AI-Powered Demand Forecasting Retail Dashboard
Project category
UrbanLeaf Market
Retail
Canada
3.5 Months
Ready to reduce stockouts and improve inventory accuracy with AI-powered demand forecasting?
Project Overview
A large retail enterprise from Canada sought to reduce frequent stockouts, optimize inventory management, and improve sales forecasting accuracy across multiple stores and product categories.
Our team implemented a Machine Learning–based Demand Forecasting Solution integrated with a custom Power BI retail dashboard. This system enabled accurate sales predictions, optimized procurement planning, and reduced excess inventory — achieving a 30% reduction in stockouts within just three months.
Main KPIs Tracked
The Demand Forecasting Dashboard focused on the most critical metrics for retail operations and supply chain performance:
- Forecast Accuracy
- Stockout Rate
- Inventory Turnover Ratio
- Fill Rate
- Demand Variance (Forecast vs. Actual)
- Days of Inventory on Hand (DOH)
- Lead Time Adherence
- Sales per Product Category
Solution Offered — ML-Based Demand Forecasting Dashboard Development
We delivered a data-driven forecasting and analytics solution through a 5-step structured approach:
Data Integration & Preparation
Consolidated historical sales, promotions, supplier lead times, and seasonal data from ERP and POS systems into a centralized SQL database.
Ensured data quality, missing value handling, and normalization for model training.
Machine Learning Model Development
Built and deployed time-series forecasting models using Azure Machine Learning, incorporating variables like historical demand, pricing, promotions, weather, and holidays.
Algorithms such as ARIMA, Prophet, and Gradient Boosting were compared and optimized for the best accuracy.
Dashboard Design & Visualization
Developed a custom Power BI dashboard that visualized real-time demand predictions, stock levels, supplier performance, and replenishment schedules — providing a unified view of supply chain health.
Automation & Real-Time Updates
Integrated automated model retraining pipelines and data refresh schedules to ensure continuous forecasting accuracy and real-time visibility into demand trends.
Insight Sharing & Collaboration
Enabled secure sharing of reports and alerts across merchandising, supply chain, and finance teams — ensuring alignment between planning and execution.
Main Features of the Dashboard
Designed to empower decision-makers with real-time insights, the dashboard combines advanced analytics and intuitive visualization to simplify demand planning. It provides a unified view of performance metrics, helping teams act faster and smarter across the retail network.
Business Benefits of AI-Powered Demand Forecasting
The demand forecasting dashboard empowered teams to predict future sales trends accurately, align inventory with demand, and minimize supply chain inefficiencies.
01
Reduce Stockouts & Overstock
Predict demand accurately to maintain optimal stock levels and minimize lost sales or overstocking.
02
Improve Forecast Accuracy
Leverage ML algorithms that adapt to real-time data, seasonal variations, and promotional effects for precise forecasting.
Enhance Supply Chain Efficiency
Optimize procurement and replenishment cycles, reducing manual intervention and lead time variability.
04
Optimize Working Capital
Free up cash flow tied in excess inventory while ensuring product availability during high-demand periods.
05
Enable Data-Driven Planning
Provide decision-makers with a unified, real-time view of supply, demand, and sales — supporting proactive planning and budgeting.
Who Gains Actionable Insights from This Dashboard?
Tech Stack Used
A robust and scalable architecture combining analytics, machine learning, and cloud technologies to deliver accurate, real-time retail insights.
Power BI
Azure Machine Learning
SQL Database
Results Achieved
The implementation of an AI-powered demand forecasting model delivered measurable improvements across key inventory and performance metrics, driving both operational efficiency and customer satisfaction.
Key Results:
The ML-driven forecasting model shifted the retailer’s approach from reactive inventory management to predictive planning, resulting in higher profitability, improved operational agility, and stronger customer satisfaction.