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TensorIoT and Amazon Web Services (AWS) built a solution for JBS Beef that improved the accuracy of their forecasting, allowing them to enhance their production strategy.
How can you use machine learning to monitor and predict the prices of beef to optimize JBS’s pricing?
TensorIoT and Amazon Web Services (AWS) to build a solution so that JBS Beef can now use machine learning to enhance the accuracy of predicting the prices of beef and meat production.
JBS is a leading global provider of diversified, high-quality food products, including a portfolio of well-recognized brands and innovative, value-added premium products. JBS S.A. engages in the processing of beef, pork, lamb, chicken, and also produces value added and convenience food products. It operates through the following business segments: Beef, Poultry, Lamb and Pork. JBS S.A. is the largest (by sales) meat processing company in the world.
JBS Beef prides itself on adapting to the latest science and technology trends to improve their products. In order to optimize their pricing, JBS wanted to improve their forecast to enhance decision making around production strategy. Using machine learning, our solution would provide insights and forecasting to help JBS prevent over and/or under pricing.
TensorIoT and Amazon Web Service (AWS) engaged with JBS to implement a machine-learning-based forecasting model to predict the prices of beef, which helps JBS better set the prices of their product and meat production. Services such as AWS SageMaker allow JBS’s team to build, train and deploy machine learned forecast that can ingest previous time series data along with metadata and external data to improve business processes by enabling actionable insights around forecasting for sales, feed prices, and prices for cattle and beef product.