You bring up good points about the submersion and drying times of the bottle label, since the timing is important for adhesion and how strong the adhesion is. With more time, more bonds can be made, allowing for a stronger stick. However, the surface of the bottle itself is not considered in your analysis. If the bottle has residue on it or oil, then the adhesive may not bind properly. This would result in label failure. I think a surface preparation step should be included to ensure that the bottle itself is not causing issues. This step would be standardized across all bottle preparation so it does not become a confounding variable. These steps can include cleaning and drying the bottles before labeling them, allowing for improvement in consistency. 

In addition, the environment that the bottle is in during drying may play a role in the issues with the label sticking. Humidity can alter the strength of the adhesive. This would lead to the failure of the label adhesion. Temperature may also affect the bonding material and strength. Monitoring these conditions should also be looked into to make sure the issue is not just with the amount of time given to the label for adhesion. If the bottles are intended for a specific environment or storage conditions, those conditions should be checked as well. 

From the project management perspective, the importance of defining test variables is clear here since the real world can be very variable and lead to unexpected problems that customers can face. This brings down the credibility of the medical device company and the team itself. Making sure to properly adjust test parameters can make sure the downstream quality of the product is not compromised. 

What other factors do you think affected the success of the label? How can these problems be addressed? Is there a way to integrate AI to develop a virtual testing environment that could save money instead of testing the conditions in real life? How reliable would this be, and how can we make it more reliable?