Outcreating Supply Chain Intelligence for a Leading Global Manufacturer with AI-Powered Demand Forecasting

Our Client

A leading global manufacturer in the paints and coatings industry, serving both retail and industrial segments across multiple markets.

The organization operates a large distributor network and manages a complex, high-volume portfolio of SKUs. With significant variability in distributor-level demand, improving forecast accuracy and inventory efficiency was critical to strengthening service levels while minimizing non-moving and slow-moving inventory.

Challenges

The client sought to improve secondary sales service fill for the General Trade market while optimizing inventory across its distributor-SKU network. The challenges included:

• Limited visibility into distributor-SKU level demand was making it difficult to forecast secondary sales accurately.
•  Inaccurate or delayed demand forecasts were affecting the ability to plan inventory for the upcoming month.
•  High levels of non-moving and slow-moving inventory were tying up working capital and reducing inventory efficiency.
•  Inventory levels were difficult to optimize across the distributor network, creating imbalances between availability and excess stock.
•  Diverse historical sales patterns across a large SKU portfolio made conventional forecasting approaches difficult to scale and maintain accuracy.
• The sheer volume of distributor-pack level data—billions of computations— made it challenging to generate actionable forecasts within operational timeframes.
•  Forecasting capabilities were difficult to scale beyond the pilot market, creating challenges in extending the solution to an all-India deployment.

The scale and variability of the distributor-SKU network made a single forecasting approach insufficient for accurately capturing different demand behaviors.

LTM's Solution

LTM developed a machine learning-powered demand forecasting solution designed to estimate monthly sales demand at the distributor-SKU level.

The approach combined historical pattern recognition with differentiated forecasting algorithms to improve accuracy and scalability. Key aspects of the solution included:

Demand Pattern Recognition

LTM analyzed historical sales patterns and categorized SKUs into distinct demand groups, including:

• Trend
• Trend + seasonality
• Seasonality
• Random
• Persistent sales

This pattern-based segmentation enabled forecasting algorithms to be tailored to the underlying behavior of individual SKU groups.

Distributor-SKU Level Forecasting

The solution generated demand forecasts for the following month at a granular distributor-SKU level, enabling the business to move beyond aggregated forecasting and gain greater visibility into localized demand.

Scalable Machine Learning Architecture

Following a successful pilot in one market, the forecasting solution was scaled across India to support the client's broader distributor network and SKU portfolio.

The solution was designed to process approximately 9.6 billion distributor-pack level computations while completing demand estimation within 24 hours.

Cost-Optimized Execution

LTM leveraged SPOT Fleet instances to optimize the cost of large-scale model execution, significantly reducing the infrastructure expense associated with high-volume forecasting computations.

Business Benefits

The intelligent demand planning model transformation delivered measurable improvements in forecasting speed, scalability, accuracy, and execution economics:

• Enabled nearly 9.6 billion computations at the distributor-pack level to support granular demand forecasting.
• Completed monthly demand estimation in less than 24 hours, enabling faster supply chain planning.
• Significantly improved demand forecasting accuracy through pattern-specific machine learning algorithms.
• Reduced execution costs by 90% through the use of SPOT Fleet instances.
• Enabled more granular demand visibility across the distributor-SKU network.
• Supported improved inventory planning by identifying different demand behaviors across SKUs.
• Scaled the forecasting capability from a single-market pilot to an All-India deployment.

Conclusion

By combining machine learning, demand pattern recognition, and cost-optimized large-scale computing, LTM helped the client transform distributor-level demand forecasting into a faster and more granular supply chain intelligence capability. The solution created a scalable foundation for more responsive inventory planning and continued supply chain optimization across the business.

Ready to Outcreate your inventory with AI-powered supply chain intelligence? Contact us at info@ltm.com.