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On November 15, 2018, LxHuang commented on Everything is Awesome: Product Innovation at LEGO :

I definitely thought about how the customised kit could work. The possible reason for shutting down Designbyme is that picking out bricks for customers and packing them into a box set may be too expensive from a labour utilization standpoint. I envision the new system as a combination of a digital tool to parse how many and what type of bricks are needed, and a link to LEGO’s Pick a Brick ordering platform. Essentially, the platform will churn out a “recipe” for custom sets that anyone can buy, with a portion of sales going to the creator as commission. The platform works more like sites sharing opensource codes than a tightly controlled toy distribution channel.

You are right about the workforce issue! If reading cases doesn’t occupy whatever little time you have left, you can take a look at the design process LEGO’s internal teams use: https://www.designcouncil.org.uk/sites/default/files/asset/document/ElevenLessons_Design_Council%20(2).pdf

Yikes! So many forms and admin work. They need to take a page out of Agile development process in software engineering and cut down overhead in their design process.

On November 15, 2018, LxHuang commented on Everything is Awesome: Product Innovation at LEGO :

You are on point about a lean competitor building speciality designs! In fact, the company’s CEO is a 9-year-old (now 13) LEGO diehard fan: https://www.brickloot.com/pages/how-it-works
One of the papers referenced in the article describes the phases of user-innovator cycle (von Hippel), and the next phase after a market for innovation develops is that user-innovators will develop their own products based on the original technology. I don’t think LEGO can sue for IP infringement if someone develops an original set using Lego bricks, pack the right quantities of each brick type and then ship it off. That is also why keeping users engaged on the platform is very important. If compliance is too onerous, users will leave the platform and independent ventures to pursue their ideas. Definitely going to be LEGO’s loss!

On November 14, 2018, LxHuang commented on How Additive Manufacturing Can Unlock Boeings Biggest Challenges :

The article brought up very interesting perspectives on how additive manufacturing can effectively reduce program costs of Boeing aircrafts. To your question about 3D printing of special materials, I believe extensive research is being conducted to develop technologies to print special material components. The benefits are two-fold: 3D printing can significantly reduce wastage during the manufacturing process, and new designs can be printed with much less part than traditional manufacturing techniques. For example, Boeing’s 787 makes extensive use of titanium parts, and 3D printing can cut costs associated with these parts significantly [1].

Currently, the costs of 3D printing large parts are prohibitively expensive, and traditional tooling methods still have an advantage over additive manufacturing during the mass production stage. I think we will definitely see more complex parts being produced through additive manufacturing, but Boeing still needs its suppliers for large parts.

[1] https://www.theverge.com/2017/4/11/15256008/3d-printed-titanium-parts-boeing-dreamliner-787

Comparing the failure of Quirky to the success of Innocentive, a challenge-driven innovation platform, my opinion to your first question is that Quirky did not have a strong relationship with enterprises to monetise its innovative ideas. The funding pressures worsened the problem, but the business model itself may not be financially viable to begin with.

I definitely agree with the previous poster on the disposal of their manufacturing business. It is a detour that Quirky was ill-afforded to take since scale is a huge determining factor in manufacturing costs. I would love to see more on the economics of commercialising open innovation ideas, and if it will make sense for companies without the backing of traditional companies with deep pockets.

On November 14, 2018, LxHuang commented on Will you marry me (if I ask with a 3D-printed ring)? :

Great to see an article about additive manufacturing and its applications in jewellery making.
One of the benefits in addition to cost and time reductions is that 3D printing allows designers to produce intricate, organic shapes that are impossible to produce using traditional wax molds. Digital models add to the freedom for clients to customise each piece according to their own needs. I think the value proposition of additive manufacturing is not replacing the traditional production method but improving the prototyping process.

To your last question, the romantic notions attached to the craft of jewellery making will be a bigger barrier of adoption for luxury brands like Tiffany. If the technology improves to the point which custom jewellery can be offered at a fraction of the cost of traditional pieces, there is definitely business opportunities for startup to capture the market.

On November 14, 2018, LxHuang commented on Shooting for the Stars :

I am intrigued by the fact that NASA has opened up its patent portfolio so that entrepreneurs and institutions can leverage on its knowledge and find new applications for these patents. Outbound innovation did not gain as much traction with organizations due to concerns about intellectual property protection. NASA is one of the rare exceptions, perhaps because it is a public agency that does not depend on its intellectual property for profit making.

On the other hand, it is unclear if tournaments or crowdsourcing platform can effectively solve the STEM shortage. While crowdsourcing can effectively outsource simpler tasks like data collection, subjects that require deep knowledge and PHDs may not fit well into the open innovation model. NASA’s internal R&D will remain as the engine of innovation for the organisation and the need for highly trained STEM talents is unlikely to abate anytime soon.

A very interesting topic to explore! I think demand forecasting has huge importance in the utilities industry. With the advent of smart sensors and IoT, we will definitely see increasing importance ML will play in making sense of the data produced by the network of smart devices. I would envision that someday we will have real demand-side management solutions through a combination of remote devices and predictive ML algorithms.
I agree with you that jobs in the utilities sector may be affected, but displaced workers may be retrained to operate and maintain the smart device network and the verdict is still out on the net impact on jobs in the sector.

I must applaud the engine’s sense of fashion, since it’s recommendations of Taobao fashion shops to me are pretty much on target. On a more serious note, the algorithm may have captured the holy grail of all marketing professionals: How to make consumer buy the product. The recommendation system is self-reinforcing because the users liked what Alibaba recommended them and went ahead with the purchase. While the omniscience of the engine may appear disturbing, in all likelihood it is also a mirror that reflects consumer tastes and preferences back to us.
More interestingly, this system seems to produce winners with no clear merits over their competitors’ offerings, as you have pointed out in the essay. I wonder if this is because the algorithm amplies small differentials in preferences by pushing recommendations to more people?

On November 13, 2018, LxHuang commented on DoorDash and Machine Learning :

The relentless march toward automation and machine learning may be finally coming to the food service industry. I will be interested to see how competition between various delivery service will drive the development of final-mile delivery robots. Given that the delivery service industry provides low-skilled employment for many workers, it is the worrying that the last bastion of human labour, the service industry, may one day be run by hordes of Amazon drones and Dasher robots…

I suspect people are willing to give up bits of their personal data to gain access to personal credit. As you rightly pointed out, a line needs to be drawn somewhere. Otherwise we run the risk of unfairly penalizing people for behavioural factors that exhibit correlation but not causation to lower credit rating. There needs to be transparency in showing consumers how their Zhima Credit scores are derived and for the industry to establish standards on what are acceptable data inputs to keep it within ethical bounds.

On November 13, 2018, LxHuang commented on Machine Learning and Music: What About the Record Labels? :

One of the interesting trends also is that record labels increasingly derive their revenue from live performances. While digital distribution is less lucrative than selling CDs, it increases the reach of local artists exponentially by connecting them to a global audience and thus increase the demand of Lives. Sony needs to shift away from a heavy reliance on music distribution to a business model that leverage on the IP rights of the record label.
There are also plenty of emerging opportunities in the mobile space. As a player of Grand Order, I can attest to the crazy amount of money people are willing to pay to purchase desirable virtual goods. Perhaps a collaboration of online/offline campaign for Sony’s artists can be the next money spinner for the company?

Very well research and thoughtful piece of article! With the rise of ML, insurance companies are adding new data points into their actuarial computations, which can help them underwrite affordable policies without the administrative overhead of highly paid human actuaries. We may see the rise of no-frills policies that can vastly improve lives of low-income groups that could not afford a traditional policy package.
I am not sure if I would be comfortable with insurance companies crawling through my social media feeds though, and the use of big data to assess insurance premiums could raise privacy concerns. Will my insurance premium go up after one too many pictures of me gobbling up pizza? There is a limit to how much information people are willing to share with their insurance providers, and it will be interesting to see how this space evolve.