Source 1, properly cited:
1. Holweg, M. (2015, June 23). The Limits of 3D Printing, Harvard Business Review. Retrieved from https://hbr.org/2015/06/the-limits-of-3d-printing
This was a great introduction to how 3D printing companies can survive, by capitalizing on their main competitive advantage of speed. I believe Proto Labs is definitely targeting the right market currently, going after customers that truly value the customizable nature of rapid prototyping.
However, I do share many of the concerns that you laid out in your questions:
1. High R&D costs: While 3D printers do rely on software to switch quickly between manufacturing different products, there is still a lead up cost regarding agreeing on the correct schematics for the product, getting the right quality/ size printer, and sourcing the necessary materials. Because the contracts are likely to be smaller, the overall cash flows of the company will experience higher volatility than a traditional manufacturer, and I worry how Proto Labs will finance a large portion of its capital expenditures, especially since there are insignificant labor savings1. Their business model necessitates that they attract a large, customer base with expensive, highly customized demand, and it is unclear if that market can match up to their investment schedule, especially since the 3D printing space is undergoing large technological transformations, and it’s easy to be left behind by new innovations. Hence, managing cash flows will become a first order concern for a company that sees its strength more in technology.
2. Production flexibility: If the end goal is the fastest turnaround time possible, then Proto Labs really needs to be a one-stop shop for their customer with regards to creating the final product. However, one clear limitation of 3D printing has been the types of materials that printers can actually utilize. Presently, plastic is still the input of choice for the vast majority of printers, but when it comes to highly customized products, there may be a needs for a greater variety of components, including glass, cloth, and other materials that are presently still incompatible with 3D printing. As the industry advances, these barriers will like reduce or disappear, but in the medium term, Proto Labs needs to find a delicate balance with satisfying customers, but working with a still-limited technology set.
3D printing is, presently, an alternative manufacturing process that allows greater creativity and individualization when creating small batches of product. I like the way that you incorporated the acknowledgment that we are still fairly far off from 3D printing entire vehicles, but I do agree that at the very least, 3D printing can help aggregate suppliers, and cut down on the high volume of specialized OEMs that a typical auto manufacturer has to source from.
I also agree with the author that one competitive advantage for more upscale carmakers with regards to 3D printing is the level of customization that the manufacturer can provide potential customers. Since luxury goods are valued more for their reputation and design (as opposed to pure functionality), their customers would also be willing to tolerate a longer lead/manufacturing time if it means that spending a premium will get them a vehicle specific to their preferences. For these personalized orders, I think 3D printing will be able to balance much of the tradeoffs regarding speed and cost, since the user has already demonstrated a willingness to pay for a differentiated product (and willing to subsidize the increased variable cost of 3D printing), and would be more patient since they understand they are deterministically responsible for the delay, and thus giving the 3D process more lead time.
One concern I do have with this analysis is if VW is the right standard bearer for automotive 3D printing. We’ve discussed how upscale customer customization is one potential method of unlocking the inherent competitive advantage of 3D printing, and while VW may be perceived as a more upscale brand, I am uncertain how its primary reputation as a semi-luxurious, safe, family-friendly, middle-of-the-road vehicle1 would play towards actually attracting that demographic. In this case, there does not appear to be an organic demand from the existing customer base for this feature. Investing in technology is critical for industries with tremendously long development cycles like auto manufacturing, but it also feels like they are trying to be in first place, but with no specific use case in mind. It’s possible that they can discover a competitive edge through further research in the space, but there is also a risk that they are developing a technology that is better suited for a competitor.
1. Bartlett, J. S. (2018, February 22). Which Car Brands Make the Best Vehicles? Consumer Reports. Retrieved from https://www.consumerreports.org/cars-driving/which-car-brands-make-the-best-vehicles-2018/
Machines cannot make decisions in a vacuum, and I think one major limitation of applying machine learning results at Netflix thus far has been the human element. The output of ML algorithms can be hard for a human to understand, especially since their very usage is predicated on analyzing volumes of data that would be impossible for a person to comprehend. This hits at a major issue of ML, which is that at times, even the scientists who built it are unable to explain the output.1 However, that doesn’t mean that the data is wrong, or unusable.
In an artistic environment such as Netflix, it can be daunting to be told that a passion project is unfeasible because a formula determined it unworthy, but not be given a refined explanation as to why that is. Rather, it should be a human’s job to take the result from the algorithm, and contextualize it in a way that appeals to the person who is receiving the decision. For example, in the case of ‘Glow’, instead of reporting that the data deems the show to be unsuccessful, if the goal is to maintain a creative relationship with the team, then the news can be framed as ‘the current show is not reaching the right users…would you be interested in working on a show in [another data-approved genre] so that we can showcase your abilities to a larger audience?’. Ultimately, machine learning cannot stand on its own just yet, and its usage has to be carefully managed by someone who is aware of how to properly utilize its insights.
1. Knight, W. (2017, April 11). The Dark Secret at the Heart of AI, MIT Technology Review. Retrieved from https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
While solutions like crash-damage estimators can be a good way to smooth out the initial customer engagement process, I think there is also danger in having it provide a potentially unrealistic quote, and sabotaging the customer experience from the start. For an event like a car accident, which could have drastic financial consequences for the average, unprepared American1, the insurance companies need to take every precaution to ensure that the customer does not receive unrealistic financial guidance. While I agree that machine leaning can be a useful tool in helping users better understand the context of their accident (i.e. which parts are more critically damaged, and might take more time/money to repair), I would veer away from providing any numerical suggestions. In the worst case scenario, if the software underestimates the true cost, then the app will be pitted against the insurance company and/or the repair shop. While we could note that the app only provides estimates, it could still leave a bad taste for customers who view it as an official extension of the insurance process.
I find the enhanced navigation solution to be particularly intriguing, although this is a feature that needs to be extensively tested before a direct beneficial relationship can be established. For example, even though the optimized route may be objectively safer, would using it lead to a higher incidence rate if the driver is unfamiliar with these roads? Alternatively, if a user takes the optimized route and gets into an accident, then would the insurance company bear some of the blame? Presently, most machines cannot solve problems without proper human context, and in a scalable business like insurance, where machine learning could definitely offer valuable insights over large datasets, I still think there is a while to go before these solutions can be made customer facing.
1. Nearly 60% of Americans Can’t Afford Common Unexpected Expenses, Bankrate. (2017, January 12). Retrieved from https://www.bankrate.com/pdfs/pr/20170112-January-Money-Pulse.pdf
While the prospect of ‘open source’ solutions sounds extremely alluring, promising cheap labor and diversified ideas, there also needs to be some consideration into whether an organization actually needs to implement its own open source solution in order to realize its benefits. In Buzzfeed’s case, I would actually argue that it’s not necessary.
Buzzfeed’s use of open source revolves around its desire to source realtime news ideas. Essentially, it gathers live data through a social media approach, an area in which traditional social media institutions like Twitter and Facebook already excel at. In this case, Buzzfeed might be better served by partnering with the existing players, and getting a feed of their content, rather than creating their own pipeline. The use of open source is only meant to generate ideas, which can benefit from the scale of a large user base, but Buzzfeed’s competitive edge in this space has to come from the quality of its editorial, which I believe should be where they invest more of their resources. Buzzfeed has already tried to maintain an in-house team for developing and maintaining more technically advanced forms of journalist, such as the Open Lab1 and the podcasting initiative2, and both ventures have been closed. Hence, Buzzfeed might not be adept at handling these projects internally, and be better suited to capitalize on the open sourced solutions of more established organizations.
1. Mullen, B. (2017, March 30). BuzzFeed is closing its Open Lab later this year, Poynter. Retrieved from https://www.poynter.org/news/buzzfeed-closing-its-open-lab-later-year
2. Spangler, T. (2018, September 20). BuzzFeed Shuts Down In-House Podcast Team, Variety. Retrieved from https://variety.com/2018/digital/news/buzzfeed-shuts-down-podcast-layoffs-1202950157/
Open innovation is a great way to draw engagement from an otherwise very fickle userbase. As the author noted, video game fanatics are a notoriously fast-shifting group, with game allegiances, and the game’s popularity, seemingly changing overnight. One common complaint around the traditional video game model is how protective most gaming companies are of its IP, wanting to maintain direct and complete control over user engagement, monetization, and other aspects of the ecosystem. I think Riot Games is really staying ahead of the curve by not telling users how exactly to engage with the game (whether it be replays, game mods, training, etc.), but by letting them vet each other for the best ideas. After all, the users are ultimately the ones who will purchase the product. A major benefit of this approach is that Riot Games also assumes very low tech debt, by not having to explicitly support any large, cumbersome, user engagement-centric platforms that it rolls out. This allows them to focus primarily on the game, and not have to commit significant resources to developing and maintaining ancillary and unsustainable products.
One large limitation to this approach, however, would be the scale at which users can contribute their own additions to the game. Something relatively straightforward, like live streaming and game training, can be done on a single user basis. However, more complex additions, such as actual game modifications (which the author suggested) might be untenable for an open source solution, since the amount of work, detail, and coordination required to keep the updated version of the game balanced could be far beyond the abilities of individual users. Open innovation is great for generating large volumes of ideas and smaller implementations, but Riot Games cannot rely on it to boost its core product offering, which still has to be carefully maintained by dedicated teams.
Additionally, one concern for open source might the maintenance of crowdsourced solutions going forward. Open source projects are very easy to start, but the more difficult task is finding the right administrators to ensure the code is updated for usability and security for a sustained period. For example, a quick search through the first two repos of open sourced League of Legends projects on Github1,2 yielded repos that have not been updated for more than a year, which is problematic given the official game is updated on a monthly cycle3. Open innovation in this case can be great for creating novel approaches to the game, but unless Riot Games wants to dedicate significant resources, it might be costly to ensure that these ideas are not just implemented, but maintained for the foreseeable future.
1. Coutin, N. (2018, January 12). League of Legends developers [Computer software]. Retrieved from https://github.com/league-of-legends-devs
2. Coimbra, S. (2017, September 1). LoL Open Source Developers [Computer software]. Retrieved from https://github.com/LeagueDevelopers
3. Most recent patch news. (n.d.). Retrieved from https://na.leagueoflegends.com/en/news/game-updates/patch