This is so interesting! It sounds like 3D printing food could have a huge impact in areas where food shortages are a problem, and I would hope that this technology can advance and scale quickly enough to make this feasible from a cost perspective.
I would be curious to know how companies like Barrila think about commercialization in areas where food shortage is not an issue. For instance, in the United States, would the average consumer have concerns about 3D-printed food being healthy? Or would it be seen as another form of the processed food that we eat on a regular basis? I think food producers would have to determine if market adoption of 3-D printed food would justify the added costs of production. Alternatively, they could go for a more niche market and focus on customization of food products.
One more thing that comes to mind is regulatory implications. If I were to order a “personalized pasta,” would I even be able to tell that it came from a 3D printer? And what if it is discovered that 3-D printing actually does have negative health impacts? I imagine there would be significant regulatory steps required to get this new type of food into the market.
Thanks for sharing – I would have never guessed that machine learning was being used in this way!
The beer industry is an interesting choice of place to test this concept. I would think that data and feedback received from customers would be difficult to interpret simply because everyone has different preferences when it comes to beer. Also, I would think an additional challenge from the customer perspective might be the opposite of the “bad batch” concern you mention – what happens if I find a beer that I love, but then can never find it again because it has been changed based on the algorithm?
On a separate note, I think the idea of a “smart pub” is fascinating and could be extremely useful in understanding other aspects of the business (e.g., Demand forecasting, customer segmentation, etc.)
This is a great article! It is interesting to see how Sidewalk is taking citizens’ input to make their own city what they want it to be. I agree that it is probably difficult to tie recommendations coming out of this open crowdsourcing to actual actions taken; one way to do this would be to get the names of the people submitting the ideas and follow up with them, but then you’d run in anonymity issues. On the other hand, maybe you are right that the intent is more around publicity and making citizens feel better about the city they live in. An interesting set of questions to consider!
It is interesting to think of Chariot using open innovation in 2014 before Uber and Lyft had gained significant awareness in the U.S. While it seems like it (Chariot) would have been very innovative at the time, I’d be curious to see how it remained competitive as Uber and Lyft entered the market – especially after they began offering their lower-cost pool options. Would Chariot have had to adopt its business model significantly? Or, would it have scaled to an extent where it could group large quantities of people in a bus-like system and position itself as the lowest-cost option? I will certainly look into it to see what actually happened!
Either way, I think the principle of getting input from your customers on what they want is a concept that could be applied to any local transportation company, even those with more of an Uber-like model.
I think this is a very interesting way to show how 3D printing can have significant social impact. My question is similar to the ones you pose – while this is a great concept, how long will it take before 3D printing technology becomes affordable enough that it can actually be used by the global community? While it may always be slightly more expensive than more traditional means of delivering aids, at what point is the added benefit of speed worth the cost increase? It sounds like non-profits like Oxfam International will play an important role in promoting this technology for social impact use as it continues to develop.
In addition to using 3D printing in times of disaster, I think it could be used more broadly in impoverished communities to address everyday problems (such as homelessness; https://www.nbcnews.com/mach/science/could-3d-printed-houses-help-solve-homelessness-problem-ncna860791)
Very interesting read! Thinking through the “customer journey” at a hotel from my own perspective, it does seem that there are many areas where machine learning (and technology more broadly) could help enhance the experience. To your last point on how to differentiate using innovative technology, I imagine there is opportunity for hotels to leverage customer data from loyalty programs to learn more about their guests and create personalized offers / experiences.
As an example, IHG has a program called “Accelerate” that offers quarterly promos to Rewards Club Members. The promos are personalized at the customer level, but a major complaint is that they often don’t seem relevant to the guest’s travel habits (article here might be an interesting read: https://www.headforpoints.com/2018/04/14/ihg-accelerate-q2-summer-2018/). Perhaps this could be the type of area where machine learning could help?