Executive Summary
One of the main drivers that got us working in this problem was a hypothesis we build from empirical data, which suggests that up to 80% of the farm-to-table cost happens within the feedlot. That is a lot. It means that no matter how large an improvement in latter processes (e.g. deboning, warehousing, logistics, commercialization, etc); the overall impact on the supply chain will be significantly less than compared with minor improvements within the feedlot. Although this percentage is hard to estimate, mainly because prices vary so much depending on so many factors (e.g. cattle and feed); if we rationalize where in the meat production process is there a higher amount of value created, most would agree it is when you actually purchase the cattle, grow it and slaughter it to meet your standards.
Hence and beyond the fact that the actual cost percentage should be revised and perhaps studied, it is for certain that any effort to make more efficient the feedlot could have a higher impact in the whole meat production industry supply chain. Still, the implementation of advanced analytics for the cattle feedlot industry turns out to be very limited. Even so more limited than other livestock operations e.g. dairy farms.
Livestock Analytics fills the analytical gap in the cattle feeding industry –by providing an integrated system that help you make better decisions in your feedlot. We do this providing a turnkey solution in which we help you acquire individual data, store it in custom databases, analyze it and provides timely knowledge to improve decision making.
Analytical approach
Being a turnkey solution based in an integral platform, the first step of this project was related to data acquisition. Given intrinsic conditions of the cattle industry, this provided a nice challenge. Usually, a customer will have operations scattered in different locations e.g. the feedlot being in one place and the slaughter house in a different one. It is also not unusual to have offices in a different location than the previous. So data acquisition must account for this characteristic. Then, operators at the feedlot won’t usually be highly akin to the usage of technology –so a friendly, error-proof system works much better. Finally, operating in a ranch provides excruciating conditions for hardware so options must be chosen wisely.
Second, we needed to design custom databases that would consider further work in both transactional and analytical environments. They also had to account for accessibility in different locations and thus cloud computing.
Next, analytics must be incorporated into the software –as data by itself provides limited value to the operation. To implement analytics, the user must be taken step by step from simple metrics and business intelligence tools; to more complex analyses. This is also consistent with the build up of data in the feedlot which takes time (e.g. one single cattle will take 4 to 6 months to finish its process and show its final result).
Finally, information must be made available to the user in a easy and timely manner. If knowledge cannot be generated for the user that requires it, when it is required, in a message that is well understood; then it provides poor value.