Pharmaceutical businesses have explored ways of solving challenges within their industry to automate certain processes, but certainly, challenges are considerably high in terms of people's safety and identifying chemicals. To solve automation challenges, while many other relevant technologies exist AI's scalability makes the technology the best option for long term success. One such solution is an AI-driven computer visioning system that is smart enough to enhance visibility and improve safety in various pharmaceutical business processes.
Ways Computer Vision Improves Pharma Productivity
Profile Chemicals
Computer visioning systems are sophisticated enough to ease the job of lab scientists by identifying, discovering, validating and profiling various types of chemicals.
Ensure Safety Standards
In pharmaceutical research labs and manufacturing, workers and scientists are often exposed to chemicals that cause a variety of hazards. To ensure safety, following personal protection equipment (PPE) requirements without fail is important. Here, the autonomous computer vision monitoring system continuously assures the correct PPE gear is being worn and can also monitor when staff handle hazardous chemicals or when chemical spills occur.
Track Employee Movement
Pharmaceutical businesses invest heavily in infrastructure to identify, track and plot the movements of each employee in the workplace using things like real-time location systems or RFID. These systems are very costly, lack accuracy and are not scalable. Here the computing vision system is both significantly more scalable and much less expensive. Being powered by AI analytics can generate data that provides deep insights into each individual's movement.
Detect Defects
During the manufacturing process, before packaging, it's challenging to identify defects in various manufactured products such as drugs, pills, or devices. Also, pharma companies face problems identifying defects in packaging. To avoid this, computer visioning systems deploy AI-powered advanced image processing algorithms to detect defects and anomalies in medicine manufacturing and drug packaging.
