# IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model
## 4 Storing Sewing Data and Transforming into Experimental Datasets  
### Database Structure to Store Sewing Data  
After data about the production process are synchronized to the cloud server system of the IoT device supplier (for example, Borther supplier), the business can retrieve this data (through the API provided by the supplier), providing business-level services and integrating with the business's MIS system data. This integration helps create more in-depth reports about the enterprise's production and business activities. For effective integration, the MIS database must be fully organized and capable of storing data from the cloud server. This stored data includes customer information, contact details, products to be manufactured, production stages, sewing workers, sewing lines (where workers are placed for the work process continuity), implementation plans and schedules, equipment, and nationwide distribution sewing lines and workers. Table 1 presents a detailed list of data tables in the database, organized to support efficient data storage and retrieval from the IoT system. The organization of data into well-defined, structured tables facilitates streamlined data access and processing, enabling rapid aggregation for high efficiency and accuracy in generating management reports. Moreover, this structured data architecture allows seamless integration with existing datasets within the enterprise MIS, supporting the creation of consolidated reports, such as employee information, production costs, material inventories, and contract details. These capabilities contribute to the continuous and precise \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Table Name** & **Description** & **Note** \\ \hline
1 & tblCustomer & List of customers & \\ \hline
2 & tblContract & List of contracts & Each customer will sign one or more contracts \\  & & & with the business \\ \hline
3 & tblProduct & List of products in the contract & Each contract orders one or more products \\ \hline
4 & tblStaff & List of company’ staffs & \\ \hline
5 & tblPlan & Daily production plan & \\ \hline
6 & tblSewing & The sewing lines & Each sewing line has many labors \\ \hline
7 & tblProcess &
\begin{tabular}{c} Progress of plan \\ implementation over time \\ \end{tabular} & Production progress is collected from IoT devices \\ \hline
8 & tblDevice & List of devices to production \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data tables used to store IoT data. monitoring of production activities through comprehensive reporting. Regarding storage, employing a relational database management system allows structured data to be stored and scaled without limitation, ensuring reliable support for long-term data retention and system expansion. Figure 3 below presents a productivity chart segmented by the product's stages. The _\"Sum of GT_TT\"_ data illustrates the planned activities to be carried out each day, while the _\"Sum of ThucHien\"_ data shows the progress of product implementation and the output achieved during synthesis. Additionally, Figure 4 shows the total number of products produced hourly. These two charts help managers monitor the implementation of the production plan in real time, thereby providing timely instructions if problems arise, helping production to be carried out continuously. In Figure 3, the information illustrates the production rate achieved compared to the planned target and identifies the responsible production team. The product codes and names are pre-organized and stored within the company's ERP system. For example, in the first column, \"PBF24... Yes NEWTI\" represents the product code scheduled for production. \"T6 1\" refers to the group of workers assigned to the task, and the production rate achieved at the time of this progress snapshot is 60% of the planned target. Figure 4 illustrates the hourly production results of the product. In this case, the target number of products per hour set by the enterprise is assumed to be 1, while the actual production values were collected hourly.