It is well known that laboratories generate a vast amount of experimental data during testing processes. Over time, the volume of data becomes enormous. For laboratory managers, without an excellent management mechanism, tasks such as personnel quality supervision, workload coordination, and experimental process problem analysis become extremely difficult. This not only consumes significant time and effort but also severely compromises data accuracy.

To address these issues, the SW-LIMS (Laboratory Information Management System) Data Statistics Query and Early Warning Module, independently developed by Sunway World, has emerged.

First, let's understand traditional data statistics and querying. Without system support, if a laboratory manager wants to assess the performance of new personnel, they need to review the relevant staff’s experimental records and reports. This involves compiling and summarizing these records and reports before submitting them to the manager for evaluation. For annual summary statistics, manual counting is also required, and data analysis involves repeatedly reviewing the data. This obviously demands more time and effort, resulting in low efficiency. This approach is particularly unfriendly to the real-time transmission of data for special materials. So, how can we change this situation? The following sections will elaborate on the application scenarios and specific implementation of this module.

Scenario One
When laboratory leaders need to evaluate the quality supervision of testing personnel, they must refer to the experimental data routinely generated by the technicians, such as sample quantities, sample preparation rejection rates, entry status of original sample results, report issuance status, and so on. If they follow traditional manual investigation methods for data evaluation, it will be very time-consuming, labor-intensive, and inaccurate.

In this case, leaders can use the SW-LIMS data statistics module. This module offers multiple query methods, including searching by sample, team, and testing personnel. Leaders can view information on samples processed by a specific technician within a certain timeframe, including total sample count, specific sample IDs, sample types, total time consumed, etc. After reviewing this information, leaders can better evaluate the quality supervision of testing personnel, enabling evidence-based and precise assessments.

Scenario Two
When laboratories prepare annual reports or other summaries, they require supporting data. For instance, materials like annual or monthly reports need data on how many samples were processed, the non-conformance rate, commission statistics, and so on.

In such cases, managers can review the sample logbook in the SW-LIMS data statistics module. By applying different filter conditions for statistical analysis, they can obtain the data needed for reports with high accuracy. After statistically analyzing each person’s workload, leaders can more accurately adjust the work assignments of lab technicians, thereby improving the laboratory’s efficiency and quality.

Scenario Three
Laboratory managers often need to understand the progress of experiments, such as the time samples spend at various nodes, the duration at each node, and whether any anomalies exist.

In this scenario, the SW-LIMS early warning function can be used. By setting detection time limits for different materials and configuring time limits for various nodes, managers can monitor data anomalies. If the data at any node exceeds the set time interval, it will be displayed on the early warning interface. Managers can visually identify which data have issues and then adjust testing processes and personnel accordingly. This serves to motivate quality improvement among personnel and continuously enhance the laboratory’s testing capabilities.

Scenario Four
Due to the unique nature of materials, workers in steel casting workshops often need timely access to the chemical element composition of molten iron. Traditionally, workers take samples of freshly smelted molten steel to the laboratory and then wait on-site for the test results. The technicians must also promptly print the test data into documents and quickly hand them over to the workers to determine if the furnace batch is qualified.

It is easy to see that during this waiting period, workers could be doing other tasks. To address this, Sunway World designed a rapid large-screen display module for the furnace front. This module is divided into a data input terminal and a data display terminal. At the data input terminal, technicians quickly enter data based on instrument readings and then publish it. By placing large screens in various locations within the workshop and inputting different furnace number parameters, data for different furnaces can be displayed. The data refreshes in real time. This way, after sending samples, workers can determine the quality of the molten steel based on the data displayed on the large screens, allowing them to proceed with subsequent work. This saves significant time and also allows for historical data queries, providing evidence for decisions.

The SW-LIMS Data Statistics Query and Early Warning Module is a highly practical tool. Its design is based on thorough communication with users and continuously optimized from the user’s perspective. It makes daily work more convenient for users and enables managers to better manage and monitor the laboratory, carrying out routine tasks more efficiently.