J Student

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Great post! Your idea about collaborating with suppliers is fascinating. For example, the customers may come up with ideas- but they need to be feasible and implemented tactfully. To this point, Nivea can take a specific idea from the crowdsourcing with customer exercise, and then do a second crowdsourcing with suppliers – to understand how to produce this new product most efficiently and effectively, while still achieving the customer’s goals.

To your second question, “How will the Company maintain a cohesive vision so that external partners do not develop their own products that compete with NIVEA?”- I believe collaboration with suppliers can help mitigate this risk. By collaborating with external partners, Nivea is forming a relationship with them and signaling to them that Nivea values their opinions and work. Through this relationship, the suppliers may be less inclined to vertically integrate to sell products themselves, or less inclined to work with (or as closely with) Nivea competitors. In such a competitive industry, high quality ingredients reliable suppliers are key advantages.

On November 15, 2018, J Student commented on Empire State of Minds: Open Innovation in NYC :

Thanks for the interesting article. I would love to learn more about the process by which the city is trying to solicit feedback. Are they compensating the winners of the challenges? I would love to know how they are motivating people to get involved and share their ideas since these are important issues that they are trying to solve!

In addition, you mention that they have a closed model whereby participants do not see others’ ideas. You point out that this prevents copycats. However, I do believe there is benefit to participants having the ability to view other participants’ ideas, such as collaboration amongst participants. I wonder if there is a way to accomplish both (preventing copycats, and collaboration).

On November 15, 2018, J Student commented on Breaking the Molds: 3D-Printing and The Future of Shoemaking :

Thanks for your article. I agree that one huge benefit of the on-demand model is it will reduce excess inventory. One issue I see that may need to be addressed is return rates. Will product return rates go up? For example, since customers are not used to wearing such a customized shoe, they may find it uncomfortable for one reason or another. What will Adidas’ return policy be, if it cannot re-sell the customized return product?

Second, how will channel partners react? Adidas is a widely distributed brand and depends heavily on its wholesale partners (i.e. foot locker, finish line). Will wholesale partners be cut out, forced to carry the 3D technology, or measure the customer’s foot in store and endure a lag-time to receive JIT inventory from Adidas? If Adidas plans to do the last, can channel partners then share the customer’s data with competitors such as Nike.

These are just a few questions I had about Adidas’ execution plan, which I believe is key as it competes in the highly competitive shoe industry where Nike is the dominant player.

On November 15, 2018, J Student commented on Chanel’s Foray Into 3D Printing :

Arting, thank you for your post! I am a bit surprised that of all clothing/beauty brands to venture into 3D printing, Chanel is at the forefront. Given the ‘timeless’ appeal of the brand, I am not surprised that they came to market with a 3D mascara, instead of with one of their higher grossing products, such as a handbag.

As a consumer, if I’m spending thousands of dollars on a handbag or piece of clothing item, I’d much rather it be handmade than made by a 3D printer. One reason is that the item seems more authentic if it is handmade. The second, is I’m not sure if 3D printing is currently advanced enough to ensure the clothing or handbag is pliable/durable enough. [1]

I’m interested if in the medium-term consumer perceptions about how authenticity and value as it relates to production will change, as well as if 3D printing can become advanced enough to actually produce similar quality products to the quality of Chanel’s products today.

[1] D. Spaeth. 3D printing is changing the face of multiple industries. ECN: Electronic Component News 61, no. 9 (October 2017): 21–23.

On November 15, 2018, J Student commented on I’m A Barbie Girl in a Digital World :

Interesting read, RJ!
What came to mind when reading this is some of the toys I used to play with as a kid. I do believe even back then there was some use of machine learning (we had the ability to speak to Furbies and command them to do certain things). Yet, still the toy industry is very fickle, preferences change so quickly and competition is stiff. Trends come and go.
The question that comes to mind for me is whether or not significantly improved algorithms or substantially more powerful computer hardware (from then until now) can give this company a true competitive advantage. Can machine learning really lead to a competitive advantage given industry characteristics I mentioned above, now that machine learning is much better and more powerful than it used to be?

Thanks for the interesting information, Sam. Cameras and censors in lieu of checkout booths make the lives of customers easier, while also helping Amazon decrease its labor costs and better understand its customers.
From the read I didn’t quite follow how filming customers can help predict optimal inventory levels, above and beyond just using POS data. Is it because Amazon will now have a way to tell whether a customer glances at an in-store advertisement before picking up a product? Are they tracking facial expressions, sentiment, etc?
My belief is decoding human sentiment, feelings, and the like is far too complex of a task for machine learning. In the article, “How to Tell if Machine Learning can Solve Your Business Problem”, good business problems suited for machine learning are one’s that “require prediction rather than causal inference”. [1] Thus, I believe machine learning should not be used to predict how a person’s facial expression (or the like) can predict sales. Rather simple POS data (which could be gathered either from a checkout booth or cameras/censors), combined with logic (i.e. weather trends) seems more reliable.

[1] Fedyk. How to tell if machine learning can solve your business problem. Harvard Business Review Digital Articles (November 15, 2016).