Automating Data Extraction and Demand Forecasting for Retail Efficiency

Demand forecasting across hundreds of stores and multiple regions is a complex challenge, especially when relying on manual processes. This can hinder efficiency and accuracy, making it difficult to track sales, monitor prices, and manage warehouses.

INTECH addressed these issues by implementing an RPA solution with UiPath, automating data extraction from shopping portals. 

The organization operates in the retail industry, managing a broad presence across multiple markets. Their operations depend on gathering and analyzing data from competitor shopping portals to support accurate demand forecasting. 

The client needed a way to gather and process data from shopping portals to forecast demand accurately. Their existing manual processes posed several challenges:

Tracking sales and demand across hundreds of stores was time-consuming and error-prone.

Monitoring competitor pricing data required significant manual effort.

Managing purchase orders and warehouse updates often faced delays due to inefficiencies.

Standard forecasting approaches relying on past sales data lacked accuracy, especially for diverse products across multiple regions.

With manual methods consuming hours and delivering inconsistent results, the client sought an automated solution to enhance efficiency and accuracy.

INTECH implemented an RPA-based automation solution using UiPath to address these challenges. The solution automated data extraction and analysis processes, enabling the client to:

Extract real-time data from shopping portals efficiently.

Reduce human intervention, eliminating manual errors in data collection and processing.

Improve forecasting accuracy by providing actionable insights derived from reliable data.

This automation streamlined operations, reduced processing time, and provided the foundation for more strategic decision-making.

1.  Understanding the requirements:

INTECH collaborated with the client to define the exact data sources and key use cases for automation, ensuring the solution met the client’s operational needs. This phase focused on understanding the data types, extraction methods, and specific business requirements for accurate forecasting.

2. Proof of Concept (POC):

A partial POC was developed to test the feasibility and functionality of the bot in real-world conditions. This initial version demonstrated the bot’s capabilities in data extraction, allowing both parties to evaluate its performance before full-scale implementation.

3, Development and Configuration:

The UiPath-based bot was custom made to handle client’s unique needs, particularly the extraction of data from multiple shopping portals across hundreds of SKUs and regions. Custom workflows and configurations were created to ensure the bot could process large volumes of data accurately and efficiently.

4. Testing:

The solution underwent comprehensive testing to validate its performance in terms of accuracy, reliability, and seamless integration. Multiple test cases were executed to ensure the bot met the client’s standards and could integrate smoothly into their existing systems without causing disruption.

5. Deployment:

After successful testing, the solution was deployed across the client’s systems. The deployment was carefully managed to minimize any disruption to ongoing operations, ensuring a smooth transition to the automated solution without affecting day-to-day activities.