I see how the open innovation strategy that LEGO seems disconnected from its younger users who are moving more into online play. This trend of moving away from physical toys would certainly seem to threaten the future of the company- however, it is not the full picture. As an educator, I have used LEGO Robotics in my classroom to help students reap the benefits of playing both in the physical and digital world. I think that the company is missing an opportunity to gather the innovative ideas that children come up with since they don’t collect as much data from the classroom. Perhaps, rather than looking at how to connect with students in their social time, LEGO open innovation could look at how to collect data from the prototypes that students draw and extending the viewing and review process to students’ work. The incentives process would also have to be tailored to become child and family-friendly to ensure that parents would consent to their children’s work being used in this process. I’d love to get your thoughts on whether this innovation funnel could work in a classroom setting.
I think this is a classic case of the iron triangle of cost-control-scope. While the lever of control is untouched, cost and control are at a tradeoff. For the $50,000 cost, the team should lower the scope of what it means to be successful. I think altering the incentives, so that the tiers are aligned with the actual value of the inputs that the startups give could increase their yield in this project.
My second theory here is the open innovation channels and audiences here are mismatched. Partnerships entities like Linqia (Influencer Optimization), Crowdly (Word-of-Mouth Advocacy), and NextUser (Marketing Data Analysis) all are used to working with high amounts of data to create success. However, data density/availability is not evenly distributed across the globe. Thus, the partners themselves could be using asymmetric data and pushing the challenges to the wrong audiences that might not be interested in micronutrient deficiencies or PET projects. I would hesitate to write off this project’s success on account of not finding enough technical startups- and instead study the profiles of who is applying to the challenges to understand who is missing from the equation.
Quite thought provoking- 3D printing, if it becomes affordable could make fast fashion even faster, and it could make high fashion more accessible. I think the longterm effect of home-based 3-D printing will be to kill “middleman businesses models” like Stitch Fix and Rent the Runway which, unlike 3-D printing, will incur recurrent costs around shipping and handling as well as the “origin businesses models” that involve fabric weaving. The main gainers from this switch will be artists who will be able to put in their designs as intellectual property and earn income every time an outfit is printed. Home-based 3-D will also create new business opportunities e.g. shared 3-D printers in apartment buildings.
The second megatrend that could intersect with this is machine learning. Imagine if after observing your outfit-printing habits, your algorithms could start recommending outfit combinations for you to print and wear? Wouldn’t we be freed to enter a world of amazing levels of self-expression?
Thank you for this uplifting piece. I agree that this particular launch reveals the both the advantages and limitations of 3D-printing. In my opinion, 3D-printing is often put up as a silver bullet that can be used in manufacturing across different industries. The truth is that given the high cost implications of 3D-printing, we’ll end up using it mainly for products and parts that need the high fidelity levels afforded by computer aided design. For products with more wriggle room, or where the customers are okay with minor variations and imperfections, we’ll continue to work with grinding and milling. 3D-printing applied across fields where “perfection” is not needed will result in Muda Type II wastes. Keeping Toyota in mind, we should cut off all Muda.
@Traderjoe, thank you for highlighting such an important topic, along with its increasing relevance to daily life on the internet. I agree with your perspective that insights from Paypal data can point out the relationship between customer churn and critical product features. However, I think the potential applications are much broader, and can even go in the reverse direction. Instead of starting from customers’ data and using it to decide what features to keep, we can go backwards, starting from features and group different types of users/build profiles of users- and then offer them the features that matter most to them. What do you think of user profiles as an intermediary step before the customer churn usage? I would love to hear your thoughts on whether this would be a useful direction.
I agree with you that the question of “who pays” is important. I think it’s not insurmountable. Training datasets used for Watson and other western-based AIs lack information many countries in the world.The lack of global data is an opportunity to commercialize existing national data sets and earn money to subsidize access to health AI benefits for the populations in these countries, while improving the AI’s predictive power.