LTIMindtree enhances CPG demand forecasting with Azure-based accelerator
LTIMindtree's innovative Demand Forecasting Accelerator leverages Microsoft's Azure platform to enable Consumer Packaged Goods (CPG) businesses to predict product demand at the Distributor SKU level. By automating the framework with advanced machine learning and AI, the solution identifies optimal time-series models, improves inventory planning, manages safety stocks, and enhances supply chain coordination. This scalable and accurate forecasting solution empowers businesses to tailor demand prediction across product categories.
- Organization
- LTIMindtree
- Industry
- Consumer & Food
- Location
- India
- Published
- April 2025
Reported outcomes
Strategic outcomes
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- LTIMindtree
- Provider
- Microsoft
- Maturity
- Production
- Linked source
- azuremarketplace.microsoft.com
Manual demand forecasting was time-consuming and prone to errors Difficulty predicting demand at granular Distributor SKU level Inefficient inventory planning leading to excess stock or stockouts Limited ability to tailor forecasts across varied product categories Lack of supply chain coordination due to inaccurate demand signals Developed automated ML framework for scalable time-series forecasting on Azure ML Deployed demand forecasting accelerator tailored for CPG Distributor SKU level Impleme
Primary read
Use case focus
Showing 3 of 5
- 1Automated Demand Forecasting at Distributor SKU Level
- 2Predictive Inventory Optimization for CPG Supply Chains
- 3Dynamic Safety Stock Calculation Using ML
- Manual demand forecasting was time-consuming and prone to errors
- Difficulty predicting demand at granular Distributor SKU level
- Inefficient inventory planning leading to excess stock or stockouts
- Limited ability to tailor forecasts across varied product categories
- Lack of supply chain coordination due to inaccurate demand signals
- Developed automated ML framework for scalable time-series forecasting on Azure ML
- Deployed demand forecasting accelerator tailored for CPG Distributor SKU level
- Implemented model selection to optimize forecasts per product category
- Automated preprocessing, training, and metric logging pipelines for experiment tracking
- Achieved more accurate demand forecasts at SKU/distributor level
- Improved inventory planning and reduced stockouts/excess inventory
- Enhanced coordination across supply chain segments
- Enabled faster and more tailored forecasting across product categories
Sources & evidence1
- Customer explicitly identified
- Deployment status explicitly supported
- Technical implementation details available
- Recent evidence check available
- Last evidence check: Jul 22, 2026.
The case's original source is still reachable.
- Cited source last checked Jun 12, 2026 — ok (0/1 broken).
Measures whether this deployment's public evidence persists — not whether the system is still in production.
AI-generated summary. Verify important details with the linked sources before relying on this case.
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