Thanks for identifying a case of extreme supply-demand imbalance and using it well to illustrate the effect of trade war at the local level in the U.S. Without any background in trade, I wonder if one solution would be to engineer a supply chain such that the raw chicken feet exported from the US can be semi-processed in another Asian country (that’s conveniently located on the transportation route from the US to China) and re-export these processed chicken feet to China to get around the current trade war seemingly specific to US – China. Or Georgia can invite a Chinese company to set up chicken feet processing facility in the US and become that company’s main supplier of raw materials (my underlying assumption here is that the Chinese government will more likely to compromise on chicken trade laws if a Chinese company can greatly benefit from this arrangement).
I wonder if there is any waste/inefficiency in the global coffee supply chain that Starbucks can also tackle to address sustainability issue. For example, for fresh produce supply chain, much produce (up to 50% for certain varieties) is wasted during transport because of climate controlling issues or produce not picked at the right time. I think there’s probably less of a degradation issue with coffee but it could be interesting to look into if there’s any efficiency gain in processing coffee closer to the source. There’s also potential savings in reducing the amount of coffee beans used in making coffee by optimizing the brewing process and developing better brewing technology.
I think WB still has a lot to offer to Chinese consumers, such as access to Western movie stars and license to iconic blockbuster characters, including comic book superheroes. So long as American culture still has global appeal, movies by WB and other American studios will not lose their relevance in their Chinese market.
Investing in local production facility might not work as a long-term strategy because the Chinese government can easily change its laws to circumvent such setup if it wants to protect its own entertainment industry. Relying on soft power such as stars and iconic characters is a more sustainable play in China.
I am really interested in how much incremental improvement the smart camera/real-time fine-tuning of train schedule technology can deliver over adjusting train schedule using historical data. I thought that since train usage pattern is quite predictable (could be a very wrong assumption), historical data (be it camera/platform density data, or ticket usage data) can probably get us 80% there in terms of coming up with a more efficient train schedule. I also wonder how this technology can ensure safety given that it has to adjust train speed frequently for multiple trains in the system. It would be great to understand more the cost/safety vs. train performance improvement tradeoff that Seoul considered before embarking on using this technology.
As a loyal Rent the Runway customer, I’ve always admired the level of complexity that this business deals with to get items (not just the item you order but also a back up dress of the same SKU but in a different size) out on time. There is so much variability in their supply chain such as 1. if the previous renter returns on time, 2. UPS to deliver the returned package back on time (which depends on weather patterns in various parts of the country), 3. level of cleaning/fixing required. These insights into their operation (which sounds like a highly automated, large-scale laundromat) are very helpful.
One question I have is whether they use their recommendation engine to also help smooth or create demand for items that they know are more likely to arrive in time for a particular rental period + zip code combination. For example, they can push items that are less in demand or less likely to need repair higher up in the search result.
I wonder if the fact that chili can be preserved, and in this case, is required to age for three years before production, can be helpful for supply chain management. I imagine that the company can preserve surplus stock in a good year to build buffer inventory against bad weather. On the other hand, given such long production lead time, I expect it also poses challenges in terms of responding to market demand as there could be product shortage if the company under-forecasts demand (similar to the situation of aged liquor such as bourbon).
Another way to counter effects of climate change is to collaborate with a company like Indigo to produce more resilient or more consistent seeds.
Lastly, I wonder if the company can capitalize on “global weirding” by introducing seasonal or limited batches of products that celebrate variability in raw ingredients as opposed to trying to standardize all ingredients to produce a single line of products.
This technology seems to have a lot of potential in solving for the speed problem in the fashion industry, which would help reduce inventory cost, reduce the need to mark down inventory, and should in theory translate to cost savings. However, given where the cost of this technology is now, I think its application is quite limited to a specific price range. At a cheaper price range (for example, where fast fashion companies like Zara and H&M operate), if you want to break even on the investment into the machine alone in one year, assuming 12 hours of machine utilization per day, the machine cost per clothing piece will be around $18. This is before any raw materials or electricity cost. Given that a piece of clothing at Zara or H&M sells for $20-40 on average, the company will likely make no margins or even loss using this technology at its current cost structure. At the high end price of the spectrum, I think customers would expect hand-made clothing pieces as opposed to ones made by a 3D printer, so the cost would make sense and potentially offer very healthy margin to the company, but it requires a lot of customer education to change their expectation. I’m very interested to see how fast the cost of this technology will decline over time and at which point it will become prevalent among lower-end fashion companies.