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Great article. It touches upon a sensitive and controversial area in public health — as a society, how can we erase the significant R&D and costs involved with creating live-saving medicines so that they can be more readily accessible to people who need it most. Gates Foundation’s disruptive approach is commendable. Interestingly, my best friend is the program manager for the Ebola crisis management for a major non-profit and I would imagine that she would also agree with you.
I am sure if you had more time and space, you could have talked about TRIPS and other global drug policies that influence limits in the market.
My biggest concern, to which previous commentators have touch upon, is this approach may not be appropriate for the end user. The individuals who require these medicine are the most vulnerable. They too deserve the highest level of care. Given that this is a matter of life and death, I want absolute certainty that the medicines developed underwent the strictest protocols and industry standards.
I enjoyed reading your post about how Safilo is trying to integrate 3-D printing. Based on your research, it appears waste is a significant cost burden for the eyewear industry. And 3-D printing could be a competitive advantage in that respect. The reduction in costs attributed to acetate disposal would be a massive cost savings for the business. I would agree, however, that I think it could dilute the brand. When I think of luxury glass brands, I imagine durability and high quality. From my understanding, a challenge with additive printing is the type of material and its durability. Manufacturers are limited in terms of building material type. Another issue is although additive manufacturing would enhance scale, a competitive advantage for luxury brands is exclusivity and limited edition. Additive manufacturing could compromise this strategy.
It was fascinating to learn how Nike is leveraging 3-D printing to create customized cleats for professional footballers. I would imagine it allows for rapid prototyping for new designs and client needs. And if Nike is able to cut down the R&D and supply chain lead time, this could make mass production viable. You bring up several legitimate concerns to which I would add a couple more. From my understanding of 3-D printing, a significant concern among industry experts is durability of the building material compared to that of traditional shoe manufacturing. I am also curious to know how this technology impacts the company’s pricing strategy. Should the company achieve scale, would Nike lower the pricing of cleats? Or would it be cause for an additional premium? I am not sure how Nike would go about it, but I think it is innovative to incorporate 3-D printing in the product development process.
KW, I would love to know what the initial estimates are for additional revenue and cost-savings for Capital One by implementing machine learning to predict new customers. I would imagine it’s current B2C business and customer acquisition costs are quite expensive. Just as an example, a friend of mine from a competitor credit card company who is the Director of B2C quoted me $1 per mail to new customers. If machine learning is able to identify the right factors that predict a good client, the business opportunity is massive. To your point around data ethicists and management of biases, use of the data in this matter could be deemed exploitative and potentially discriminatory — a significant risk that the firm cannot ignore. In your research, did you find any indication of how Capital One plans to manage those risks?
Good job!
Good article on the benefits of machine learning in the healthcare sector, in terms of cost savings. Healthcare costs have escalated in the United States to extent that is unreasonable. I agree in principle that automating a step in the communication chain between providers and insurers can reduce the burden of administrative tasks, and consequently total costs. However, a major issue with machine learning in healthcare is its practicality and adaptability given the volume and unique nature of patient presentations. There some much room to mistakes with patient filing, which is why good case managers are in such high demand. I can only imagine the how costly and time-consuming it would be to course correct a mistake as a result of machine learning system such as Evicore. As Matt B pointed out above, a provider would need to see data around the efficiency and error rate between a automated –> automated chain before making the decision to implement a system like Evicore.