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Tania
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Hey Andres, thank you for reading my blog! Below are my answers to your questions:
1. Most cows that are used for dairy production are between 1 to 4 years of age. With age the yields of the cows reduces drastically, however, some Indian farmers choose to keep their cows to help produce the next generation.
2. While this may be a controversial topic, most cows that are not useful to the farmer anymore are sold to middle men who take the cows across the border or to slaughter houses. India does produce 1.53 million tons of beef every year. It is a bit ironic but also, a natural way the industry has evolved over many years. http://www.theatlantic.com/business/archive/2015/02/selling-the-sacred-cow-indias-contentious-beef-industry/385359/
3. India exported 13 million tons of skim milk powder as of 2014, however, it is not a large part of domestic consumption. It take about 7 liters of milk to get 1 kg of milk powder, hence, this would be difficult to implement at the village lever. However, to make sure that more milk is captured at the beginning of the supply chain and wastage is minimized, newer technologies such as small thermal and solar chillers are being introduced. http://news.mit.edu/2015/promethean-power-india-milk-chillers-0908
Hey Andres, thank you for reading my blog! Below are my answers to your questions:
1. Most cows that are used for dairy production are between 1 to 4 years of age. With age the yields of the cows reduces drastically, however, some Indian farmers choose to keep their cows to help produce the next generation.
2. While this may be a controversial topic, most cows that are not useful to the farmer anymore are sold to middle men who take the cows across the border or to slaughter houses. India does produce 1.53 million tons of beef every year. It is a bit ironic but also, a natural way the industry has evolved over many years. http://www.theatlantic.com/business/archive/2015/02/selling-the-sacred-cow-indias-contentious-beef-industry/385359/
3. India exported 13 million tons of skim milk powder as of 2014, however, it is not a large part of domestic consumption. It take about 7 liters of milk to get 1 kg of milk powder, hence, this would be difficult to implement at the village lever. However, to make sure that more milk is captured at the beginning of the supply chain and wastage is minimized, newer technologies such as small thermal and solar chillers are being introduced. http://news.mit.edu/2015/promethean-power-india-milk-chillers-0908
Thank you Christine for writing this post. It is amazing to see how online platforms are dis-intermediating players in the banking industry and providing better pricing compared to traditional banks. However, one question that came up in my mind was, how does lending club conduct credit rating checks on their borrowers if they are completely online based? (as providing quality of the output of their process (loans) would be vital to ensure their business is sustainable). And on that note, do you see their fees rising in the near future, as regulation on online transaction and data security increases?
Hey Silvan, thank you for writing this insightful post. Reading about how Chef Robuchon redesigned the Michelin star experience by creating the meal in-front of customers was interesting. Storing pre-cut food ensure that the meal is standardized and take minimal preparation time once guests are seated. However, unlike what we learnt in the IDEO case, all innovation has been centralized by Chef Robuchon. Without taking divergent views from his cooks and junior chefs, it seems Robuchon is creating most of the new dishes by himself. Do you think by hosting cook-offs and competitions he would be able to add newer dishes?
Thank you Christy for this interesting post on Dinner Lab. It was great to understand how their lean (outsourced) operating model has helped them scale rapidly. Since no parameter is fixed – location, staff, cuisine and chef are always different, there is a lot of variability in their process and it becomes difficult to create a standard meal. Currently, this works fairly well for Dinner Lab as their format removes customer’s expectations out of the equation. Before their first experience, customer’s do not know what they should expect. Hence, as long as Dinner Lab does not compromise quality and remains customer-centric, they would be able to keep customers satisfied and loyal. However, what happens if a customer gets a unsatisfactory meal once? How would Dinner Lab use its’ data to get the customer to eat with them once again?