Case Studies AI Demand Planning Helped Agriculture Supply Chain Company Forecast Commodity Availability, Reduce Carrying Costs, & Improve Supply Chain Efficiency

AI Demand Planning Helped Agriculture Supply Chain Company Forecast Commodity Availability, Reduce Carrying Costs, & Improve Supply Chain Efficiency

Supply Chain Optimization

An agriculture supply chain company seeking to reduce carrying costs and improve supply chain efficiency turned to AI demand planning for help. We implemented predictive analytics to forecast commodity availability and optimize spending on primary commodities. The AI-enabled forecasts, supported by LSTM Deep Neural Network models, helped optimize inventory management and logistics, resulting in improved storage, handling, and processing capacities.

Critical Issue

An agriculture supply chain company was facing excess costs on farmer commodities due to suboptimal buy decisions that favored the farmers too much. Their sales team was spending too much time prospecting farmers who were not ready to sell. Farmers aimed to sell at peak demand to maximize profit, while the client sought to buy at the lowest price before supply exceeded demand.

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Customer Profile

Agriculture Supply Chain Company

$10B in revenue, 100 facilities

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Key Problems

Excess costs on farmer commodities

Sales team is wasting time prospecting farmers who aren’t ready to sell

our solution

We utilized predictive analytics to forecast the peak and trough points for commodity volumes, enabling our client to optimize spending on their primary commodities such as Sorghum, white corn, yellow corn, soybeans, and wheat. Our forecasts covered the top 5 commodities by spending and were fully automated, ensuring that the latest supply and demand patterns were considered. MVP was deployed in one production environment, supported by a dashboard for users to monitor the accuracy of the forecasts. Our AI-enabled forecasts are Long Short-Term Memory (LSTM) Deep Neural Network models, and the entire implementation was completed within 9 weeks by a team of 2 (consisting of one senior data scientist and one entry-level data scientist). We planned to incorporate commodity prices on the futures market in forecasts in the subsequent phase. The technology stack used for this project included Azure, Python, and Databricks.

The results

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Better intel resulted in better sales planning and more profitable price negotiation. Additionally, more pricing insight improved risk management for price volatility and supply disruptions.

Process gears

The forecasts helped optimize inventory management by improving storage, handling, and processing capacities, while also enhancing the logistics of shipping commodities via rail, truck, and barge.

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Implementing a more predictable supply chain increased customer satisfaction and enhanced the company’s reputation for consistency and reliability.

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