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Centaur's Internet-of-Crops® Adopted by Athenian Brewery Heineken in Greece to Revolutionize Barley Management.

Implementation Date

June 1, 2024

By

Athenian Brewery Heineken

City

Stefanovikeio

story image

Description

The production of fine-quality beer is facing numerous challenges, including climate change, supply shocks, and stringent quality standards. One critical aspect is the post-harvest management of barley, a key ingredient in brewing. To address these challenges, a leading malting and brewing company in Southeast Europe partnered with Centaur to deploy their Internet-of-Crops® solution. The client, Athenian Brewery Heineken, is dedicated to sustainable production of high-quality beer, to meet a growing demand. They recognized the need to optimize the post-harvest management of barley to maintain its quality and prevent food waste.

Impact & Result

The production, storage, and post-harvest management of barley presents several challenges for the malting and beer industry. To tackle these challenges, the client implemented Centaur's Internet-of-Crops® solution, leveraging the following technologies: 1. Smart Grain Monitoring Sensors, installed in silos of malting barley. 2. Digital Twin Quality Analytics, driving the decisions of grain managers. 3. Pest Treatment Monitoring, precise tracking of pest treatments to eliminate infestations.

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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