What is a
Fluidic Enterprise?

Responsive

Example: For a major telecom client navigating post-Covid market uncertainties and chip shortages, Nagarro developed a tailored ML-driven forecasting and inventory management system. This system fine-tunes inventory levels by considering a variety of influential factors, such as the impact of online streaming service adoption on device demand, inflation, and seasonal variations. This advanced approach empowered the client to implement a flexible and responsive inventory strategy. 

Metric: This initiative led to a 10% reduction in inventory holdings for the client, resulting in savings of around $10Mn, while maintaining a 98% service level. 

Sustainable

Example: Nagarro employed its Machine Vision platform to aid one of the globe's most extensive in-flight catering providers. The challenge was enhancing flight food experiences. Using images of leftover food trays, Nagarro devised a solution to determine consumption patterns. This process involved capturing tray images upon the airplane's landing as they passed through an automatic belt, followed by machine vision analysis to deduce dish preferences across different classes. 

Metric: The solution showcased >99% prediction accuracy for airline and fare class, >93% for dish detection, and >90% for dish consumption (fully eaten, partially eaten, not eaten). Such precision aids airlines in refining food menus and reducing wastage costs. 

Evolving metrics with AI

The beauty of AI-driven solutions in the Fluidic Enterprise model is that they are inherently dynamic. As they consume more data and refine their algorithms, the benefits often magnify. The metrics/benefits mentioned above are indicative of early-stage implementations. As AI continues to learn and adapt, organizations can expect even better results, further enhancing the responsiveness, efficiency, intimacy, creativity, and sustainability of the enterprise.