Mattel turning around their business using digital innovation, focusing on machine learning for their unique product development
Interest article Masato Nakamura! Really helps us contextualize how even the biggest companies can face issues related to innovation despite all the monetary or talent resources. Would be interesting to understand how companies similar to Mitsubishi crowd-source innovation – for example in the automotive industry Toyota and Suzuki are focusing quite a bit on getting any and all ideas through the funnel, so would be curious to know why Mitsubishi still applies such a rigorous process for evaluation of ideas. Would also suggest Mitsubishi to hold competitions, partner with universities, within and outside Japan to get the most “raw” ideas and then develop them along the way. This way the funnel of ideas will be extremely wide, but they can still use management’s or other experts judgment to pick those top 5 ideas.
Thank you for the article on open innovation! Definitely reminisce the days where going to the Upper East side was a struggle. Your article raises a valid question on how to make the process transparent yet protected, so people don’t feel like their idea can be stolen. In addition, the ideas run the risk of becoming irrelevant if the time to implementation is similar to the one for the Subway systems. However, in addition to the time to implementation, how can the funneling of ideas happen so that many ideas are narrowed down to a short list. Would be helpful to know if there are specific KOLs or other groups who are responsible for judging these ideas that can protect the ideas, but also get insight from a group of people with different perspectives.
Thanks for the article, Carlos! Very important considerations raised around the financial aspect and certification for Technology Readiness. In addition to the certification, would be curious to understand how GE is participating with universities and colleges to add additive manufacturing to the curriculum. Particularly, if GE partners with current engineering students, it could create innovation early-on and leverage the other expertise that students have in relevant industries – aerospace, mechanical, electrical, industrial etc.
Moroever, if competition brings this technology as well, how will GE respond? Will it need to just expedite the process of prototyping further – i.e., making exploitative changes vs. exploratory changes?
Interesting article! Thank you for sharing. Raises a very important question around the social impact of additive manufacturing in the housing context. Would like to understand how additive manufacturing can be sold as a time-saving even for large contractors. Particularly, since contractors and real-estate builders have significant capabilities in building houses in the shortest possible timeline and with least resources. What are the benefits that additive manufacturing can provide to such contractors?
Additionally, if additive manufacturing can also help in creating several models of houses through 3D printing, potentially it could be used to test out weather conditions. Particularly in areas with heavy wind, flood and rain conditions, actual 3D models could be used to simulate several different scenarios, test out weather-abilities of different construction materials, identify areas which need to be marked out where construction shouldn’t happen based on wind/flood conditions.
Thank you for the extremely interesting article on AI in a totally new context – marketing! The questions you have raised around integration of AI copywriting with human copywriters is at the heart and core of the subject, to distinguish what a machine can do vs. humans. I am curious to learn more about how AI actually fosters more creativity? Particularly, if AI is driven by historical copywriting trends, how does it create a completely new type of copywriting? What are the inputs used for it to test out different copywriting methods? Moreover, how will advertising companies differentiate themselves if a machine is doing the task of copywriting – as you have pointed out, perhaps the human copywriters and their art to distill that information will continue to be the key differentiating factor. This research and article, almost reminds me of the Gap case study, where we try to understand the role of a creative director and if a computer and machine learning can truly replace the “art” part of the creative director, while helping the “science” part of their job.
Interesting article on a totally different format of in-store purchases – keen to visit a store myself 🙂 Your recommendations on personalized experience and increasing basket size based on altering product locations was particularly relevant. If we consider the customer segment that comes to a store like this, it would be the millennials who are extremely digitally-adept and care about their time. In addition to your recommendations, perhaps Amazon could also monitor this location pattern to see if there are ways in which the customer’s time inside a store can be reduced? For example, if most customers buy milk and bread, placing them next to each other can reduce time – something that the consumer will care about a lot! Moreover, as Amazon decides to take over the entire retail business of the world, would be interesting to see if Amazon is willing to sell this technology to other brands who will perhaps never merge with Amazon and sell through them – e.g., luxury brands, drug companies etc.? Could Amazon gain revenues through selling technology, given its vast research in this space?
Thank you for this interesting article! Raises a very relevant question around future of pharma companies in light of the digital technology and advances made to reduce the time and $ investment in the product development. Particularly, raises questions around how barriers to entry are changing in the pharma industry. Roche has been a leader for several decades, but how will that change if startups and other competitors can also emulate the same machine learning strategy that Roche is currently deploying. Will be interesting to understand what the competitive difference will be then, if machine learning costs are substantially lower than current tools/systems for clinical trials?