Your example of disaster relief is one of the strongest examples of open innovation being used for good. It’s a compelling cause that the social media companies you listed could do more to help. I agree with your points around preparation and response to disasters. While the information is hard for the IFRC to control, they could share more best practices and training to help responders and contributors give more timely, relevant information in crisis situations. The question about balancing information availability and public access is tough. Perhaps there could be automated technology to help, for instance, take sensitive information offline. Thank you for sharing these thoughts, looking forward to following how this space develops!
As one of the oldest FMCG companies out there, I find this topic of Unilever outsourcing their product development process very interesting. Companies like Unilever have traditionally tightly held their secrets. It’s great to see them out there collaborating with startups to come up with new ideas. I agree with your point about alignment between innovation and strategy. Without direction, these products could very well end up in the zone. One way to make this work, to your question about crowdsourcing being an enabler/constraint, is to buy ideas that are already developed and fit on the broader strategy map. Unilever’s purchase of Dollar Shave Club illustrates a way to bring more developed ideas in house (http://fortune.com/2016/07/19/unilever-buys-dollar-shave-club-for-1-billion/). This way, a smaller, more nimble team can source the idea, take on the risk to develop it, and Unilever can bring it in house in order to apply their management expertise at scale. There seems to be a lot more growth left in this space!
Great choice of a popular consumer application of learning models! Spotify’s Discover Weekly feature is an amazing example of machine learning’s relevance in the real world. They have capitalized on the complexity of the data and used algorithms to successfully synthesize musical preferences for users. Their success seems to rest in what you discussed as the continual loop of data feedback – more listens, more preferences, better suggestions. What’s great about Spotify is its simplicity and relevance, this is where I think introducing video content could be a tricky proposition. While Spotify is well positioned to introduce music videos along with songs, at what point will its new content cross over into the social media category with its own set of challenges?
This is a great piece on a retailer successfully integrating machine learning techniques into their business. Burberry is in the special spot of being digitally forward, while many others do not have this choice. This article talks about how digital is built into their culture and is not just projects they pursue for short term change: https://digiday.com/marketing/burberry-became-top-digital-luxury-brand/.
Your question about the security of third parties holding data also merits a lot more thought. Retailers have traditionally faced the challenge of having many digital initiatives they want to take on while not being able to hire enough technical talent in house. Thus, in order for most retailers to succeed at truly personalizing the purchase experience, they need to really invest in understanding security requirements and bringing on the right external expertise to help them accomplish their goals. Thanks for sharing!
It’s very interesting to think about one of the most classic companies trying to change their production process using new technologies. Given that additive manufacturing is traditionally used on faster, more limited prototypes, do you think GM has the ability to transform the technology to be effective in car production? Since there is so much at stake with auto manufacturing, it looks like there is still a ways to go for the cost and efficiencies to catch up. There have been some attempts at using this method as a supplement to test or restore cars: https://all3dp.com/2/10-coolest-3d-printed-cars-of-2018/, so it looks like there will be a lot more progress in the future. Thank you for sharing!
This is a great topic and one that I’m sure will become ever more important to the future of national security as additive manufacturing technology develops. On that point, I agree there should be more training for service members to be fully fluent in this competency. Reducing a prototype from 581 down to 4 days has significant benefits and could be huge in urgent situations. It would be amazing if this could be used to evaluate other inefficiencies in the DoD. Given the differences in requirements, do you think these methods of production can be deployed at scale across the DoD?