Integrating private LLMs and ensemble forecasting

After-sales operations in the automotive industry depend heavily on the availability of critical parts. When inventory gaps occur, service delays and customer dissatisfaction follow—creating measurable financial impact. Forecasting this is complex, since the demand distribution is shaped by lifecycle behavior, geography, seasonality, and unexpected disruptions

TVS Motor Company is a multinational mobility solutions company offering motorcycles, scooters, mopeds, three-wheelers, and electric vehicles. It is among the top three two-wheeler manufacturers in India. It ranks within the top five globally, with a customer base of over 60 million and 9,500+ sales and service outlets worldwide. TVS Motor is the second-largest two-wheeler exporter from India, present in 80+ countries across multiple regions. The company is customer-centric, with its products ranked No. 1 in the J.D. Power Survey in seven of the last eight years till 2025.

Forecasting in practice: A simplified view

The forecasting engine blends classical statistics, machine learning, and deep learning models to address the diverse behavior of over 6,000 parts across more than 90 countries. The ensemble mitigates risk by reducing dependency on any single method and produces stable projections with 95% confidence interval.

Beyond accuracy, the benefit to planners is prioritization. The system highlights where volatility is emerging, where forecast performance is stable, and where intervention may be warranted.

How the private LLM layer adds value

Forecast outputs often require interpretation before they become actionable. The private LLM addresses this by generating structured, role-specific narratives—without exposing data to external systems.

Because the model is locally hosted, it incorporates internal vocabulary, accuracy rules, and segmentation structures. Planners receive insights focused on execution. Analysts obtain diagnostic patterns. Executives see financially relevant summaries.

This alignment supports faster understanding and more consistent decisions across regions and domains within the company.

Technical architecture: Only what matters

Figure 1 summarizes the system architecture. All components operate within the com- pany’s secure perimeter, supporting both analytical rigor and data governance.

Figure 1: End-to-end architecture integrating forecasting, private LLMs, and governed datawithin the enterprise environment.aption

1. A multi-model forecasting engine that generates stable predictions.
2. A private LLM layer that produces consistent, business-aligned narratives.
3. A data and API governance layer ensuring controlled access and traceability.

Context engineering with two private LLMs

Figure 2 shows the two-model LLM workflow that standardizes how insights are con- structed.

Figure 2: Two-model private LLM context-engineering loop enabling secure, consistent forecasting insights.

Model 1 interprets user intent and applies evaluation standards. Model 2 executes domain- level analysis and returns structured findings. Model 1 assembles the final narrative, referencing governed data. This ensures consistency, auditability, and compliance.

Implementation: Scorecards and trends

The performance scorecard serves as the operational cockpit, summarizing accuracy, bias, and performance distribution.

The AI insights highlight where accuracy is improving or deteriorating, enabling proactive responses. Private LLM-generated explanations help teams focus on insights that matter most. Likewise, similar AI insights and scorecards are generated at item, location, and SKU levels.