Powering a leading carmaker's smart factory vision

We, first, adopted a consultative approach to ensure alignment with the organizational objectives, foster collaboration and maximize transformative outcomes. We engaged with diverse stakeholders, conducted on-site plant visits, and interacted with factory teams.

Through our discovery workshops and assessment of the IT landscape, we defined the business goals, KPIs, and Industry 4.0 use cases. The outcome of this extensive exercise was a comprehensive, and actionable technology adoption roadmap, ready for implementation.

The success of the program lay in crafting an efficient data management strategy. The complex web of systems, processes, and diverse access requests posed a challenge in establishing a standardized data management and fulfillment framework.

To surmount this challenge, we first developed detailed personas of the data consumers, highlighting their unique requirements and pain points.

Next, we devised a comprehensive data management strategy with standardized processes of data ingestion, validation, storage, transformation, and consumption. This approach not only addressed the key pain points of the consumer but also streamlined data operations to achieve excellence.

Inspection processes are often labor-intensive and prone to errors. We built a digital solution to automate the inspection processes of ready-to-ship vehicles, which ensured robustness, transparency, and traceability. The solution’s modular design allows for quickly recording observations and capturing defects through photographs taken during inspections. The solution can also be adapted by other applications such as preventive maintenance or shifts changeover. The solution frees the workforce to focus on continuous improvement, increasing productivity, and decreasing operational costs through paperless processes.

We harnessed Nagarro's IoT platform accelerator to streamline the entire data lifecycle, from acquisition and storage to management and consumption. Our solution not only consolidates data from various sources, including sensors, PLCs, and IoT devices but also optimizes the organization of domain-specific data for rapid access.

Edge device applications capitalize on this wealth of data for advanced analytics and AI/ML initiatives. The platform offers continuous health monitoring for the IoT ecosystem, streamlining onboarding and device upgrades, and managing massive daily data ingestions from numerous process shops located in diverse manufacturing sites. This eliminates data silos, empowers real-time alerts, and fuels data-driven decision-making.

On the data front, we implemented standardized frameworks for data fulfilment and a secure methodology to govern data generation, sharing, and consumption.

We architected a scalable platform for data-driven business intelligence solutions. This platform features unified dashboards, offering a comprehensive health and cost report of cloud resources across accounts, projects, and cloud services with thresholds to monitor usage.

Furthermore, the platform introduces a critical layer of data quality assurance by monitoring the accuracy of ML models and the early detection of anomalies. It significantly amplifies enterprise-level visibility into data and service health, driving enhanced efficiency and productivity.