Great effort on putting 800+ words in 799 with abbrevitions and eliminating spaces! 😉
For the content: I think open innovation can be successful with scale, when you have a large enough dataset to filter out outlier ideas. If you do not have enough data, you are running the risk of implementing something marginal on a high cost. For this, customer engagement will be key and I think the interesting question is how to engage them without compromising any major pieces of the strategy (e.g. pricing, place, etc.)
Very interesting topic! I definetely see the value in prototyping with 3D printing especially in Nike’s case where they iterate their models based on expert (professional player) feedback so many times. However I have concerns about the mass production applications – maybe because I do not know much about 3D printing. Would it hurt super high quality? I always had the feeling (maybe it is incorrect) that a human workforce can do better quality products than a 3D printer. Also – how many people would end up without a workplace if large manufacturers take this direction? Probably it is inevitable, but interesting aspect.
For me the most striking part was about operations planning. It is clear why you cannot do just in time, but what can be they best way of scheduling the raw material deliveries to optimize inventory / blacklog? This is a much more difficult setup than any other transportation question we studied. Very interesting!
Thanks for the comment, I really appreciate it! I would be happy to discuss further, because I do not fully understand your logic.
This is what I think: yes, Waze is working together with the police to make the app safer (i.e. the police gives road block info to Waze, what is awesome). However the question of speed traps is a different one. The police is actively fighting against that from 2 reasons: 1) people want to avoid speed traps to be able to drive faster (what is less safe than following the rules). I truly don’t understand how the info on speed trap locations can help to improve safety, it is just not logical to me. Although it is a great user magnet. 2) Police men were killed because Waze uncovered their locations (for more info on this, please check out my reference  for example.
For your Google point: what Waze is doing is not illegal, just unethical, moreover supporting the unetchical behaviour of the customers, so directly not even unethical. If the users are ethical, Waze is ethical – and that was my whole point. If crowdsourcing is your key operating logic, how can you make sure that your business is fully ethical with unethical users?
I am looking forward to see tour thoughts! Thanks again for the comment!
I think one of the biggest obligation is channeling in the opinions / ideas of a diverse enough group to be able to make decisions which covers every possible point of view. As you mentioned this is extremely difficult and I would put most of my focus on figuring out ways to reach out to as many different people as possible. Built-in translator and the offline program sounds great starting points for diversification, but some kind of balancing (towards these alternative ways of outreach) will be also needed, because most of the comments will probably come from very similar mindsets.
This is genius and scary at the same time. I think it can work pretty well with frequent customers who has a “predictable taste”, i.e. they eat mushroom pizza all the time, but I do not see it working with people who has random ideas, not a usual pattern of eating out / ordering, just something more spontaneous. I also see a risk in losing customers who decide to change their diets (what is more and more common), e.g. they can have a commitment of eating healthy from tomorrow or losing wait, etc., and then a pizza shows up at their door at day 1 of the new life as the biggest temptation based on their historical buying pattern, what they would like to change. This can be a real pain and can turn away loyal customers.
Interesting questions! For me this sounds a bit like a chicken vs. egg problem. I see two potential revenue sources when the business model is fully ramped up, one is from loans (so interest rate from the core activity) and the other one is from the extremely valuable data they are collecting (expenses can be relevant for any kind of retailers). Sufficient amount of data is needed to enable both revenue streams, without that it is just not working as you described. I am wondering it the company should sacrifice on loan revenue (i.e. taking up more risks and giving discounts) to actually fuel the growth of the dataset which will pay back in two front, through loan revenue (volume game) and through database revenue (if they can sell their aggregated data anyhow).
Nicely put thoughts! I was actually wondering who LEGO’s real target group is. A lot of our peers (20+ year old people) are buying LEGO for themselves or for their friends as Christmas gifts for example, although the box says that the toy is for kids between 4-9 year old. I have just bought a Millennium Falcon to my partner, who is 28 years old and he can’t wait to build it. I think LEGO should really extend its target group to older audience (maybe with more sophisticated toys) and then focus more on crowdsourcing. The future is in customer engagement and the best way for that is involving customers in product development. LEGO is on a good track for that, but could push it more.