Automotive Assembly Line
Use Cases

Next-Gen Automotive: Where Personalization Meets Autonomy

Dealer Service Analytics

The Client's Challenge

Lack of actionable data related to auto performance and customer use made it difficult to optimize dealer service communications and operations and enhance customer satisfaction.

Spearhead Solution:

Dealer KPIs (sales, customer feedback, service quality) are fed into predictive ML models.

  • XGBoost predicts future performance and identifies areas of improvement.
  • Sentiment analysis (NLP models like RoBERTa) processes customer feedback to uncover pain points.

Interactive dashboards provide dealership managers with recommendations for specific interventions.

Solution Diagram

Technology Utilized:

Algorithms: XGBoost for prediction; NLP models (RoBERTa) for sentiment analysis.

  • Tools: Power BI/Tableau for visualizing KPIs and trends.
  • Integration: APIs connect dealership data systems to the analytics platform.
  • Cloud Infrastructure: Microsoft Azure for scalable data processing.

Benefits Realized:

  • 30% improvement in dealership service performance through targeted interventions.
  • Faster identification and resolution of performance bottlenecks.
  • Increased alignment with corporate sales and service goals.
Futuristic car wireframe Reinventing Mobility

Supply Chain Optimization

The Client's Challenge

Supply chain delays were being caused by inaccurate demand forecasting. Overstocking was raising inventory expenses, while stockouts resulted in lost sales opportunities and reduced customer satisfaction.

Spearhead Solution:

  • Mixed-Integer Linear Programming (MILP) optimizes supply chain resource allocation (e.g., inventory levels, transportation routes).
  • Gradient Boosting Machines (GBMs) predict demand at SKU-level granularity.
  • Risk scoring for supplier disruptions using Bayesian Network.

Technology Utilized:

Algorithms: GBMs (e.g., XGBoost, LightGBM) for forecasting; MILP for optimization.

  • Cloud Platforms: AWS S3 for data storage and Snowflake for data aggregation.
  • Integration: APIs connect ERP systems to ML pipelines.
  • Visualization: Power BI for dashboards showing forecast accuracy and supply chain KPIs.

Benefits Realized:

Improved forecast accuracy by 30%, reducing excess inventory.

  • Enhanced flexibility to respond to supply chain disruptions, minimizing losses.
  • Improved dealer satisfaction with better order fulfillment rates and reduced lead times.
Solution Diagram

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