I am struck by how iterative Clover’s menu selection process is. Clover changes 80% of its menu in a month. Is that enough time to evaluate any item and conclude that they are a successful component of the menu? Moreover, are customers attracted to Clover because their food is getting more delicious as part of their open innovation process, or are they attracted to the constant variety? It would be interesting to test whether their menu today is better than their menu a year ago. In other words, is the customer feedback leading to a better product? If that is not the case, then Clover may want to explore the accuracy of their data. It may also lead one to question whether customers actually know what they want.
I agree with your comment that additive manufacturing will allow greater customization of jewelry, especially when considering that both the mold and the jewelry itself can be 3D printed. As we think about greater customization and the printing of one layer of material on top of another, I wonder if we can customize not only the design of jewelry but also the way we use precious materials to create jewelry. For example, I can envision a piece of jewelry that is made of multiple precious metals that don’t intermix. Perhaps additive manufacturing will allow for more precise designs where we can also personalize how we use precious metals within jewelry.
As mentioned in your article, one of the benefits of additive manufacturing is a greater ability to customize products given shorter set-up times. I think this capability is particularly relevant to the footwear industry, as each individual’s feet are shaped differently. I could envision the possibility of Adidas creating shoes through additive manufacturing that are specifically shaped to your foot. Perhaps the company can start by only focusing on individualized insoles created through additive manufacturing. With such an individualized product, customers would be willing to wait longer for their products. As a result, you could concentrate your production in one facility and then ship your product to the customer.
You pose an interesting question: Is machine bias equivalent to human bias? This case would seem to indicate that the answer is “yes.” However, I wonder what data was used to identify successful candidates. While it is clear from the article that Amazon used information from applicants resumes, it is unclear how those inputs made women appear to be less capable candidates than men. Was Amazon using supervised machine learning and was the historical data that identified successful individuals biased? This is a case where bias from machine learning probably reflected bias within the firm or from society as whole. We should also be careful to ensure that machine learning allows us to correct our biases instead of perpetuating them.
I commend LEGO for seeking ideas beyond those of its employees and for rewarding external parties that provide successful ideas. However, you mentioned that designing sets “requires significant technical skills and prior design experience.” I wonder if the existing requirements of their open innovation process are doing the opposite of what is intended: limiting the amount of ideas submitted due to the high level of requirements. I agree that one way to increase the amount of ideas is to improve the skills contributors. However, LEGO might also want to consider changing the requirements tied to submissions and subsequently refining the design themselves through internal capabilities. In this way, more people could contribute to their open innovation initiatives.
You mentioned that the machine learning algorithm is identifying poor quality feedback such as “Good job.” What exactly is the machine learning to do? Is it gathering data and learning what constitutes a good versus poor quality feedback? What ultimately defines a good quality feedback? It would be interesting to tie the machine’s learnings to the actual performance of the students after they have received their feedback. In this manner, good feedback would be categorized as such only if the students’ performance improved. While we would be assuming that all students proactively change their behaviors to incorporate their feedback, the ultimate goal should be “effective” feedback as opposed to “good” feedback.