Bob Appel's Profile

This was a great article! It’s interesting to me how many different industries are investing heavily in AM without having a clear direction regarding its commercial applications. In Hershey’s case, 3D printing appears to provide greater product design and prototyping for new products; however, it’s hard for me to comprehend how AM contributes to supplementing specific nutritional requirements for consumers. What specifically does this technology do that allows it to create more exact, desired macronutrient proportions than the traditional methods currently does? If possible, the properties of AM seem to have groundbreaking technology that’s applicable in the nutrition segments of the food industry.

Thanks for sharing this insightful piece. It’s interesting how Wikimedia has been able to create a platform (or engine) that runs relatively well without much intervention, editing, and oversight. I really liked the idea of identifying different ways in which the company can incentivize high-quality users to produce content, whether it be through money, credits/tokens, or social recognition. As mentioned in the article, my main concern regarding crowdsourcing and open innovation is the lack of content integrity that may be perceived, EVEN when it is highly reliable and unbiased. Consequently, what can a Wikimedia do to gain the trust of consumers while preventing biases from becoming prevalent when using crowdsourcing?

Really insightful piece! This is an example of machine learning that makes one ask how long an organization should fund potentially groundbreaking technology rather than recognize the R&D as a sunk cost and move on. Unfortunately, it seems as though UA ventured outside of its core competency and began investing capital in a space that it lacked knowledge, underestimating the task at hand. On the other hand, can you blame an athlete first company for trying to push the boundaries of health and fitness through the use of innovative technology that would significantly help it capture market share from its largest competitors?

Very insightful! Crowdsourcing for PepsiCo has seemed to be a resource used to reduce capital spending and processing time during innovation. I’d be curious whether large CPG firms realistically envision these ideas to be long-term sustainable revenue engines for the foreseeable future or marketing ploys to attract more eyes to its brand. Though these contests provide an efficient way of gathering a wealth of ideas, actually testing and producing a desired flavor seems to be the overarching issue. What would it take to make the “Do Us A Flavor” challenge produce a viable product in the long-run? Would it be feasible to crowdsource the tasting component in addition to the idea gathering process?

Thank you for providing insight on the use of additive manufacturing (AM) in a quite distinctive industry and segment. It sounds as though this startup recognized the value of 3D printing during the iterative phase of product development before going to market. It makes sense why AM is the optimal tool for prototyping in the earlier stages of ideation; however, it’s hard for me to believe that the use of 3D printing is scalable in this industry. Once Centimeo develops the proper prototypes (e.g., vending machine shape, product packaging, etc…), why would AM be the most cost-effective way in commercializing the idea? Intuitively, I would presume that more traditional means of vending machine manufacturing would be more efficient than relying on 3D printing technology, which seems to be currently uneconomical for mass-production.

I found this to be a very interesting piece on a lifestyle trend that seems to be growing exponentially. There’s a very thought-provoking episode of Netflix’s series Black Mirror titled, “Hang the DJ”, which analyzes the pros and cons of machine learning and dating apps that you may consider giving a watch.

A few concerns that came to mind when thinking about the integration of machine learning and dating apps are the following:

1. When identifying “undesirable” and “desirable” matches, what is the algorithm doing to promote diversity within relationships rather than simply linking people together who share relatively homogenous characteristics?
2. Does machine learning posses the capability to match people based on attributes other than physical preferences given that most of the user input during the “matching” process derives from users making a decision based on photos of the other individual?