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Predictive Analytics on Grain Conditions
Predictive Analytics on Grain Conditions

See your product's temperature, moisture and quality metrics evolution in the future, up to 6 months in advance.

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Written by Centaur Tech
Updated over a week ago

To see the predictions of your safe storage time, grain quality metrics, and/or run grain conditioning scenarios with or without aeration, go to "Predictions" tab.

Centaur's predictions mechanism allows you to run ahead in order to see what will be the condition of your grain based on:

  • The condition of the product at the time of loading,

  • The weather in the area

  • Any grain conditioning you did (aeration, cooling or drying), and,

  • The weather in your area for the upcoming months, based on historical weather data.

Moreover, you get information on a number of grain quality metrics, such as:

  • Dry matter loss,

  • Estimated time when will have visible mould,

  • Estimated time when you will start having significant dry matter loss.

You can get the prediction as is or you can choose to run the simulation including specific periods of aeration. This capability helps you see if you can effectively prolong the safe storage time of your grain or improve its condition to avoid spoilage and/or deterioration in its quality.

To add aeration in your scenario, click on "Enable Aeration":

In the pop-up window, set the starting date after which will periodically do aeration sessions, the interval (i.e. every how many days) and the duration of each aeration session:

Press "Calculate" to run the predictive analytics, including the effect of grain conditioning (aeration and/or cooling). Press "Save" to save the new scenario:

Press "Run analysis" to fire the new simulation and start producing predictions on grain quality, including aeration:

In this scenario, periodic cooling solves the germination capacity loss warning and shows that safe storage time can be safely prolonged:

Why are Centar's Predictive Analytics so Important?

The predictive analytics help user take the correct decisions on his/her logistics. For example, if the system reveals that a grain conditioning scenario will improve the condition of the product and prolong its safe storage time, the user may decide to keep it and/or negotiate its price more. In an opposite scenario, the user may choose to consume or sell the product sooner.

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