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Development of an Advanced Sales Forecasting System for Global Fashion Retailer

n-ix.com

Retail

eCommerce

Consumer products & services

Business Challenge

Inaccurate sales forecasting for new products leading to inventory misallocation, overstocks, understocks, and missed revenue opportunities in a highly seasonal and trend-driven market

About the Client

Leading global fashion retailer with thousands of stores worldwide, specializing in fast-paced fashion retail and omnichannel sales

Key Objectives

Enhance sales forecasting accuracy for new products by 50% within the first weeks of release
Optimize inventory allocation across global stores based on predictive analytics
Reduce product deficit rates to below 5% through improved demand prediction
Minimize unsold inventory costs while maintaining product availability

Core System Capabilities

Machine learning model for sales prediction using historical product comparisons
Dynamic trend analysis incorporating seasonal patterns and consumer behavior shifts
Product similarity engine (color, size, category, style) for accurate benchmarking
Automated ETL pipeline for handling large-scale retail datasets
Forecast accuracy tracking dashboard with deficit rate monitoring

Technology Stack

MLflow

Snowflake

Azure Databricks

Apache Spark

Scala

Azure

System Integrations

Enterprise Resource Planning (ERP) systems
Point-of-Sale (POS) systems
Market trend data APIs
Inventory management platforms

Operational Requirements

High scalability for handling terabytes of global sales data
Real-time processing capabilities for trend adaptation
99.9% system availability during peak forecasting periods
Role-based access control for sensitive sales data

Expected Business Impact

Projected 50% improvement in sales forecast accuracy leading to optimized inventory levels, 5% or lower product deficit rates, significant cost savings from reduced overstocking, and enhanced customer satisfaction through improved product availability across all channels