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Tag: MIT

3D Printing as Disruptive Innovation

Last week, I attended a MIT/Stanford VLAB event on 3D printing technologies. While I had previously been aware of 3D printing (which works basically the way it sounds) as a way of helping companies and startups do quick prototypes or letting geeks of the “maker” persuasion make random knickknacks, it was at the event that I started to recognize the technology’s disruptive potential in manufacturing. While the conference itself was actually more about personal use for 3D printing, when I thought about the applications in the industrial/business world, it was literally like seeing the first part/introduction of a new chapter or case study from Clayton Christensen, author of The Innovator’s Dilemma (and inspiration for one of the more popular blog posts here :-)) play out right in front of me:

  • Like many other disruptive innovations when they began, 3D printing today is unable to serve the broader manufacturing “market”. Generally speaking, the time needed per unit output, the poor “print resolution”, the upfront capital costs, and some of the limitations in terms of materials are among the reasons that the technology as it stands today is uncompetitive with traditional mass manufacturing.
  • Even if 3D printing were competitive today, there are big internal and external stumbling blocks which would probably make it very difficult for existing large companies to embrace it. Today’s heavyweight manufacturers are organized and incentivized internally along the lines of traditional assembly line manufacturing. They also lack the partners, channels, and supply chain relationships (among others) externally that they would need to succeed.
  • While 3D printing today is very disadvantaged relative to traditional manufacturing technologies (most notably in speed and upfront cost), it is extremely good at certain things which make it a phenomenal technology for certain use cases:
    • Rapid design to production: Unlike traditional manufacturing techniques which take significant initial tooling and setup, once you have a 3D printer and an idea, all you need to do is print the darn thing! At the conference, one of the panelists gave a great example: a designer bought an Apple iPad on a Friday, decided he wanted to make his own iPad case, and despite not getting any help from Apple or prior knowledge of the specs, was able by Monday to be producing and selling the case he had designed that weekend. Idea to production in three days. Is it any wonder that so many of the new hardware startups are using 3D printing to do quick prototyping?
    • Short runs/lots of customization: Chances are most of the things you use in your life are not one of a kind (i.e. pencils, clothes, utensils, dishware, furniture, cars, etc). The reason for this is that mass production make it extremely cheap to produce many copies of the same thing. The flip side of this is that short production runs (where you’re not producing thousands or millions of the same thing) and production where each item has a fair amount of customization or uniqueness is really expensive. With 3D printing, however, because each item being produced is produced in the same way (by the printer), you can produce one item at close to the same per unit price as producing a million – this makes 3D printing a very interesting technology for markets where customization & short runs are extremely valuable.
    • Shapes/structures that injection molding and machining find difficult: There are many shapes where traditional machining (taking a big block of material and whittling it down to the desired shape) and injection molding (building a mold and then filling it with molten material to get the desired shape) are not ideal: things like producing precision products that go into airplanes and racecars or printing the scaffolds with which bioengineers hope to build artificial organs are uniquely addressable by 3D printing technologies.
    • Low labor: The printer takes care of all of it – thus letting companies cut costs in manufacturing and/or refocus their people to steps in the process which do require direct human intervention.
  • And, of course, with the new markets which are opening up for 3D printing, its certainly helpful that the size, cost, and performance of 3D printers has improved dramatically and is continuing to improve – to the point where the panelists were very serious when they articulated a vision of the future where 3D printers could be as widespread as typical inkjet/laser printers!

Ok, so why do we care? While its difficult to predict precisely what this technology could bring (it is disruptive after all!), I think there are a few tantalizing possibilities of how the manufacturing game might change to consider:

  • The ability to do rapid design to production means you could do fast fashion for everything – in the same way that companies like Zara can produce thousands of different products in a season (and quickly change them to meet new trends/styles), broader adoption of 3D printing could lead to the rise of new companies where design/operational flexibility and speed are king, as the companies best able to fit their products to the flavor-of-the-month gain more traction.
  • The ability to do customization means you can manufacture custom parts/products cost-effectively and without holding as much inventory; production only needs to begin after an order is on hand (no reason to hold extra “copies” of something that may go out of fashion/go bad in storage when you can print stuff on the fly) and the lack of retooling means companies can be a lot more flexible in terms of using customization to get more customers.
  • I’m not sure how all the second/third-order effects play out, but this could also put a damper on outsourced manufacturing to countries like China/India – who cares about cheaper manufacturing labor overseas when 3D printing makes it possible to manufacture locally without much labor and avoid import duties, shipping delays, and the need to hold on to parts/inventory?

I think there’s a ton of potential for the technology itself and its applications, and the possible consequences for how manufacturing will evolve are staggering. Yes, we are probably a long way off from seeing this, but I think we are on the verge of seeing a disruptive innovation take place, and if you’re anything like me, you’re excited to see it play out.

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Science of Social Networks

Another month has gone by which means another paper to cover!

image This month, instead of covering my usual stomping grounds of biology or chemistry, I decided to look into something a little bit more related to my work in venture capital: social networks!

The power behind the social network concept goes beyond just the number of users. Facebook’s 500 million users is pretty damn compelling, but what brings it home is that by focusing on relationships between people rather than the people themselves, social networks turn into a very interesting channel for information consumption and influence.

This month’s paper (from Damon Centola at MIT Sloan) covered influence – specifically, how different social network structures (or “topologies” if you want to be snooty and academic about it) might have different influences on the people in the networks. More specifically, it asked the question of what social network would you expect to be better able to influence behavior: one which is more “viral”, in the sense that connections aren’t clustered (i.e., I’m as likely to be friends with my friend’s friends as people my friends don’t know), or one which is more “clustered” (i.e., my friends are likely to be friends with one another).

It’s an interesting question, and I found this paper notable for two reasons. First, its the most rigorous social networking experiment I’ve ever seen. Granted, this isn’t saying very much. Most social network/graph studies are observational, but I was impressed by the methodology and the attempt to strip out as much bias and extraneous factors as possible:

  • The behavior being tested was whether or not they would sign up and re-visit a particular health forum. This forum had to be valuable enough to get people to use it (and actually contribute to it), but also unknown and inaccessible to the rest of the world (as to avoid additional social cues from the user’s “real world” social network).
  • The author (and I do mean one single author: pretty rare these days for a Science paper as far as I know) created different social graphs which were superficially identical (same number of users, same number of contacts per user) but had the different network structures he wanted to test(one structure had subgroups of tightly inter-connected users, the other structure had random connections scattered across the network). The figure below shows one example of the network structures: the black lines show connections between people. On the left-hand-side is the highly clustered social graph – the individual users are only connected to people “next to them”. The right-hand-side is the more “viral” social graph, where users can be connected to any user across the social network.
    image
  • The users made profiles (with user name, avatar, and stated health interests), but to preserve anonymity (and limit the impact of a person’s “real world” social network on a user), the user names were blinded and users were not allowed to directly communicate (except in an anonymized fashion through the health forum) or add/remove contacts
  • However, whenever a user’s contacts participated in the health forum, the user would be notified.

The result was a somewhat bizarre and artificial “network” – but its certainly a very creative (and probably as good as it can get) means of turning social networking studies into a rigorous study with real controlled experiments.

Second, the conclusion is interesting and has many implications for people who want to use social networks to influence people. Virality may be a remarkably fast way to get people to hear about something, but the paper concludes that virality does not necessarily translate into people acting. The author conducted 6 different trials with slightly different network topologies (number of users ranged from 98 to 144, number of contacts per user ranged from 6 to 8). The results are in the graph below which shows the fraction of the users who joined the forum over time. As you can see, the clustered networks (solid circles) had much higher and faster adoption than the “viral” networks (open triangles):

image

Why would this be? The author’s standing theory is that while “viral” networks might be faster at disseminating information (e.g., a funny video), clustered networks work better at driving behavior because you get more reinforcement from your friends. In a clustered network, if you have one friend join the forum, chances are the two of you will have a mutual friend who will also join. At a very basic level, this means you get the same cue to join the forum from two of your friends. In a un-clustered network, however, if you have one friend join the forum, the two of you are less likely to have a mutual friend, and so you are less likely to receive that second cue.

Does this matter? According to the study, someone who had two contacts join the forum was ~75% more likely to join than someone who only had one contact join. And, someone who had four contacts join was ~150% more likely to join than someone who only had one contact. While this effect rapidly diminshes with more contacts (having five or six contacts join made relatively little difference compared with four), its a powerful illustration of quality vs. speed in a social network – something which is also borne out by the fact that while only 15% of people who only had one contact join returned to the forum, 35-45% of users who had multiple contacts join did.

This was definitely a very impressive and well-designed study. While it would be fair to attack the study for its artificiality, I don’t really think there’s any other way to systematically strip out the  biases that are intrinsic to most observational (not a controlled experiment) studies of social networks.

Where I do think this was lacking (and maybe the researcher has already teed this up) is the black-and-white nature of the study. What I mean by this is while I find the argument that network clustering helps drive greater behavior plausible, I think there needs to be a more rigorous/mathematical conception – how “clustered” does a network need to be? If a network is overly clustered, then it loses the virality which helps to spread ideas more quickly and widely – is there an optimal balance somewhere in the middle? Also, the paper only dug, on a very superficial level, into how network size and the number of contacts per user might impact this. I think further experimental and mathematical modeling/computational studies would be nice to really flesh this out.

Paper: Centola, Damon. “The Spread of Behavior in an Online Social Network Experiment.” Science 329 (Sep 2010) – doi:10.1126/science.1185231

(Image credit – social network diagram) (Figures 1 and 2 from paper)

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