Data Ownership: Creating Information from Manual Production

By Fang Shin Yun

In the <IDC FutureScape: Worldwide Manufacturing 2022 Predictions> published by IDC (International Data Corporation) on December 3, 2021, Fang-Yun, Jiang, the general manager of IDC Taiwan, predicts:

By 2023, digital transformation and investment in digital technology

will contribute more than 52% of the global GDP.

The supply chain system has also led companies to design their roadmap according to digital transformation. Those plans prioritized the establishment of a comprehensive database. According to DigiTimes, the manufacturers also viewed data analysis and collections as one of the most important implementations. Without the establishment of data centers and the introduction of AI vision, companies will miss these opportunities to succeed in those areas. As a result, data ownership is a stepping stone in digital transformation, but how can AI vision support manufacturers to strengthen their robustness in this issue?

Supercharger for IE engineers

With AI vision, boost the efficiency of data collection:

AI vision helps collect data 24/7. Compared to the amount of data collected by an IE engineer with a stopwatch and a blank sheet, using AI is definitely a better option.

100% intuitive. AI uses start-to-start technology to record cycle times more accurately than IE pressing a stopwatch.

Around-the-clock operation. This has a huge benefit for 24-hour production lines, IE engineers no longer need to work long shifts.

Traceability. When looking at outliers, the IE’s notes are not able to give a complete insight into the bottleneck. AI vision can retrace the situations so that engineers can know what the outliers refer to.

Comprehensive Recording of Operating Time

By using AI vision, we can know the excessive working time on the production line and have some ideas about how to improve it; we can also know what is delaying the production line. This principle is also adopted for remote planting. Engineers at the Taiwan HQ can “see” the difficulties of foreign plants at any time through AI vision, and provide them with immediate assistance or instructions.

Having those data in hand enables us to react firmly to the waves of the unstable supply chain. But we cannot put these data somewhere in the data lake, so how can we use this great reference and apply them to the issue of data ownership?

Let data be your best support

Having large amounts and accurate data is basic, being able to analyze it and apply it, is the key to success in digital transformation and building IoT systems. This is also a great know-how for engineering teams and can help companies in many ways:

Bottleneck Identification

Through AI vision, from 4M1E, IE can tell what is delaying the process.

Downtime Analysis

Factory managers can know if an operator has left his post through the AI system, and thus learn the reason for production line downtime. Real-time alerts also allow them to address problems in the shortest possible time.

Line Balancing

The engineering team can take the accurate values provided by AI as a strong reference when it comes to SOP optimization.

Assit Future Decision

AI Vision can provide the following assistance in future decision-making.

Improve quality and productivity
AI vision input in each workstation to ensure the production line is more competitive.

Remote Management
With real-time vision, Taiwan HQ can be updated from a foreign factory with zero time difference. Digital twins can also be used to simulate the production line before the actual factory is built.

Provide facilitates information
The data collected by AI can provide effective information for companies to quickly gain insight into the production lines and get new factories up to speed.

Visualize production lines in real time
Through AI vision, the “blind spots” of existing bottlenecks can be clearly presented so that everyone can clearly understand the current difficulties.

The power of building data ownership

Finally, let’s go through the whole idea again. In terms of data ownership, AI vision can help collect a large number of accurate values. After analyzing the data, it can also visualize the production line and assist us in future decisions. Yes, this sounds complicated, but without the ability in data ownership and the help of AI vision, our future vision like global expansion and remote planting, cannot be easily achieved. Next week, we’ll take you through the topic of data applications of AI vision and explore how to effectively use this data to improve data ownership capabilities.

Read More:
Beyond Productivity: Fulfill ESG Metrics from Revolutionalize the Production Line
Data Ownership: Global Operation Realized by Using AI Vision