Automated Sales Data & Rebate Processing with Intelligent Data Integration

Project Solution

INNERLUXES created an automated platform for a healthcare supply chain aggregator to streamline its rebate management. The solution standardizes and validates distributor sales data, automates delivery to manufacturers, and reduces manual processing.

The Client

The Client is a player in the medical supply chain that partners with many manufacturers and distributors. A significant portion of its operations revolves around a complex rebate program, in which manufacturers offer rebates based on distributors' sales volumes. With distributors submitting data in inconsistent formats and rebate processing performed manually, the Client faced a serious scalability bottleneck and data-visibility issue. It engaged INNERLUXES to create a centralized, automated system that could handle increasing data volumes, eliminate rebate-calculation delays, and improve accuracy.

The Approach

The goal was to replace the Client's fragmented, manual processes with a robust, scalable data integration system built with Azure technologies and modern data warehousing. Key actions taken:

• Built automated pipelines that connect to multiple distributor FTP servers.
• Automatically ingested and transformed multiple data types (CSV, Excel, proprietary).
• Cleansed, mapped, and validated data using dynamic schema discovery.
• Stored clean data in a Snowflake data warehouse for centralized access.
• Reformatted and sent data to manufacturers in the required formats via secure FTP.
• Established automated scheduling and monitoring for reliability and transparency.

The Solution

INNERLUXES implemented a dynamic Azure Data Factory solution integrated with Snowflake to streamline how transaction data is ingested, transformed, and formatted for rebates.

Key capabilities:

• Automated ingestion: Azure Data Factory connects to each distributor's FTP on a fixed schedule, downloads the files, and stages them in Azure Blob Storage.
• Dynamic data transformation: schema detection automatically identifies the structure of incoming files; mapping and cleansing convert fields to a standard format, remove duplicates, resolve missing fields, and standardize date formats; and validation rules ensure data accuracy before loading into the warehouse.
• Centralized storage in Snowflake: a unified data warehouse stores all cleaned and transformed sales data, enabling efficient querying, auditability, and analytics.
• Manufacturer-specific outputs: the system filters, aggregates, and formats data according to each manufacturer's needs, then sends it securely via FTP.
• Scalable, monitored pipelines: ADF pipelines are fully automated and monitored using Azure Monitor to flag failures and performance declines.

The Impact

• Efficiency gains: automated ingestion and rebate workflows reduce manual workload.
• Improved accuracy: validation and standardization cut down on rebate errors.
• Full visibility: a centralized system provides sales and refund transparency to all stakeholders.
• Scalability: easily supports growth in distributor and manufacturer partnerships.
• Faster processing: rebate cycles are shortened, strengthening external relationships.
• Data-driven decisions: better data quality fuels smarter, faster strategic choices.
• Lower operational costs: automation reduces time, labor, and error-related expenses.

Technologies and Tools

Azure Data Factory, Snowflake Data Warehouse, Azure Blob Storage, FTP protocol, dynamic schema mapping, data validation & cleansing, CSV/Excel/fixed-width formats, Azure Monitor.