## Demand forecasting

Operations managers can use visual reporting to sync your shop's manufacturing with sales and ML to forecast future demand.

## Advanced customer segmentation

Understand textual customer feedback, group similar customers into clusters, and segment customers based on demographic and buying behavior to gain a rich approach to customer segmentation.

## Predictive maintenance

Analyze machine logs, maintenance records and historical machine performance metrics to reduce downtime, improve safety and avoid costly breakdowns.

## Report financials

Create powerful executive dashboards for near-real-time updates on revenue and loss.

## Securely share data

Safely communicate information across multiple facilities, organizations and even countries.

## Enable smart manufacturing

Use the power of IoT and data to address challenges like predicting machine failures, reducing greenhouse gas emissions, improving supply chain management, reducing material costs and optimizing production for improved operational margins.

- Demand forecasting
- Advanced customer segmentation
- Predictive maintenance
- Report financials
- Securely share data
- Enable smart manufacturing

## Demand forecasting

Operations managers can use visual reporting to sync your shop's manufacturing with sales and ML to forecast future demand.

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  **Use cases for predictive analytics in manufacturing**  
  Predictive analytics provides your manufacturing operations with the ability to extract valuable insight from the complex and diverse data you’re already gathering, seeing well beyond the horizon.
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  **Data science for Demand Forecasting**  
  Analyzing and understanding your data provides valuable insight into demand for your products, improving the accuracy of your financial planning, strengthening sales and marketing efforts, and more.
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  **Manufacturing & retail company**  
  An international manufacturing company was struggling to consolidate sales metrics across a variety of markets. The process for generating reports was unclear to business intelligence teams and stakeholders, and it seemed that much of the work was done manually by one person.