A major agricultural company, operating as a key entity under a larger global conglomerate, holds a pivotal position in the farming sector. Headquartered in Europe, it takes the forefront in integrating advanced technologies to navigate the complexities of smart manufacturing, ensuring they provide crop protection and innovative seed solutions to support the farming community worldwide.
The primary ambition for integrating Litmus Edge into the organization’s framework was twofold. The aim was to efficiently collect data from various equipment and pioneer an advanced model for early detection of leaks and optimize capping quality ensuring quality control in the chemical packaging process. Furthermore, the initiative sought to establish a robust platform capable of deploying fine-tuned machine learning models leveraging real-time data.
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Before the successful implementation of Litmus Edge, the customer dealt with:
Scattered data repositories
Loss of precious time due to the unprocessed raw data from diverse operating systems
Inherent complexities of a multi-vendor strategy and thereby the lack of a comprehensive and unified approach that could seamlessly solve:
Data connectivity
Data processing and analysis
Edge operations management
After initial discussions with the Litmus team that commenced during the iTap 2023 event in Singapore, the customer went through a proof of concept and trial period that extended over a six-month span. The results were clear-cut, with Litmus Edge showcasing a user-friendly interface, extensive support for various drivers and controllers, effortlessly deployed containers for running machine learning models, and an outstanding level of technical support.
Given the scale of their infrastructure, deploying and using Litmus Edge was remarkably quick, requiring only a few hours for installation and fine-tuning to achieve operational readiness.
The team leveraged some of the foundation capabilities of Litmus Edge to unlock the full potential of their OT data.
Exhaustive library of native industrial connectors
Efficient data collection and normalization
Meaningful contextualization of operational technology data
The implementation of Litmus Edge, augmented by domain-specific expertise, paved the way for the customer to achieve an impressive accuracy rate of 89% in detecting and classifying capping quality using machine learning models. This is a significant leap from the 38% accuracy rate achieved via traditional statistical methods.
Today, the customer is actively evaluating broader applications of Litmus Edge across its operations, signifying the platform's potential for integration into a variety of use cases.
This customer’s story highlights the platform's potential to greatly simplify the unified namespace for data, making it an indispensable tool in the user’s technological arsenal.
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