AG-TECH
Transforming supply chains with digital technology.
Digital transformation in agriculture is not just important, it's critical to stay ahead of the competition.
CASE STUDY 01 / CROPERATION
Creating a Cutting-Edge Logistics and Operations Platform for the Cotton Industry.
Croperation and Upgrowth teamed up to create a cutting-edge logistics and operations platform specifically for the Australian cotton industry.
The platform utilises data to provide real-time information, which enables traders and operations to make accurate, informed decisions in real time.
With workflow automation and API integration, this platform has significantly increased the flexibility and efficiency for a large global merchant. The results have been impressive, with a 150% reduction in workload achieved.
With workflow automation and API integration, this platform has significantly increased the flexibility and efficiency for a large global merchant. The results have been impressive, with a 150% reduction in workload achieved.
Keen to learn more?
CASE STUDY 02 / SUPPLY CHAIN RESEARCH
Increasing Transparency and Efficiencies in the Cotton Supply Chain.
Upgrowth was tasked with researching, designing, and developing solutions for supply chain management.
Over 12 months, we evaluated the supply chain by conducting interviews with farmers and industry experts to better understand the problem space. Using this information, we mapped out the supply chain from start to finish and built a process map to identify problem areas at each phase.
Through our research, we discovered that the agribusiness generate an abundance of data, but stakeholders struggle to effectively utilise it.This resulted in a reduced ability to make sound decisions, an increase in risk and overhead costs, and a decrease in transparency.
Through our research, we discovered that the agribusiness generate an abundance of data, but stakeholders struggle to effectively utilise it.This resulted in a reduced ability to make sound decisions, an increase in risk and overhead costs, and a decrease in transparency.
The solution
Keen to learn more?
CASE STUDY 03 / SUPPLY CHAIN RESEARCH
Transforming Data from Bales to Business Insights with Bales 227.
Croperation and Upgrowth worked together to develop a data hub that allows for ingestion of data via both API and email.
The platform initially focused on the cotton industry, which is known for its heavy reliance on large data sets that are sent via email with csv attachments. To solve this problem, Croperation collaborated with Ecom Trading and AWH to design and test an API solution that creates a two-way data connection, eliminating the need for manual data handling whilst allowing for gradual transition from emails and attachments to APIs.
Bales 227 was created to serve as a drop box for data, facilitating the automatic transfer of data between Gins, Classers, and Warehouses. This eliminates the need for manual data handling, and ensures that a merchant or farmer's data is instantly available in one centralised location.
Bales 227 was created to serve as a drop box for data, facilitating the automatic transfer of data between Gins, Classers, and Warehouses. This eliminates the need for manual data handling, and ensures that a merchant or farmer's data is instantly available in one centralised location.
Developing the Bales 227 Dashboard.
Keen to learn more?
CASE STUDY 04 / MIC 4.0
Streamlining the Way Farmers and Brokers Manage Online Tenders.
Croperation partnered with Upgrowth to develop a Tenders platform, simplifying the process for farmers and brokers to create and manage online tenders.
Farmers have faced challenges in recapping their own cotton for selling through tenders to merchants. To address this, MIC 4.0 was developed. Cotton data requires software to process it and produce information that buyers can use to determine the value of the cotton.
Creating MIC 4.0
Keen to learn more?
CASE STUDY 05 / JOHN DEERE
Heat Maps and Harvests: Visualising Cotton Quality with John Deere Tractors.
Upgrowth developed a proof of concept to identify the location of cotton bales and match them with quality data from the gin.
Cotton farmers in Australia have access to a wealth of data through their use of John Deere Tractors. However most farmers are not about to utilising this information in a meaningful way through data visualisation.
We collaborated with a large family farm and analysed the data collected during the cotton-picking process. By examining the data, we could identify the geographical coordinates of where the module was dropped in the paddock.
With the module ID and Ginning and Classing information, we connected the cotton quality to the specific paddock it was grown in. We are able to visually demonstrate the varying qualities (using heat maps) produced in each field.
We collaborated with a large family farm and analysed the data collected during the cotton-picking process. By examining the data, we could identify the geographical coordinates of where the module was dropped in the paddock.
With the module ID and Ginning and Classing information, we connected the cotton quality to the specific paddock it was grown in. We are able to visually demonstrate the varying qualities (using heat maps) produced in each field.
Let's partner up
Looking for a progressive company to partner with? Reach out to see how we can help you deliver your next project.