9 Steps Towards Successful Innovation Projects
In biopharmaceutical manufacturing, maintaining sterility is of utmost importance, as any deviation can lead to contamination, compromised product quality, and significant financial losses. Humans, despite their skills and expertise, present a considerable risk to sterility simply because they are human. They are prone to fatigue, distraction, and errors, especially when tasked with monotonous, repetitive operations. These “stupid” tasks not only underutilize human creativity and problem-solving abilities but also expose sterile environments to unnecessary risks.
Computer vision technology offers an ideal solution by providing consistent, precise, and reliable monitoring that surpasses human capabilities in such environments. Unlike humans, computer vision systems do not get tired or distracted. They can continuously scrutinize processes with the highest level of perfection, ensuring that every detail adheres to stringent standards. By replacing humans in these repetitive tasks, computer vision not only mitigates the risk of human error but also allows human workers to focus on more complex, value-added activities that require creativity and expertise.
Through real-time monitoring and analysis, computer vision systems can instantly detect handling errors, such as improper gowning or incorrect component handling, thus ensuring that the manufacturing process remains sterile and compliant with industry regulations. This technology is poised to revolutionize sterile manufacturing by enhancing efficiency, quality, and reliability while minimizing the inherent risks associated with human involvement.
1. Define the Problem and Objectives:
- Clearly define the problem, focusing on areas where human error is prevalent or where manual processes are inefficient.
- Identify specific objectives, such as improving quality control, monitoring complex production processes, or automating error detection.
Example: In a biopharmaceutical setting, improper handling of sterile components can lead to contamination, resulting in batch rejection. A sophisticated computer vision system can monitor these processes in real-time, instantly detecting any deviations from standard procedures, such as improper gowning or incorrect handling techniques.
2. Conduct a Feasibility Study:
- Assess the feasibility of implementing computer vision for your specific use case, especially in environments where tracking complex requirements is challenging.
- Consider technical, security, and operational factors, including the availability of necessary hardware and software.
3. Assemble a Multidisciplinary Team:
- Gather a team with diverse expertise, including biopharmaceutical engineers, data scientists, data security/IT-access experts and quality assurance personnel.
- Ensure that the team understands both the technical/IT aspects and the complexities of biopharmaceutical processes.
4. Collect and Prepare Data:
- Gather relevant datasets for training and testing your computer vision models. This may include images or video from production lines, microscopy, or other imaging modalities.
- DeepEyes works with customer data only. Our tasks include annotating the data accurately, capturing a wide range of scenarios, including common human errors and complex process requirements.
5. Work with the most Sophisticated Computer Vision Algorithm
- Never underestimate the complexity of your requirements as well as the your need for the highest standards, both in functionality, but also in data safety and privacy aspects. Settling for quick and dirty solutions will in the long run bring more issues than benefits.
- The DeepEyes computer vision algorithms and frameworks will be based on your specific project requirements (e.g., object detection, segmentation, classification).
- The model will be developped and trained using your prepared dataset, ensuring that it meets the desired performance metrics and can detect subtle errors, also when it comes to human handling.
6. Integrate with Existing Systems:
- Plan the integration of your new monitoring solution with your existing manufacturing systems, such as production management or quality control systems.
- DeepEyes ensures that the solution is scalable and can be adapted to future needs, particularly in areas where human oversight is critical.
7. Test and Validate the Solution:
- Conduct thorough testing in a controlled environment to validate the performance and accuracy of the computer vision system.
- Simulate real-world scenarios, including potential handling errors, and ensure the system can reliably identify them.
8. Implement in Production:
- Deploy the solution in the production environment, starting with a pilot project if necessary to minimize risks.
- Monitor the system’s performance and make adjustments as needed to ensure it operates effectively, especially in detecting human errors.
9. Continuous Improvement and Compliance:
- Continuously monitor the system’s performance and gather feedback for improvements.
- Ensure that the solution complies with industry regulations and standards, such as Good Manufacturing Practices (GMP) and FDA guidelines.
JOIN THE CONVERSATION
By following these steps, you can systematically plan and execute a computer vision project that enhances efficiency, quality, and compliance in biopharmaceutical manufacturing. The ability to instantly detect handling errors and support human operators can significantly reduce risks and improve overall process reliability. Let us know about your projects! How do you automate complex monitoring tasks? We are happy to hear from you.
