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Implementation of ML-Powered Demand Forecasting System with Real-Time Visualization

instinctools.com

eCommerce

Manufacturing

Challenges with Traditional Demand Forecasting

Manual Excel-based forecasting caused low long-term accuracy (23%), frequent stockouts, overstock issues, supply chain inefficiencies, and inability to adapt to market changes due to rigid linear regression models and fragmented data sources.

About the Client

Online clothing retailer with garment manufacturing operations in Vietnam serving Canadian consumers

Strategic Forecasting Transformation Goals

Achieve 90%+ long-term demand forecasting accuracy
Reduce stockouts to below 10%
Maintain overstock levels under 5%
Automate forecasting workflows
Enable real-time market adaptation
Implement scenario modeling capabilities

Core System Capabilities

Recurrent Neural Network (RNN) forecasting model
Power BI visualization dashboards
Automated data pipeline integration
Multi-source data aggregation (internal/external)
Real-time forecast recalibration
What-if scenario modeling for promotions/market shifts
Automated retraining mechanism

Technology Stack Requirements

Azure Machine Learning

Power BI

Recurrent Neural Network (RNN)

Azure SQL Database

Microsoft Dynamics 365

System Integration Needs

E-commerce platform APIs
Social media analytics tools
Third-party marketplace data sources
Supply chain management systems
Inventory tracking solutions

Operational Requirements

99.9% system availability
Real-time data processing (<1s latency)
GDPR-compliant data handling
Horizontal scalability for seasonal demand
Automated performance monitoring
Role-based access control

Business Transformation Outcomes

Enables data-driven inventory optimization through 92% accurate demand predictions, reduces operational costs via automated workflows, enhances supply chain resilience, and supports strategic decision-making through interactive visual analytics while maintaining compliance with regulatory requirements.