A well-known mathematical problem begins with a group of millionaires meeting over lunch (clearly a pre-COVID scenario). The group has a wonderful time, enjoying their food and drinks wholeheartedly. As they ask for the bill, one particularly cheeky member of the group proposes that the richest among them pay the bill. The group agrees, but immediately finds itself at an impasse over the bill. No one wants to share their exact wealth.
This scenario, called the Millionaires’ problem, was first proposed in 1982 by computer scientist and computational theorist Andrew Yao. It is a secure multi-party computation problem, a subfield of cryptography with applications in e-commerce and data mining. It is commercially applicable to any situation where there is a need to securely compare numbers that are confidential.
Many solutions to this mathematical problem have been put forth, the first presented by Yao himself. As the Glowlit team set out to reimagine market intelligence in the Animal Feed industry, we looked to the Millionaires’ problem for inspiration. What we saw was really an issue of trust. Each of the millionaires needed to know where they stood relative to the others, without giving away their own position.
Glowlit facilitates trust across the industry by allowing users to anonymously benchmark their position relative to the market. Like the Millionaires’ problem, its solution is mathematical. The Glowlit algorithm ensures that only verified price entries make it into the reports, and the accuracy of reports encourages more users to enter better data. As products grow in numbers of user entries, proportional increase in the number of verified entries is oberved. Slowly but surely, it is restoring trust across the supply chain.
In third week of September Feed Additive Focus, brought to you by Glowlit, takes a look at the latest price changes in L-Threonine 98.5% & Vitamin B7 (H) Biotin 2%
Spot buyers are paying 13% less for Vitamin B7 (H) Biotin 2% than they were just one week ago. A 35% decrease in user interest is observed compared to the previous week.