Seung Baek Kang

Vice President of Megazone Cloud

Digital Transformation In The Manufacturing Industry Is A Matter of Survival  

According to the Korea International Trade Association's report “Digital Transformation Strategy Using Cloud” in July 2021, Korea is ranked 3rd in the world, but the utilization of digital technology is reported to be below the OECD average. The cloud mobility rate was reported to be 22.1%, utilization of CRM/DB/computing at its lowest level, and the mobility rate of big-data analysis to promote data utilization at only about 2.5%.

It has been 2 years since the report was published, and although the overall pace of change in society has accelerated due to the COVID-19 pandemic, the utilization of digital technology in Korean manufacturing still appears to be in its infancy. In order to utilize data, the introduction of cloud, the core infrastructure of the data industry, is essential, but cloud adoption among Korean enterprises still remains at 15%. The adoption of cloud and artificial intelligence/machine learning services by manufacturing companies among enterprises is estimated to be lower than that.

According to the data released by the OECD on September 19, Korea's economic growth rate this year was expected to be 1.5%. This is due to the impact of exports, which have been decreasing for 11 months, and was analyzed to be due to the decline in unit prices of major export products such as semiconductors, petroleum products, petrochemicals, and steel.

Amid the above changes in the business environment, digital transformation in the manufacturing industry is now considered a matter of survival.

To support this, the government plans to implement the Smart Manufacturing Innovation Act to create a stable environment for promoting digital manufacturing innovation and to strongly promote digital transformation policies for small and medium-sized manufacturing companies in the future.

How should manufacturing sites process digital transformation? Successful manufacturing professionals I have met showed the following characteristics.

First, they had a clear goal to voluntarily solve problems on site before anyone asked or ordered. (For example, introduction of computer vision technology to reduce defect rate)

Second, they had a culture of constant experimentation and attempts. Rather than trying to solve field problems all at once, they try to solve 1-2 problems. The teams valued speed and flexibility, similar to the Lean management method of a startup.

Third, they attempt a platform approach rather than introducing a specific solution to solve the problem. Previously, the focus was on collecting, processing, and analyzing specific data to reduce specific defect rates, but through computing vision and IoT (Internet of Things), real-time data from products and factories was collected, and machine learning using this data was used to improve production quality. We had a plan to build a cloud-based framework to analyze the impact and optimize the production process.

For digital transformation, it is important to first create a use case. In the current difficult business environment, it is most important to select and implement a use case that can provide the quickest compensation, considering financial impact and ease of implementation.

Among the cloud enterprise use cases carried out in 2022 and 2023 by manufacturing customers of Megazone Cloud, the largest managed service provider in Asia, I would like to introduce the three largest cases as follows.

1) Infrastructure conversion to Cloud - As it is difficult to process and analyze increasing data due to the limitations of the existing infrastructure, conversion to Cloud is being carried out as a prerequisite.

2) Data Lake and Analysis – We are implementing everything from collection and processing of various production IoT data, building a platform for data analysis, and dashboards that allow various departments to effectively utilize the analyzed data.

3) Artificial Intelligence/Machine Learning – This is a case of selecting a task to solve a specific business goal and utilizing various cutting-edge technologies (artificial intelligence/machine learning) to do so. In particular, many new experiments have been attempted recently, such as reducing repetitive personnel tasks by learning personnel data using generative AI technology.