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Centaur IoT Tech Adopted by Cargill in Santarém, Brazil for Sustainable Soybean Supply Chain

Implementation Date

January 1, 2024

By

Cargill

City

Santarém

story image

Description

The leading commodity trading and manufacturing group specializing in oilseeds faced significant challenges in the post-harvest and storage phases of soybeans. These challenges included maintaining the quality of soybeans and preventing costly spoilage incidents during storage and transportation. In addition to other quality problems, the company also encountered issues with soybeans spoiling in flat warehouse storage as well as in barges and Panamax vessels, resulting in the formation of agglomerated, rocky masses that required expensive clean-up procedures. Furthermore, heat damaged soybeans produced inferior yields when processed into soybean oil.

Impact & Result

To address these challenges, Cargill turned to Centaur and its advanced IoT and predictive data technologies. Centaur offered solutions that focused on early detection, monitoring, and prediction to mitigate quality problems and minimize the associated costs, costs from spoilage and inefficiencies from missed shipments.

Solution Spotlight

Centaur builds a software and sensor-based platform to transform today's grain & cereal bulk supply chain into a trusted, end-to-end, post-harvest quality chain. With its monitoring solution, Centaur offers visibility, quality control, traceability and sustainability along all stages of the supply chain. This is achieved by automatically creating a digital simulation (a "digital twin") of the conditions both inside and outside the grain storage. To achieve this, the Internet-of-Crops platform pulls in all available digital information such as weather and climate data on outside conditions, plus internal conditions identified by sensors (e.g. gas profiles, temperature, moisture levels) and inputs all of this into a state-of-the-art digital model of how grain performs when subject to any and all of these conditions. The result is a state of the art prediction tool of likely problems, allowing remedial action in advance.

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