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 Canadian retail enterprise struggled with three major issues:
- Products frequently went out of stock
- Some items piled up as excess inventory
- Sales forecasts were often inaccurate
This created problems across stores — unhappy customers, lost sales, and poor inventory decisions.
To solve this, we built an ai-powered demand forecasting system connected to a Power BI Retail Dashboard.
The system predicted upcoming sales more accurately, optimized stock levels, and improved ordering decisions.
Result: They reduced stockouts by 30% within three months, improved forecasting accuracy, and strengthened overall supply chain performance.
Main KPIs Tracked
These KPIs directly help retail teams understand whether they have the right products, in the right quantity, at the right time:
- Forecast Accuracy - Shows how close the AI’s predicted sales are to the actual sales. Higher accuracy is equal to better planning.
- Stockout Rate - Tracks how often products run out of stock. Lower stockouts means happier customers.
- Inventory Turnover Ratio - Tells how quickly inventory is sold and replaced. Higher turnover means products are moving efficiently.
- Fill Rate - Shows how many customer orders were fulfilled without delay. Higher fill rate means fewer lost sales.
- Demand Variance (Forecast vs. Actual) - Highlights gaps between predicted demand and actual demand so teams can correct forecasting errors.
- Days of Inventory on Hand (DOH) - Tells how many days current stock will last. Helps prevent overstocking or running out.
- Lead Time Adherence - Tracks whether suppliers are delivering on time. Late deliveries hurt forecasting and stock planning.
- Sales per Product Category - Shows which product categories are performing well or need attention.
These KPIs were converted into clear auto-updating visuals, making them easy for teams to understand in seconds.
Solution Offered — ML-Based Demand Forecasting Dashboard Development
We followed a detailed, 5-step approach to build the AI-powered forecasting system:
Data Integration & Preparation
We gathered critical retail data from various systems (POS, ERP, inventory management), ensuring we had a comprehensive view of sales history, supplier performance, promotions, and seasonal trends.
The data was cleaned, missing values were handled, and variables were standardized to ensure accurate model training. This allowed us to create a reliable dataset for demand forecasting.
Machine Learning Model Development
We built time-series models using algorithms like ARIMA, Prophet, and Gradient Boosting. These models predicted sales based on historical data, weather patterns, holidays, and promotional events.
Each algorithm was tested for accuracy, and the best-performing one was selected for deployment. Continuous performance monitoring was set up to ensure forecast accuracy improved over time.
Dashboard Design & Visualization
We designed a custom Power BI dashboard that visually displayed real-time demand predictions, inventory health, and sales forecasts. This allowed all teams to monitor key metrics at a glance.
The dashboard included easy-to-understand charts, traffic-light indicators, and drill-down reports, making it accessible even to non-technical users across departments.
Automation & Real-Time Updates
We integrated a system that automatically updates the forecasts as new sales data, inventory levels, and external factors (like weather or holidays) come in.
The model was designed to retrain itself regularly, adjusting to new patterns, seasonal trends, and changing consumer behavior to maintain its accuracy.
Insight Sharing & Collaboration
Dashboards and reports were made shareable, allowing merchandising, procurement, and finance teams to collaborate effectively on stock planning and replenishment decisions.
We implemented alerts that notified relevant teams when critical issues arose — such as when stock was running low or when forecasts deviated significantly from actual sales.
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 helps teams accurately predict sales trends, keep inventory in sync with demand, and reduce supply chain issues.
01
Reduce Stockouts & Overstock
Accurately predict demand to keep stock levels balanced, avoiding running out of products or overbuying.
02
Improve Forecast Accuracy
Use machine learning to adjust forecasts based on real-time data, seasons, and promotions for better predictions.
Enhance Supply Chain Efficiency
Streamline procurement and restocking, reducing manual tasks and delivery delays.
04
Optimize Working Capital
Free up cash tied in unsold stock while ensuring enough products are available during busy times.
05
Enable Data-Driven Planning
Give decision-makers a real-time view of supply, demand, and sales, making it easier to plan and budget effectively.
Who Gains Actionable Insights from This Dashboard?
Tech Stack Used
A powerful and flexible system combining analytics, machine learning, and cloud technologies to provide accurate, real-time insights for retail.
Power BI
Azure Machine Learning
SQL Database
Results Achieved
The AI-powered demand forecasting model improved key inventory and performance metrics, making operations more efficient and boosting customer satisfaction.
Key Results:
The AI model shifted the retailer from reactive inventory management to proactive planning, leading to higher profits, faster operations, and happier customers.