Efficient material forecasting is critical for ensuring **timely and cost-effective delivery** in construction and manufacturing projects.  
However, variations in project type, size, and market demand often make manual estimates unreliable — resulting in costly **overstocking** or **critical shortages**.  

### Challenges with Manual Estimation  
- **Overstocking** → Ties up working capital and storage space.  
- **Shortages** → Causes project delays and increased costs.  

### Opportunity with Machine Learning  
By applying **predictive modeling** and integrating it into supply chain workflows, organizations can improve:  
- **Material visibility**  
- **Procurement planning efficiency**  
- **Project delivery timelines**  

### Your Challenge  
Develop a **predictive analytics solution** that:  
1. Estimates the **type and quantity of materials** required for upcoming projects using historical consumption patterns and project metadata.  
2. Identifies **nearby vendors** to source the predicted materials efficiently.  

### Link to the webinar session recording
- [Webinar 22nd August](https://dprprod-my.sharepoint.com/:v:/g/personal/nileshb_vconstruct_in/EcyADfBtp5hHuoVSjsl6OsYBexieDfWzgR65wAOm05nb2Q?e=Sd427c)