Cool unexpected application of 3-d printing technology! I think you are right that the key to commercializing this technology is reducing the cost down to where it would be more affordable than traditional home-building. Many of the places where people currently lack affordable housing have quite low labor costs to begin with, so it seems like reducing labor cost via 3-d printing is not a big relative advantage of this technology. I think the key is to reduce material costs and I’m curious as to whether the material cost savings from decreased waste are large enough to make this technology more cost-efficient overall for use in the developing world. How much of the total cost of a new build is attributable to waste?
This was a great read Melcolm! I’m curious about what operational changes Nike needs to make to scale 3-d printing to mass production and how these changes would affect its cost structure. Nike currently manufacturers a lot of its products using overseas, low cost labor. While 3-d printing has the potential to reduce manufacturing costs by reducing waste, increasing quality / uniformity and increasing speed / output rate, the technology is still pretty nascent and would require a significant capital outlay to implement on a large scale. Would the cost savings from reduced labor outweigh the increase in capital costs? How many years away from this scenario are we? Or could Nike command a high enough price for 3-d printed shoes to make the same profit margin on the higher cost base associated with 3d printing?
What an inspiring use of machine learning technology! This article reminded me that technological advances like machine learning can have broader applications beyond increasing revenue and reducing costs for corporations and can be a force for social good. Perhaps PAWS could expand its data set by partnering with governments around the world, particularly those who are struggling with the poaching proble. Many governments already collect topographical and environmental data (including wildlife migration patters), regardless of whether they have a problem with poaching, as part of their weather, environmental conservation, and national security efforts. Could feeding this data into the algorithm, including data from those nations not plagued by poaching, could provide more “learning material” for the algorithm.
Thanks for a great read! I think you brought up a great question about what other portions of its business OTTO can apply machine learning techniques. I really liked your suggestions regarding manufacturing, supply chain management and customer engagement but paused a little at your suggestion regarding product development. I’m skeptical about how machine learning can deliver a sustainable competitive advantage in product development given that its main strength is in predicting outcomes based on existing data sets and in my view, building great new products is only partially related to what was successful in the past and therefore predictable . I think a larger part of successful product development in fashion relates to having a new/better/more creative vision for the future than you had in the past, and the ability to deliver this vision, season after season, is what generates an enviable competitive advantage in fashion. I’m curious about how to leverage machine learning in more “creative” processes like new product development in fashion.
Thanks for a very interesting read on BuzzFeed’s application of open innovation methods to news media. I think crowdsourcing news has the potential to be really powerful and a real game changer for the news industry, as we saw from the use of Twitter during the the Arab Spring revolution. Buzzfeed could use its skill and scale at crowdsourcing to differentiate itself from traditional news outlets, who similarly also seek tips and real time news from readers on the ground, but have a user base that is much less engaged on social media than Buzz Feed’s customer base, and I would imagine, much less likely to contribute content to the site. However, as we’ve seen recently from the proliferation of fake news on Facebook during election cycles, crowdsourcing of news content can also be very dangerous. I think the key to using crowdsourcing news media effectively is to marry it with a high degree of curation and fact checking. I think this would be an instance where there are tremendous benefits to a more selective application of open innovation – opening “idea generation” but not “idea selection”. If Buzzfeed can find a way to sift through and qualify crowdsourced news quickly and at scale (whether through machine learning techniques or human effort or some combination thereof), it could have a real edge over traditional news outlets in producing a high volume of high quality news, which, in the 24-hour newscycle world, seems like a key competitive advantage.
Thanks for a great read Aparna! I think this company is a great example of the extreme end of open innovation, where the company’s entire innovation process (from idea generation to selection) is opened to input from external forces. My big concern with this business model and approach to innovation is how can Betabrand create and capture value in this ecosystem, and what should it get paid for (and by whom). Since it has outsourced idea generation to designers and idea selection to customers, it almost seems more like a platform business to connect designers and customers than a fashion company itself. If that’s the case, perhaps the company’s true value-add is its ability to manufacture designs and I wonder if this should be an area where the company should invest to decrease production time, increase quality, and/or reduce cost, and thereby create and capture value. I think another potential area where the company is adding value is its ability to introduce buyers and sellers interested in a specific type of product (ie. connect makers of rain boots with rain boot enthusiasts). Perhaps it could focus on acquiring and matching designers and customers as another way to create and capture value.