Melina Anlin

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I like the OpenIDEO idea because it can bring in fresh perspectives to the company. When we have worked at the same organization for some time, we tend to be stuck in the “bubble” and this could prevent us from thinking more in a divergent manner. However, the risk here for IDEO is that the team might have to spend much more time reviewing and filtering ideas and this could lead to decreased productivity. I wonder how much incremental value OpenIDEO will bring to the company.

On November 15, 2018, Melina Anlin commented on The Rise of 3D Manufacturing in Footwear: What it Means for Nike :

I agree with you that Nike should consider transitioning their strategy to making customized footwear for the mass consumers, rather than simply targeting celebrity athletes only, especially because their main competitor, Adidas, is already doing so. The question here would be how fast can Nike scale up their customization/personalized design process to make it accessible for consumers. Furthermore, the idea of having customized shoe designs getting 3D-printed in Nike stores would be a massive attraction to pull consumers into Nike stores.

On November 15, 2018, Melina Anlin commented on Invisalign: A Pioneer of Mass Customization through 3-D Printing :

As you have pointed out, I would worry about how long Align could keep its competitive advantage in 3D printing of liners, given that it has been replicated by other startups. For example, Candid, a direct-to-consumer teeth alignment company has managed to use the same technology in producing customized liners for consumers at a fraction of Invisalign’s cost (Candid costs ~$2000 while Invisalign costs ~$8000), because it removed the middlemen (i.e. the dentists) and is able to ship liners to consumers directly, making the product more accessible. Rather than pursuing innovation in additive manufacturing that could easily be adopted by competitors, I would recommend Align to focus on improving their existing technology in areas such as designing liners that could help consumers correct their teeth faster and more accurately.

I agree with you that there is a potential here for Toutiao to use its machine learning technology to filter fake news more proactively. I could see that they would be able to create value in users and society should they be able to help filter fake news on their platforms. With regards to your closing question, I think it would be a very fine balance for Toutiao to take in drawing the line between creating social responsibility and encouraging users to spend more time on the app. It would be very challenging for Toutiao to not include any hard news in its “curated content” to users if some users are only looking for soft news on the app.

On November 14, 2018, Melina Anlin commented on Your Personalized Dinner is Served :

This is an interesting space within nutrition food that Wellio is carving for itself. One thing that I thought Wellio could do is to engage in a subscription model because it needs to quickly gather as much data as possible in a short time frame in order for its machine learning algorithm to work effectively. I would also be curious to know the level of personalization that Wellio is striving for with the amount of data that they have currently. Is it possible for them to really personalized on a customer level basis or will they segment customers into different groups and each group will receive the same personalized nutrition?

There is definitely a barrier for consumers to switch from traditional skincare brands/products and switch to the personalized skincare category. Companies like Proven needs to prove themselves that they can be more effective than traditional skincare brands in increasing skincare efficacy. I agree with you that they would need to have a much more robust set of data from both satisfied and unsatisfied consumers coming from a spectrum of skin types and ethnicity, and then increase the speed of adoption in consumers to try their products in order to track and gather data on its product efficacy. If Proven can do this initial steps successfully, I do see a potential for them to take over shares from the traditional skincare brands in the long run.

It’s very interesting to know that Nestle has been using Open Innovation as part of its R&D in the past 10 years. This is definitely a move to show that large MNCs are staying ahead of megatrends and actively seeking ways to innovate and stay relevant. With regards to your question in the end, I think Nestle should take a proactive and decentralized approach in attracting and incubating startups. For example, they could host a regional/local startup pitch competition on a specific topic that they would like to get more ideas of. I see a win-win situation here for both Nestle and startups in Nestle’s Open Innovation model – Nestle gets new ideas and startups could tap on Nestle’s financial, marketing and sales & distributions capabilities to get their products to market quickly.

I agree with you, Peter, on the question on whether social media apps like Toutiao/Bytedance ultimately create or destroy value for users and society if their ultimate goal is to make us spend more hours on their platforms. I think most users had unconsciously trapped into the additive nature of the apps that is fueled by machine learning. With regards to the proliferation of fake news on social media platforms, I wonder if Toutiao/Bytedace has thought about using machine learning or other mechanisms to filter out contents that are fake or sexual/violent.

On November 13, 2018, Melina Anlin commented on How can ML help Fox predict box office performance? :

Thank you for sharing, Miguel! This is a very interesting read. I agree with your point on the machine learning technology that is applied currently is somewhat used too late in the process, as the movie has been produced and millions of dollars have been spent as sunk cost. It would be interesting to see how companies like Fox can apply machine learning before the production process and how they can balance the use of technology with creativity in the movie industry. My other concern was the use of trailers which constitute a very tiny portion of the whole movie, as an indicator for the movie’s success. I wonder how accurately can the technology predict box office performance based on this minute set of data. Another point that is worth noting is how applicable machine learning is for movies that are made in the niche segments (that would not be a box office usually, as they cater to a very specific and narrow set of audience). In addition, if machine learning was to ultimately inform film studios and producers on what scripts to write and types of movies to make, then, where would the role of film makers’ creativity and passion be in this space?