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What is a Fluidic Enterprise?

Imagine a human-centric enterprise that thrives on the use of AI to give it superpowers, where technology and human ingenuity converge in harmony. A Fluidic Enterprise is:

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Why human-centric?
The Fluidic Enterprise puts people first. While it uses AI to be more responsive, intimate, efficient, creative, and sustainable, the main focus is always on the human—whether they're employees or customers. It blends human qualities like intent, creativity, and empathy with AI's power. It emphasizes the responsible and ethical use of AI. The goal is to make technology work for people, not the other way around. 

 

Co-creation and customization
Our Fluidic Enterprise vision provides a flexible framework that allows for customization to meet your unique needs. Through our 'Fluidic Enterprise Vision Workshop', we blend your insights with our expertise to tailor a precise vision for your organization.

Fluidic Enterprise Vision in Action

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. 

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Efficient

Example: A leading aluminum coil manufacturer aimed to enhance their order scheduling system. They faced challenges like over-reliance on human schedulers, unclear delivery dates for new orders, and difficulties in adjusting work shifts according to production needs. Nagarro introduced a tool based on genetic algorithms to mitigate these issues, focusing on reducing coil order delays and decreasing machine waiting times. This tool also included strategies for efficiently storing ready coils. An interactive screen allowed experts to adjust production plans as needed.. 

Metric: As a result, manual scheduling efforts decreased by 80%, and resource utilization improved by 4%. 

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Intimate

Example: To address the challenge of processing a large volume of ambiguous leads from social media, an automotive major in the Middle East aimed for a deeper understanding of potential buyers. Nagarro introduced a bespoke lead prioritization framework using Bayesian modeling. This framework could evaluate the potential of leads even from minimal initial data and utilized incremental rescoring to refine lead assessments as more information became available during the sales process. 

Metric: This nuanced approach improved lead management, boosting monthly sales by 8-10 vehicles, equating to an uplift of approximately $160,000 to $220,000 USD per month. 

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Creative

Example: Nagarro has transformed the car evaluation process for a global automotive OEM with an AI-driven application that leverages computer vision, breathing creativity into what was once a routine task. Skilled at handling the subtleties of over 4,000 daily car evaluations, this tool not only boosts operational efficiency but also revolutionizes customer interactions. The application is currently in a pilot phase at selected service centers, with a strategy for broad deployment across more than 5,000 locations, marrying advanced technology with a dedication to customer-centric innovation. 

Metric: With a 90% accuracy rate in automatic defect detection, the application standardizes vehicle assessments, slashes training costs, and fosters customer trust by ensuring transparency, all contributing to a superior customer experience. 

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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. 

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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.

We are shaping the company of tomorrow

We are not trying to emulate the companies of yesterday, we are the company of tomorrow. We are the future, logistically apart, together always.

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