AI plays an important role in reducing quality loss in industrial production in several areas such as:

• Develop AI-based products using AR (augmented reality) and VR (virtual reality). This allows manufacturers to test multiple prototypes before starting production, thus reducing the complexity of maintenance and troubleshooting.

• Defect Detection. AI can analyze images, videos, or sensor data from the production line to detect anomalies, defects, or deviations from standard specifications faster and with more reliability than manual inspection. AI connected factories can use cloud-based sensors and devices to monitor production processes in real time, so it can adjust schedule on the fly in response to unexpected situations such as equipment malfunction, disruptions in the supply chain or changes in demand. It also allows for timely intervention and optimization of the production process.

• AI predictive maintenance can monitor machine performance, condition, and status. It can predict when maintenance or repairs are needed by scheduling maintenance activities at a reasonable time before failure occurs.

• AI can schedule production by considering factors such as machine availability, operator's skill set, order priority and production limitations. This allows the facilities to allocate resources, create a balanced workload, reduce downtime, and effectively improve overall production efficiency.

• AI can enhance resource optimization by assessing the availability and capabilities of key resources such as machinery, labor, and materials to facilitate strategic resource management and guarantee smooth operating procedures.
  

• AI can reduce costs by optimizing resource distribution, reducing waste, and increasing energy efficiency. The lower operating costs directly increases profitability.

• Improving products based on customer feedback. AI can learn from past data and feedback, then adjust the processes according to the conditions or requirements, which leads to improved product quality and greater customer satisfaction.

        Some companies that have been using AI to reduce their quality loss in industrial production include Siemens, which uses AI to identify defective parts and the source of the defect to ultimately improve quality. Another example is Samsung which uses AI in quality control to improve production processes and product quality by searching for defects, improve the inspection process and increase overall efficiency. This contributes to higher quality products and greater customer satisfaction. Another example, General Electric, has been using predictive maintenance to predict equipment breakdowns and improve maintenance schedules.

        In the case of chemical manufacturing, the Israeli startup Seebo has been using AI to predict and prevent production losses by analyzing complex data and identifying hidden inefficiencies. This technology helps chemical manufacturers to better understand their processes and reduce production losses from various causes such as inefficient packing and cleaning, decay of raw materials, inappropriate reaction and contamination.

        If you do not want to miss the latest news and updates on various movements in the construction and facility management, stay tuned to our future Blogs and mark your calendar for FacTech, ASEAN’s Only Exhibition on Factory Construction, Maintenance, Facility Management, and Technology - 3rd Edition on 19-22 June 2024 at BITEC, Bangkok.