Using Data for Risk Management
Company Size
11-200
Region
- America
Country
- United States
Product
- Granular
Tech Stack
- Data Analytics
- Cloud Computing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Agriculture
Applicable Functions
- Business Operation
- Logistics & Transportation
Use Cases
- Predictive Replenishment
- Supply Chain Visibility
Services
- Data Science Services
About The Customer
Walnut Grove Farms is a sixth-generation farm located in Kentucky, United States. The farm is managed by Stephanie Halcomb, who joined the family business in 2012. Coming from a manufacturing background, Stephanie brought a process-oriented approach to the farm's operations. She is responsible for managing the operational backend, including planning at the beginning of the year, managing software, and capturing activities. The farm engages in a significant amount of forward contracting and has detailed plans for the storage of their crop. They work with a marketing and brokerage firm to handle their options contracts and use a combination of market trends, advice from the firm, and their own projections to manage their risk.
The Challenge
Walnut Grove Farms, a sixth-generation farm in Kentucky, was facing challenges in managing their grain marketing strategy. They were heavily reliant on forward contracting, which was only viable when the price was right. However, recent market conditions had seen prices fall below break-even for most farms. The farm also had to be strategic about their storage capacity, as they could not store everything they produced. They needed a way to better manage their risk and take advantage of market opportunities.
The Solution
The farm implemented Granular, a data management platform, to manage all of their contracts. The Revenue Forecast tool in Granular allowed them to track their futures prices and contracted quantities, providing a comprehensive view of their projected sales. This information served as a trigger for decision-making when the market price was above their average price and they still had enough to sell. Granular also provided real-time harvest data, which was critical during busy periods like harvest. This eliminated the need to download grain cart data or wait around, improving efficiency and productivity.
Operational Impact
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