In the era of smart manufacturing, Dtuy has become a critical link. Every day, vast amounts of data are generated by equipment and systems in the production environment. However, not all data is valuable. Many business owners are unaware of how much of the data they rely on daily is outdated or erroneous. Mistakes or omissions during employee data entry can cast doubt on data accuracy and even affect decision-making.

Traditional data collection methods often rely on manually filling out reports followed by computer entry. This approach, however, suffers from inaccuracies in manual data entry, time lags, and a host of other issues. In factories with legacy or imported equipment, data collection becomes even more challenging and costly due to non-open interfaces. Moreover, collecting more data is not always better—each data point requires time or labor costs, and not all data is needed at the current moment. Therefore, when performing Dtuy, we must focus on the timeliness, accuracy, and usability of the data.

By using external collectors, they have successfully achieved real-time collection of equipment on/off status and real-time production output, enabling plant managers to clearly understand the operational status of each piece of equipment. This multi-dimensional approach to Dtuy provides a fresh perspective for workshop management, helping them promptly identify anomalies in production, thereby improving production efficiency.

Therefore, to fully realize the economic output driven by data, merely having data is insufficient. More importantly, ensuring the timeliness and accuracy of data is essential for truly achieving the goals of smart manufacturing. In the wave of smart manufacturing, prioritizing data quality and collection methods is the key to gaining a competitive advantage in the future.