I really appreciated the insights you shared in your article – especially the nuances in the benefits and the real-world business concerns that you identified.
As I read your article, I immediately thought of the open-innovation model for space exploration that NASA has created. Any information collected by its equipment, such as a Hubble Telescope, is made public within 24 hours. This has resulted in amazing discoveries. Rather than just NASA employees looking at the large volume at data created, hundreds of millions of people around the world look into the data and find insights that lead to unobvious discoveries.
However, given that oil and gas is a very competitive space for private industry, I think open innovation would be hard to sustain unless the number of opportunities vastly outstripped the capabilities of existing companies to make use of them. If a company was convinced that someone else using their data would in no way affect their business prospects, then perhaps the right incentives could be created.
I would never have associated a healthcare-focused government agency to be involved in anything as hyper-innovative as a hackathon, primarily due to the high levels of bureaucracy (often needed) in this space! Your insights into these efforts was very informative and useful for me.
I think the government absolutely has to take the lead in holding open innovation challenges aimed at addressing social issues. I imagine such an effort running in a similar model to DARPA – which has inspired, enabled and developed so many key technological innovations in aerospace, mobility, and many other high-tech fields. Also, I think that these efforts should be formalized as a requirement by HHS’ governing bodies and used as a model for other regulatory agencies.
I think GE, with the high levels of diversification across many industry verticals, is setting itself up for success by investing heavily in additive manufacturing! Your identification of GE focusing on innovating in additive manufacturing as a separate business rather than within a particular vertical is a key point. This will enable the new unit to learn from a wide range of solution spaces and create robust / versatile manufacturing methods.
At the same time, I think there is a risk that the wide focus can lead to slow progress. If GE balances this well, I think they can become an innovation leader here.
I think your identification of the JetX partnership as key to Rolls Royce’s efforts to build momentum here hits the nail on its head.
3D printing is a fast evolving technology, with the flaws of each iteration immediately obvious to its users. Especially in aerospace, engineers can quickly become risk-averse due to the high amounts of safety required in their products.
By involving fresh eyes as much as possible, I think the pace of innovation can be accelerated and new solution approaches can be found.
Advertising is an immensely profitable business and I appreciate the insights you wrote about in your article about new ways to approach ads. While I agree that advertisements will continue to become more personalized and targeted, I do believe that the market for broad advertising campaigns will continue to exist.
I see two reasons for such campaigns: products/services that haven’t figure out what their target market exactly is, and to reach consumers that aggressively protect their privacy with tools that prevent developing ad profiles for them. For example, I think products such as a breakfast cereal would fit into the first category. Until the company figures out exactly who it appeals to, they would want to use a broad marketing strategy. The second category’s draw towards broad advertising campaign would depend on the size of population.
You have identified a very interesting problem. I appreciate that you decoupled the need for machine learning here from the current business goals (operations) of Flexport. Combining the two could have proven a very difficult, and potentially fatal, pivot for Flexport.
While I agree that Flexport should bring in outside vendors to help create the machine learning solution here, I think Flexport should play a more active role in shaping the product to address the bottleneck to its use that you identified in your article. By employing machine learning, the product could use various variables (who is inputting the data, seasonality, size of shipment, destination, etc.) to suggest values for the fields that need to be entered by the shipper. That way, the effort needed is lower, and Flexport can have better data to create the supply chain optimizations it needs to.