1. Manufacturing supply chain from the perspective of a semiconductor trading company

In discussing SCM, I would like to first touch on the importance of semiconductors from the perspective of a semiconductor trading company. Semiconductors are incorporated into all modern products, and the market size is predicted to reach 100 trillion yen in 2030. However, rising geopolitical risks and supply shortages due to the COVID-19 pandemic are having a major impact on the supply chain.

2. Agile data management that captures uncertainty

Data management that improves the ability to deal with uncertainty
The COVID-19 pandemic has highlighted the limitations of traditional data analysis methods. In a situation where past patterns do not guarantee the future, real-time collection and analysis of data is essential. It can be said that the manufacturing industry is required to design operations on the assumption that predictions will fail. Even in the field of data science, the emphasis is on monitoring and rapid decision-making rather than improving the accuracy of predictive models.

Simulation tools to support human decision-making
The latest simulation tools and demand forecasting models can visualize complex supply chain situations and support human decision-making. For example, by instantly simulating inventory status and production capacity, optimal ordering and production plans can be created. This improves the agility of the entire organization.

3. Risk Management in Global Supply Chains in the Manufacturing Industry

The Importance of Cybersecurity and Regulatory Compliance
While data utilization (visualization and sharing) within the company progresses, the risk of cyber attacks also increases at the same time. In particular, in global data management, it is essential to comply with the laws and regulations of each country and strengthen security governance. It is necessary to take appropriate security measures through inventorying and risk assessment of IT/information assets.

In addition, security risks will increase as new service models that utilize data are rolled out to customers. For example, when using IoT devices and cloud services, data security and privacy protection are required. Technical and organizational measures are needed to ensure this.

4. Knowledge management for sustainable management

It is important to utilize digitized collective knowledge from the past and consider creating solutions for in-house knowledge using generative AI.
With the spread of digital technologies such as IoT and AI, and the evolution of data science, it is now possible to utilize huge amounts of unstructured data (images, videos, audio, etc.).