Demand Prediction for E-Commerce
Project Overview
A mid-sized eCommerce company offering thousands of SKUs across shifting seasonal demand struggled with inventory inefficiencies. Their planning team relied heavily on spreadsheets, historical averages, and guesswork. As they expanded into new markets, inaccurate forecasting became more costly.
Challenges & Constraints
The business lacked a reliable forecasting system capable of accounting for:
- Seasonal fluctuations
- Flash sales and promotional spikes
- Market trends
- Supplier delays
- Multi-warehouse stock differences
This led to:
- Frequent overstocking
- Stockouts during peak seasons
- Cashflow strain
- Excess storage costs
- Customer dissatisfaction due to product unavailability
Project Solution
MindRind designed a time-series and ML-driven demand prediction engine capable of producing SKU-level forecasts with precision.
The solution delivered:
- Multi-Feature Forecasting Models
Used sales history, seasonality, promotions, and external variables. - Automated Restocking Alerts
Notified the procurement team of low stock and expected demand. - Predictive Sales Dashboard
Offered dynamic forecasting visualizations and SKU-level predictions. - Warehouse-Level Forecasting
Predicted inventory needs across multiple fulfillment centers.
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Client Satisfaction Rate
Our Approach
- Data Analysis & Cleansing
Combined historic sales, marketing events, supplier timelines, and web traffic analytics. - Model Development
Built multiple forecasting models and compared performance to select the best fit. - Integration with ERP
Ensured predictive outputs flowed directly into their stock planning system. - Monitoring System
Set up accuracy tracking and recalibration logic to maintain long-term precision.
Technologies Used
Python • Prophet • Time Series ML • Power BI • Cloud Pipelines
Results
- 28% improvement in forecasting accuracy
- 17% reduction in inventory holding costs
- 32% decrease in stockouts
- Warehouse operational cost savings increased significantly
Client Impact
The company gained predictable stock planning, improved revenue stability, and resolved some of its biggest operational pain points. Forecasting moved from manual guesswork to automated intelligence.
Let's Address Your Questions Today!
Yes, the models retrain regularly to stay aligned with new trends and patterns.
Absolutely. The system is designed for scale and can forecast thousands of items simultaneously.
Yes, promotional calendars and pricing events are part of the forecasting pipeline.
Yes, warehouse-level forecasting is supported.
Project Name
Demand Prediction for E-Commerce
Category
AI/ML
Duration
3 Months
