Sugar Research Australia
Transforming complex data into an innovative mobile harvesting predictive tool for cane farmers
Key Points
- By collaborating with Clevvi, SRA aimed to transform a complex harvest decision support tool into a user-friendly web/mobile application
- By leveraging AWS serverless architecture, Clevvi transformed the Excel-based model into a cost effective, low maintenance app designed to accommodate users with varying levels of computer literacy
- Clevvi's understanding of the importance of building in scalability to the solution granted SRA the ability to scale down its services when usage of the tool is low to reduce costs and maintenance
Sugar Research Australia (SRA) is the principal research and development corporation for the Australian sugar industry. As an industry-owned organisation, SRA invests in research across the sugar production chain to improve productivity, profitability and sustainability. SRA aims to drive continuous improvement in the competitiveness and resilience of the Australian sugar industry through strategic research programs aligned to industry priorities.
Transforming a complex, Excel-based model into a user-friendly web and mobile application that can cater to a userbase with wide-ranging computer literacy, and allows continuous updates and easy maintenance by SRA staff.
- Clevvi used AWS serverless technologies and architecture for building a low-maintenance solution. "What was really important for Clevvi was NOT handing something to SRA's IT department that would need a lot of maintenance and support. What appealed to us was that AWS has a suite of managed services and platforms-as-a-service”, says James Deck, Clevvi's CEO.
- The tool allows SRA staff to update the back end with the latest research findings and economic inputs, helping farmers and harvesters adopt harvesting best practices (HBP). SRA estimates that optimised harvesting could add $44 million to Australian sugarcane growers’ revenue. With AWS's serverless architecture, SRA can scale down services during low harvesting predictive tool usage before harvest, reducing costs and maintenance effort from staff.
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