Staying on top of emerging technology is one of the Core Values of APQ Engineering and there are several options:
- A private, on-premise, AI can be applied to provide rich information.
- Machine learning models can analyze production data with many interdependent variables. This can be trained predict outcomes and prompt operators when setpoint changes are needed to maintain quality or prevent production losses.
- GPTs can search and organize operations knowledge like manuals, data sheets, bulletins, and use this data to generate work instructions and assist troubleshooting.
- Replace old HMIs with the latest generation of SCADA software. These platforms natively combine databases with machine communications to give new operators and managers the information they need. Here are some of the functions that will enable operators:
- Work Instructions.
- Quality Reports.
- Material Safety Data Sheets.
- Lab data reports.
- Batch schedules.
- Recipes & Changeover settings.
As an example; For a food blending line we record process data, quality data from the lab, and corrections that are entered by operators. When a quality check requires a correction then operators can view historical records to see what past operators did to adjust the process and how that affected lab results.
- Redesign of HMI displays using High-Performance HMI principles will have several benefits;
- Training and orientation are reduced by applying consistent and standardized design.
- Operators are able to more quickly transfer between lines when the controls are presented with a consistent look and feel.
- Troubleshooting time is reduce by providing diagnostic information, sensor status for example.
- Operators recover quickly from upset conditions if they have useful alarm messages and nuisance alarms are eliminated.