, for example, the most recent case of the Hyundai’s battery fiasco. Hyundai had to recall more than 82,000 vehicles – thereby costing the company around $900 million, amounting to $11,000 per vehicle. Similarly, General Motors had recalled around 7 million vehicles due to faulty airbags that hit the company with a whopping $1.2 billion.

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Secondly, it creates an unnecessary burden on the companies’ working staff, leading to increased man-hours, overburden of the work, and delays in meeting the targets set by the organisation. This delay is avoidable by emphasising the pre-production phase and adopting AI-based tools for precise product identification. Thirdly, defeated products in the market can cause injuries and fatalities, creating a massive brand image declination. 
Lastly, the Consumers Protection Laws of the respective countries will hold companies accountable for the defects and the harm caused to the consumers. This has been seen recently in March 2021, where Johnson & Johnson (J&J) has appealed with the US Supreme Court in a final effort to reverse one of the country’s biggest product liability verdicts.
The Way Forward
It’s better to embrace the latest AI, ML-based models, and technologies to provide a new life to companies’ production facilities to enhance the final products before rolling out in the market. Rather than facing trials, managing brand crises, or paying hefty sums, companies can look out for deep tech solving the problems and providing a cushion for the long-term good.
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