Cement Sales Forecasting with AI

In the highly competitive construction industry, accurately forecasting cement sales is crucial for balancing inventory, meeting demand, and avoiding costly overstocking or stockouts.

The Challenge

Our client, a major cement distributor in Southern Europe, struggled with:

• Significant demand variability across regions and months
• Frequent stockouts leading to missed sales opportunities
• Overstocking in low-demand areas causing waste and cost overruns
• Manual forecasting processes that failed to adapt to real-time market changes

Our Approach

We implemented a tailored AI-based forecasting system using:

• Time series modeling with Facebook Prophet and LSTM neural networks
• Regional clustering to group similar sales patterns
• External factor integration (weather, holidays, market indices)
• Interactive Power BI dashboards for local managers
• Automated retraining pipelines using Azure ML and GitHub Actions

Results and Impact

The project delivered concrete business value:

• 68% reduction in stockouts
• 19% increase in forecast accuracy
• $730K in annual savings from optimized logistics
• Improved service level agreements (SLAs) across all 15 distribution centers

Key Learnings

• AI models must be continually retrained with up-to-date data
• Visual tools empower non-technical stakeholders to act on predictions
• Collaborative workshops with sales and operations teams enhanced adoption

Looking Forward

Based on the success of the cement forecasting model, the client is expanding AI forecasting to other materials like aggregates and concrete additives.

This use case showcases how AI can tackle complex supply chain challenges in traditional industries and deliver measurable impact.