I’ve been increasingly impressed with the unique content produced by Amazon Studios. For them to be even relevant in the media and entertainment industry, competing with giants such as HBO, Netflix, and Hulu, is incredible. Certainly, this shows the value of crowd sourcing ideas. A key issue here though is how large the audiences might need to be for a particular idea to get off the ground. Obviously producing content that pleases the masses is incredibly difficult and some of the ideas generated might be extremely attractive but only to small segments of customers. The challenge is then estimating the appeal to broader audiences without sacrificing the quality of the content.
Additive manufacturing presents a huge opportunity to create prototypes much faster than before. As we learned in TOM, the most cost effective production process may be a highly specialized and rigid assembly line making large volumes of standard products. However, when producing only a few units of a product (such as a prototype), a job shop-type production process is more efficient given the large set-up costs involved in an assembly line. 3D printing can quickly produce a single product on a cost efficient basis but, importantly, it far less scale-able. Especially for the technology and biotech industries, 3D printing allows for highly specialized prototypes that would be difficult to produce with current machinery. This is an exciting technology and I’m excited to see what it can do!
It’s smart for H&M to leverage machine learning here and it’s actually not too far off from their current strategy. H&M’s success is based on emulating high fashion designs and producing them at a lower cost given that they pay much less for designers and marketing. Applying machine learning to receipts, returns, and loyalty card activity is a great way to understand the popularity of specific designs, colors, and sizes, which could inform future ordering. It could also give insight into specific products that are often defective or uncomfortable (e.g., specific SKUs with high returns). I’m curious to see how this pans out because obviously many firms within the fashion/clothing industry could leverage this technology, including larger retailers such as Target and Walmart.
The CityMapper flexible bus solution reminds me of the Harvard Shuttle. While public transport within Cambridge and specifically near the HBS campus is lackluster, the Harvard Shuttle system offers a convenient solution to students. Certainly, there are very common routes that students need to take (e.g., HBS to Harvard Square) and relying on the community government to provide a public transport solution may not be ideal. I think it’s reasonable to assume that the larger Harvard Shuttle buses travel along the most common routes while solutions such as the Harvard Evening Van allow students to specify their (more unique) routes when the larger buses are no longer running. Perhaps Evening Van routes taken most commonly then evolve into larger bus routes. In a similar vein, I think there would be tremendous value in community transportation agencies leveraging data from Uber/Lyft about top routes taken by users to develop new public transportation routes or modify existing ones to better serve customers (though there are incentives for them not to share this data).
Great job, Toby Johnson! Crowd sourcing ideas in the food industry is a fantastic idea. What’s even better is making it a contest. Contests have high levels of engagement and earned media. I am a bit surprised by some of the winning flavor ideas, such as “southern biscuits & gravy” since I wouldn’t typically associate those complex flavors on chips. What I would like to know is how much testing these ideas costs. I imagine Frito Lay has incredibly complex and specialized production lines and that it would not be feasible to adapt them for one-off new flavor testing purposes but perhaps there is a more adaptable “job shop”-type production testing functionality that’s better for this case.