This is a fascinating article and a topic that I’ve never heard about before — thank you for educating us on this! While I think the idea is extremely creative and has great potentials, I do question whether it solves the challenge of space travel (or rather, whether it adds real value to space travel). Fuel availability and travel speed are real limitations for human’s space ambitions, and AM does little to address either of the challenges. In addition, carrying raw supplies needed for the build-as-you-go program demands space and weight on the rocket. While build-as-you-go helps save some time, do benefits of time savings from such program outweigh the hassles of carrying extra materials/supplies that may potentially demand greater fuel consumption? I personally don’t think such idea is useful for space exploration, at least not in the near-term, and likely not commercially viable. However, I do think the build-as-you-go using AM is a promising idea, especially on international cargo ships. Shipping goods from one continent to another takes weeks. Imagine being able to build something on the ship using AM during that few weeks — it’ll help shorten inventory turnaround time and lessen the burden of inventory holding, delivering massive financial benefits to companies. Dell did this in the early 2000s by assembling their laptops on ships from Asia to the U.S., and I think AM allows companies to produce a larger variety of products during the shipping process at a much lower cost
Great post. In response to the questions you posed, I think Boeing needs to consider two additional factors when deciding whether to further invest in AM and where to invest (what parts/components). One is the value of maintaining a stable inventory and minimizing supply disruption. For example, if Boeing is able to use AM to manufacture parts that are currently experiencing shortage issues on a frequent basis, there is value in investing in AM, even if direct cost-benefit analysis does not support investment. Minimizing supply disruption and expediting plane production is a worthy goal that needs to be prioritized. Second, on the flip side, leveraging AM means that Boeing will in-source production of parts that it currently procures from other vendors. This vertical integration will subject Boeing to additional risks (such as fluctuation in raw material prices) and requires expansion of capabilities (such as additional purchasing and inventory management) — potentially making Boeing’s earnings more cyclical and making Boeing harder to value (do investors value it as an airplane producer or a 3D manufacturer? What valuation multiple should be used?). I think Boeing needs to carefully consider how this will translate to the investor community and impact the company’s valuation before committing to the act.
Great article. You likely have already considered this, but I think addictive manufacturing may have a great financial impact for LEGO given the nature of its products (plastic, easier to build, blueprint/design is highly scalable). Re: open innovation, I think Brett’s point is spot-on: the approach works if the idea contributors have a good sense of what the end customers want — i.e. do parents know what their children will like? The other thing Lego should consider, in my opinion, is how to efficiently pick winning ideas out of a large pool of ideas. It could be very costly for LEGO to manually review all submissions by contributors and evaluate the ideas individually. One way they could accomplish this is to invest in data science capabilities to capture key words from each individual idea, and rank ideas based on popularity (for example, 10% of users submitted ideas containing word Batman). This allows LEGO to prioritize building toys based on potential audience size, and help them obtain a higher level of ROI for their R&D efforts. Lastly, I actually think online is a great channel to combine with the Open Innovation project. Nike is allowing customers to customize their shoes when purchasing online — something they could not have achieved in-store with fixed inventories. LEGO could use the online channel to more effectively source ideas throughout the customer purchase journey and deliver more customized products for end consumers.
The central dilemma in applying open innovation to investment management is that large sets of investment ideas will almost inevitably produce market-level return. We discussed the law of large numbers in finance — when an investor diversifies his/her holdings by investing in a large number of assets, the resulting returns will be close to market (SP500) index performance. The merit of open innovation is that it allows a large set of fresh ideas to emerge and a few great ideas to blossom through a peer review/selection process. In the case of investment management, it is doubtful whether more ideas = better performance. Re: the question you posed on incentives, I think Quantopian could do something SeekingAlpha had some success with — SeekingAlpha systematically backtested the recommendations put forth by its members online and certified a small group of idea contributors as “power investors”. And the platform charges a fee for accessing power investors’ ideas. I think Quantopian could tap into a similar approach of idea certification and tiered-pricing in order to provide sufficient incentives for people to contribute quality ideas
A well written and well researched essay. I worked in B2B sales before and have firsthand experience with Einstein. While the product holds great promises, there are two important challenges to consider: (1) prediction effectiveness depends on input data, and currently Einstein is built as a Salesforce extension and does not integrate rich reservoirs of customer data sitting outside of Salesforce. Because of this, Einstein’s prediction accuracy is somewhat limited. (2) As you rightly pointed out, many startups are working to tackle sales AI. Important to note is that many large tech companies
(Microsoft, Google..etc) already have developed internal sales intelligence & churn mitigation tools. Google, for example, used its internal product usage and engagement data to flag key at-risk customers. Einstein, as a general-purpose sales intelligence tool, cannot compete effective against internal tools built using internal/proprietary data specifically for parent companies. I think adding to your list of near-term and long-term strategy is the need to expand their Business Development effort — working with client companies to build a large set of APIs to effectively integrate non-Salesforce customer data (for example, product engagement data) into Einstein to enhance prediction.
Application of machine learning in food services can indeed decrease costs and reduce wastes. However, one thing to consider is that while it is possible to forecast demands at the aggregate level, forecasting demands at the individual food truck /restaurant level may be difficult and inaccurate. Individually, humans are unpredictable, and it is hard to tell with great precision/accuracy what each individual might want to eat on a particular day and what toppings he/she might want to try. This reminds me of the law of coin flipping — individual food trucks can have wildly inaccurate demand forecasts (equivalent of getting 20% heads or 30% heads), but at the aggregate level all of food services will have pretty accurate forecast (precisely 50% heads). The challenge is that if individual food truck/restaurant can’t forecast accurately and see the benefits of such technology, adoption will be slow and the impact of this technology will be very limited. Other questions to consider is the dependency on delivery. Can this business model succeed without delivery? Would people want to visit a restaurant without staff and human service? And how does this limit the Total Addressable Market of this technology?