The latest AI-based model from Fujitsu can reduce the cases of products and services recalling for the company.

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%.

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.

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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Today’s operators are undergoing vast digital transformations to help shape their roadmaps for future innovation. That includes transforming existing networks to more virtualised environments and preparing for 5G. The new networks must be more robust and agile and at the same time, able to adapt to whatever the future will bring, Anand Gonuguntla, co-founder and CEO of Centina. Operators must also be prepared to manage a continued trend of software as a service and cloud-based service models.

Assuring quality of existing and future services becomes both more challenging and more critical to these operators as the competition heats up. Customers demand affordable and excellent quality on-demand, ready for anything they want to do—both at the business and residential level.

That makes service assurance in today’s world a challenge. Leveraging the latest advancements in Machine Learning and Artificial Intelligence, will become imperative for today’s operators to continuously assure their networks in a dynamic environment. Choosing the right service assurance solution to adapt to these needs is critical.

Optimising and managing complex networks with Artificial Intelligence and Machine Learning

While traditional service assurance offers a more reactive approach to remediation of network issues, in a hybrid or virtual network environment, service providers can be much more proactive in both network monitoring and optimising performance.

Today’s AI driven service assurance solutions are offering predictive analytics tools, and invaluable business and network intelligence to its users. Spotting problems before they occur saves significant time and resources that both improve customer experience and prevent or reduce expensive down time.

Another important benefit that these kinds of predictive monitoring solutions offer is in SLA compliance and cost savings. Avoiding unnecessary customer credits because of network interruption has tremendous operational savings for service providers.

As 5G approaches and with it promises of ubiquitous connectivity, operators must be prepared to up their investments in service assurance. Ensuring that solutions leverage Artificial Intelligence and Machine Learning is critical. But how does a provider know what to look for?

Here is a list of AI and ML features that today’s best service assurance solutions should offer:

• Performance-based anomaly detection

The ability to collect and analyse performance data over long periods of time to learn what’s normal for the network and alert when network or service performance trends from past norms.

• Alarm and event-based anomaly detection and resolution

The ability to learn from event and alarm patterns and resolutions to automatically correlate network events together and pinpoint the root-cause of network and service outages. Machine Learning algorithms could then use knowledge bases to suggest or automate resolutions.

• Automated optimisation and remediation

After detecting network issues, the ability to automatically re-configure the network to optimise deteriorating performance or re-route services due to failures – either directly to network devices or through orchestration systems, controllers and Element Management Systems.

The author of this blog is Anand Gonuguntla, co-founder and CEO of Centina

About the author

With over 20 years’ experience in the telecom industry, Anand co-founded Centina. As CEO, Anand oversees all strategic planning and execution of the company’s corporate, sales and product initiatives. Under Anand’s leadership, in just 10 years, the company underwent global expansion and has been recognised by leading analyst firms such as Gartner and Frost & Sullivan. The company was also ranked by Deloitte as one of the fastest growing companies in America and has achieved 314% growth from 2009 through 2013. These accolades are a validation of Centina’s enterprising spirit and it’s commitment to it’s core values.

Prior to Centina Systems, Anand held leadership positions at Xtera Communications and Fujitsu Network Communications. Anand also holds patents in the area of network management and is well published in the communications industry. He received his master’s degree in Electrical Engineering from the University of North Dakota and a bachelor’s degree in Electronics and Communications Engineering from Jawaharlal Nehru Technological University, India.

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