There are a lot of different businesses in different industries and although they might have similar Quality Assurance processes is highly unlikely that they will have the same quality assurance process. I know some of the emerging technologies and methods that might be almost used within every industry to keep up with the effectiveness of the quality assurance department. These emerging technologies, although they are not a platform but using the same platform with different algorithms and methods. I am, of course, talking about programming languages. Every day there is an update for particular programming languages, and of course, there are some dead ones too that do not get any updates and eventually getting stopped in terms of usage. The top 5 of these coding platforms can be listed like this:
1) Selenium: Widely used for browser testing automation.
2) Appium: Widely used for mobile application testing automation.
3) Java: It has multiple different uses according to various businesses and industries. It has a strong language that can help ease the execute long codes, thus keeping things smooth while assisting with the testing automation process.
4) Python: It is an excellent programming language for not only text processing but also machine learning and deep learning algorithms. I mostly got exposed to academia, but it is also widely used by many companies to help with their automation process for quality assurance.
5) Matlab: It is an unusual language to be used in the industry for quality assurance purposes (at least I never saw or heard of any) in research and academia. It is widely used for not only doing analysis but also checking the quality of it thus; it is, in a way, helps with the automation of quality control and quality assurance.
All of these platforms, although they are getting updated, an essential aspect of them is the user or the programmer. Like I have said earlier, the platform is the same, but the methods and the algorithms change, and this is done by new individuals adapting and executing these skills. In conclusion, I can say the most emerging technology in quality assurance is again the emerging graduates from different universities.
I believe the future of AI and other technologies are going to be very useful in the biomedical engineering if its closely monitored by quality assurance and quality control personal. I would suggest increasing the rate of testing to ensure the safety of the new technology being used and to prevent any side effects for patients.
I believe keeping a relevant knowledge of new technologies is the same as any field: continue reading research and engaging in new educational classes or forums. I think continuing ones education in a particular field, whether it be through talks or meetings, shows not only interest, but the drive to want to advance one's self. This will allow a person to take new information learned and apply it to his or her QA team and process. For example, if a department head requires that the team learns a new skill or new information regarding the work, this will allow for fresh ideas and new opportunities for the team and the overall company because the outside knowledge will advance the in-house work and common goal.
In this age, different emerging technologies are beginning to rapidly change the nature of work. Internet of things(IoT), Artificial intelligence (AI), Machine learning are just a few examples of the new, disruptive technologies that are impacting the jobs in one form or another in a very short period of time.
Now the question is how can you keep up with all these technologies and make the QA process more efficient and more accurate.
Any thoughts?
This is a great concept to consider. In my opinion one of the best ways to ensure that QA is a priority for these technologies is to have a pre-established guideline for the requirements that must be adhered to in order to use the technology. In addition to these rules there should be periodic quality checks performed for these newly emerging technologies to ensure their safety and efficacy. To some degree these technologies will replace base level jobs. However, there will always be a need for people to monitor and fix machines and there will always be something positive about having human interactions.
Many have mentioned above the implementation of new technologies such as AI will help measure dimensions and automate processes. This in turn will help and rapidly increase production, since a computer can process data much faster than a human. Another application which I did not see discussed are vision systems. Utilizing cameras to judge the comparison to the correct image has already started to be implemented into several industries and is a useful means of quality control. Not only is this sensitive to chances in dimensions or missing parts, but it is color sensitive as well. Therefore, this type of technology has a wide range into many different products and would be a useful means in testing nearly all medical devices. However, there should still be manual checks from time to time just to ensure the system is working as intended.
I think it is very important to keep up with emerging technologies especially when it comes to the QA department as it facilitates their processes and makes it easier and more efficient. One of the ways to keep up with the emerging technologies is to simply keep attending the industry conferences and tech events which occur multiple times during the year. In these conferences, one can learn about new innovations and insights from their peers. Moreover, one can make new contacts and sharpen their knowledge from experts. Another way is to use intelligent search. An AI can be used to keep track of all new emerging articles on the web that have specific keywords relating to the industry one is working in. One can also join a professional organization where they keep updating their members about any emerging technologies through weekly emails. Furthermore, communication is key. Just keeping in touch with your peers and colleagues from other companies can help you keep up with all the technologies. An easy way to do this is to be active on social media like LinkedIn where numerous experts always post about the related industry.
Keeping up with the awareness of these technologies, I believe, isn't as difficult as you'd think especially with AI or machine learning that is related to the specific industry. However, keeping the company updated with this latest technology is the difficult part. Of course, the number one factor would be money. The other issue is with these updates you'd be affecting the workflow because operators are so accustomed to the current systems. It's similar to the idea that Dr. Simon mentioned in the lectures of how when a small company is bought out by a new company you can't just flip the place around overnight. Actually, a company that I worked for had just been bought by a bigger company when I first began working, and being that I was the first hire of the bigger company I got to witness the transition process. Having to get the team to get used to the new processes was anything but easy which is why it makes sense some companies stick to how they work since it's getting the job done and this ties into Quality.
A substantial way that I believe can improve the way we keep up to date with the latest technologies and implementations is by employing more younger staff and personnel. Another way is by utilizing forums that are open to students in the biomedical engineering field or medical field in general. The inspiration behind this idea is that if two heads are better than one, then 100+ heads are even better too. This is a way to broaden medical device research that doesn't involving looking over data that is so outdated is misconstrued so often. Overanalyzing age-old concepts is how misconceptions come into factor. If the actual person who wrote the rule, or the theory that is measured against, isn't readily available for questions people may have, then it is almost no help at all. At least with an online thought forum, you can chat back and forth with multiple people with experience or knowledge in the field. Not to mention it is a plus for networking as well.
@christoph Yes! Traceability through RFID tags or barcodes could help propel QA validations and time frames. Traceability of products through small identification mechanisms could readily identify batch processes and ingredients that may have been contaminated through the manufacturing process. While this may not be the most forward technological application, I believe it could streamline the process of validation and make way for new technologies to be incorporated into the manufacturing process. Management should also be willing to provide the technology and train their workforce to incorporate the technology in their QA process. Management also should be on the lookout for how these emerging technologies could be cost efficient and reduce errors. This may require hiring skilled personnel, but then the educational process can be passed on throughout the workforce to keep up with industry-leading methods.
Technology such as AI can be helpful in developing semi or fully automated quality inspections processes
I completely agree and to further this discussion I would say that AI is gaining popularity as the future technology because it tends to automate redundant learning and searchability through the data without any issues like fatigue. It helps to attain unparalleled accuracy, which was not achievable previously, and it can overcome Barriers, including language and economic barriers that may limit the company’s growth and development.
With newer technology emerging, I definitely believe companies should try to embrace these technologies and use it to enhance their QA and QC processes. Electronic batch records, documentation, and data is becoming a big part of the regenerative medicine field in a lot of bigger companies. Also, there could just be testing techniques that are used that QA could then implement into their guidelines for QC. Improvements on the testing methods for medical devices, such as failure testing, could be able to predict failure with more accuracy or there could be technologies that could make some testing more efficient. QA could then update and their overall guidelines so that it could have continuous improvement for QC.
Predictive maintenance is powerful IoT based technology used data tools to find issues with operation and alerts system about up time and possible fixes. It prevents machines going fully down. Model based condition monitoring was used by NASA in for early warning system in shuttles engine. This can boost Pharma production and will reduce human efforts in calibrating and continuous service checks.
Issue with AI within QA is traceability, like @am2343 or @Christoph have said. Traceability is the the golden ticket to quality and especially in audit. Mistakes from AI has almost zero traceability expect to the individual who employed it and even then, its the AI who modified it. Hence they were doing there job correctly but were given incorrect results. Issues with AI could come from the resources available to buy it, dependency on these tools if they get obsoleted or flawed, or security concerns when it comes to documentation. The change in rate of company standards to industry standards, to regulatory standards is a lot to consistently update for a software and easier understood if we had more of a handle on it. There a balance should be employed. Additionally AI could shift the quality's importance to a lower degree, which becomes a dangerous perspective in the long run.
I think that staying current with emerging technologies and incorporating them into the QA process requires a commitment to learning, experimentation, collaboration, and adaptability. If those processes were to be followed the QA process could be more efficient and accurate in the age of IoT, AI, and ML.
