Mohamed Elshorbagy's Profile
Great article! Similar to what we have seen in multiple cases from marketing, I think this is a very interesting example of a company that could be doing greater good in a profitable way. Clearly, this creative business model not only benefits Plastic Bank, but also the public and the environment. What I think may be interesting to look into is how can Plastic Bank leverage this business model and its benefits to the environment in a way to create other sources of income. We have seen from the International Carbon Finance case how companies make profits by reducing their carbon emission footprint allowing them to create carbon credits and to sell them to other companies that have larger carbon footprints. Given that recycling reduces carbon emissions by eliminating waste disposal in incinerators, I wonder if in the future Plastic Bank could be scaled to a point of making big enough reductions in carbon emissions and using those reductions to create carbon credits that could be solid for profit.
Very interesting article! I actually believe that the use of machine learning in such dating apps could be very useful. Given the massive amounts of scams and fake users on dating apps, I strongly believe that machine learning’s biggest advantage in this context would be to filter out scams and bots/fake users. That in itself, is a great competitive advantage and would, in my opinion, improve dating apps drastically and increase customer adoption. However,I am not a proponent to using machine learning for increasing the efficiency of matches and trying to help people “find the love of their life”. My biggest concern is data privacy and how would that data be used. We have seen how sensitive the issue of data privacy could be and how ugly it could get given Facebook’s recent data privacy scandal. My second issue is that while I believe that machine learning is a great tool in using data and iterating to come up with new models and algorithms to refine the results, a machine, in my opinion, can never be able to process feelings and emotions. Dating apps are mostly based on these “softer aspects” that in many cases can not be rationalized and iterated to find better results. People don’t usually know what they are looking for in a partner or what attracts them in a person, and in my opinion no matter how a machine learning model could be, it wouldn’t be able to capture those “random”, non-rationale, varying factors.
Very interesting article! I personally believe that leveraging 3D printing to create customized shoes for customers without having to incur additional costs or operational inefficiencies would create a competitive advantage for Adidas. While I believe that your questions about how should Adidas strategically pursue those goals and how should it convey this competitive advantage to customers are very strong points, I have a different concern that I am curious to know if its valid. I would like to know whether the materials involved in creating these shoes using 3D printing are different than those used in traditional shoe manufacturing. If so, what are the implications on the environment? Do we have to consider different practices to recycle them or dispose them? And would they have a worse footprint on the environment compared to traditional materials? Also, if true what would be the impact of regulatory and environmental policies on the business considering this transition towards 3D printing?
Very interesting article! I am personally a strong believer in Artificial Intelligence and genuinely believe that Machine Learning has the potential to outperform humans in certain fields. I totally agree to your point that Machine Learning wouldn’t essentially replace doctors but instead help do diagnosis and therefore increase the utilization of doctors for other more sophisticated tasks. While I believe that Machine Learning can deliver more accurate results in some fields (which can linked to them scoring higher than doctors in the MRCGP), my biggest concern of utilizing machine learning in a critical field such as healthcare is accountability. Even if the machine is projected to be more accurate than doctors, what happens when a machine gets it wrong? Who is to be held accountable? I believe that as humans we always feel secure when we know who is being held accountable for something and even if we know that the machine will be more accurate, the fact the we can not hold a machine accountable, in my opinion, one of the biggest challenges of AI in healthcare.
Very interesting article! What I find very insightful is how they capitalized on existing successful business models such as Uber, Airbnb, etc to create a platform that makes use of network effects and aims at optimizing current resources by increasing the utilization of existing 3D printers. With regards to your question of “How will 3D Hubs be able to scale and have a more global presence?” I have a follow up question that I believe may be relevant to the scalability issue addressed in the article. Given that the 3D printing technology is advancing very quickly and in some sense feels like it’s almost being commoditized in the near future and the costs are being reduced drastically with time. How do you see customers still adopting such platforms given the very low cost of actually owning a 3D printer in the future?