Supply Chain Analytics

Business users, like sales and supply chain managers, often struggle to process and analyze large datasets. Extracting key insights—such as order conversion rates, inventory turnover, demand fluctuations, and forecasting—can be challenging, especially when dealing with complex or overwhelming volumes of information. Traditional BI tools often require technical expertise, making it difficult for these stakeholders to quickly obtain actionable information to make timely and data-driven decisions.

To address these challenges, DIAL offers an open, flexible, and feature-rich platform that can be integrated with the existing software ecosystem and data sources. It provides all the necessary tools and technologies to create powerful, goal-oriented solutions that meet the specific needs of the business and are intuitive for both power and business users.

In this case study, we describe our multi-agent application for supply chain managers which enables them to work, analyze and interpret sales data stored in database to analyze orders conversion rates using only natural language.

Key highlights:

• Integration with Corporate Data: We created a dedicated multi-agent application, that can draw data from the corporate Databricks Lakehouse by transforming requests from business users in natural language into SQL queries and returning answers as narrative explanations and data visualizations.
• Data Validation and Transparency: Users can preview the steps and reasoning behind each agent's action, ensuring transparency and accuracy.
• Structured and Unstructured Data: The application uses DIAL RAG as an agent to upload and analyze files in addition to the data extracted from the data storage. The context is preserved throughout the conversation, enabling the combination of findings from the SQL database and uploaded files to generate rich visualizations and analytics.

Outcomes and Benefits:

The implemented solution can bring significant benefits for a retail enterprise by improving data accessibility and enabling research and analysis for non-technical users. The application quickly transforms raw information into actionable insights using natural language queries and AI agents, enabling business users to easily explore data, uncover trends, and draw insights. Intuitive and dynamic visualizations further enhance data exploration and analysis.