Cargo
Predictive Modeling

Reduce transportation risk for agricultural commodities and anticipate in-transit cargo condition with Cargo Predictive Modeling.

When transporting agricultural commodities, the risk of spoilage can result in rejected shipments and financial loss. SGS and Centaur, a provider of turnkey IoT SaaS solutions for the agricultural supply chain, have joined forces to create Cargo Predictive Modeling – dramatically reducing the risk for agricultural commodities in transit.

Cargo Predictive Modeling

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Loading barge with a crop of wheat grain

Transport Agricultural Commodities More Efficiently

Cargo Predictive Modeling enables you to:

Reduce transportation risks
Optimize value chain performance
Improve the efficiency and sustainability of operations
Gain competitive advantage

Top view from drone of a large ship loading grain for export

The Power of Prediction

Cargo Predictive Modeling fuses testing with automated data collection and Artificial Intelligence (AI) to predict:

The impact of different cargo moisture levels and simulate anticipated storage conditions and atmospheric influences
Cargo condition risks that may occur on the route of an ocean bulk carrier, river barge, or container ship
The stability of the cargo, identifying areas that may be susceptible to temperature change, moisture migration and mold formation in each hold of a vessel or barge during transportation

Cargo vessel heading to Vancouver harbour

The Key to Cargo Consistency

Cargo Predictive Modeling collects and processes anticipated climatic parameters along the planned route, including ocean temperature, to provide you with accurate, climate-correct predictions. Within 24 hours of your shipment being loaded, a report evaluates and supplies actionable data on the potential risks of cargo spoilage.

The solution continues to learn over time, producing actionable data that will enable an improved resiliency, further reducing risk in the long term.

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