Problem

The steel industry is highly competitive, and Roll Shops need to find ways to reduce costs while maintaining quality. One of the biggest expenses for Roll Shops is purchasing rolls for hot rolling mills. With so many brands available, it can be difficult to determine which ones are the most cost-effective without spending a significant amount of time researching and analyzing data.

Solution

We developed a vertical SaaS product that uses machine learning algorithms to analyze data from various sources and determine which brands of rolls are the most cost-effective for each customer. The product takes into account factors such as roll wear, maintenance costs, and production output to provide customers with personalized recommendations.

Impact

Reduced roll purchasing costs by X%
Increased customer satisfaction by providing personalized recommendations
Streamlined roll purchasing process, saving customers time and resources
Improved accuracy of roll purchasing decisions, leading to better overall performance of hot rolling mills

Team

Jane Smith - Project Manager
- Oversaw project planning and execution
- Liaised with stakeholders to ensure project met their needs
John Doe - Data Scientist
- Built machine learning algorithms used in the product
- Analyzed data from various sources to inform algorithm development
Maria Garcia - UX Designer
- Designed user interface for the product based on customer feedback
- Conducted user testing to ensure product was intuitive and easy to use

Revolutionize Steel Industry with Cost-Effective Roll Purchasing