Using Machine Data For Profitability
Company Size
1,000+
Region
- America
Country
- United States
Product
- John Deere Equipment
- Granular Mobile App
Tech Stack
- Cloud Computing
- Data Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- Agriculture
Applicable Functions
- Discrete Manufacturing
- Logistics & Transportation
Use Cases
- Asset Health Management (AHM)
- Farm Monitoring & Precision Farming
- Supply Chain Visibility
Services
- Data Science Services
About The Customer
Ben Singleton is the CFO of Anson Family Farms, a large farming operation that farms around 20,000 acres in 8 counties in Indiana and Illinois. The farm primarily grows corn and soybeans, using a 50-50 rotation, and practices no-till farming with cover crops on most of their acres. Ben grew up on a small cattle operation in Indiana and has an accounting and finance degree. He initially worked as a public accountant before joining his wife's family's farming operation. In addition to his role as CFO, Ben is also involved in the operational side of the farm, managing their soybean planting and one of the harvest crews.
The Challenge
Anson Family Farms, a large farming operation spread across 20,000 acres in 8 counties in Indiana and Illinois, was facing challenges in managing and utilizing the vast amount of data collected by their John Deere equipment. The data, which included information on planting, spraying, harvesting, etc., was primarily used for crop insurance reporting. However, the farm was unable to use this data to optimize operations due to technological limitations. The data was initially stored on a desktop machine, then moved to a server, but both methods were slow and cumbersome. The farm also struggled with real-time data access, which made it difficult to identify and correct errors in a timely manner.
The Solution
The farm started using Granular's mobile app to manage their machine data. The app allowed workers to receive work orders and enter data on the go. The data from the app was then cross-checked with the machine data to identify and correct errors. The farm also moved their data to the cloud, which improved access and speed. Looking forward, the farm plans to use the data for more than just crop insurance reporting. They want to use the data to compare the profitability of different types of ground and to determine whether they would be better off running more equipment or farming less but more profitable land. They also plan to use the data for activity-based costing and to filter the data in meaningful ways.
Operational Impact
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