Enhanced Shipping Prediction for Retail Logistics
Optimizing order picking and workforce scheduling through advanced machine learning models.
Client
A leading fashion retailer with 3000+ stores and an e-commerce platform serving over 20 million users worldwide.
Problem Statement
Inaccurate shipping forecasts caused inefficiencies in workforce scheduling, resulting in delays, overstaffing, and higher operational costs.
Industry
Solution
Quick Summary
- QBurst implemented a machine learning-based prediction model to enhance order picking accuracy and automate shift scheduling.
- Integrated sales forecasts into predictive workflows to align staffing with real-time demand.
- Leveraged Prophet and time series modeling to account for seasonality, trends, and promotions.
- Achieved measurable improvements in cost savings, scheduling accuracy, and customer satisfaction.
Client Profile
Leading fashion retailer with more than 3000 physical stores and an e-commerce website that serves over 20 million active users.
Challenges: Mismatch Between Shipping Forecasts and Workforce Allocation
- Inaccurate shipping estimates caused mismatched workforce allocations across distribution centers.
- Overestimation led to overstaffing and inflated labor costs.
- Underestimation triggered delays, stock shortages, and missed delivery targets.
- Manual forecasting systems lacked adaptability to sales fluctuations and seasonal variations.
QBurst Solution: Machine Learning–Powered Demand Forecasting and Workforce Optimization
We deployed a machine learning-driven solution centered on time series forecasting to predict order-picking demand with precision. Multiple models, ARIMA, SARIMA, and Prophet, were evaluated using metrics such as MAPE, R², and MAE, with Prophet emerging as the most accurate and robust model.
- Integrated Prophet for sales forecasting and order-picking prediction
- Used sales forecasts as regressors to strengthen predictive accuracy
- Incorporated seasonality, holiday effects, and promotional data to refine forecasts
- Automated shift scheduling based on predicted order volumes
- Delivered interactive dashboards to visualize forecasts and historical performance
Technical Highlights
- Time series modeling with ARIMA, SARIMA, and Prophet
- GCP Vertex AI for scalable model training and deployment
- BigQuery for data warehousing and analytics
- Automated retraining and tuning pipelines using Python, Pandas, NumPy, and SciPy
- Intuitive dashboards for real-time operational insights
Sales Forecasting Graph
Order Picking Forecasting Graph
Impact
- Up to 30% improvement in prediction accuracy for shipping and order-picking volumes.
- Reduced labor inefficiencies through optimized scheduling across multiple distribution centers.
- Significant cost savings from minimized overstaffing and reduced operational disruptions.
- Improved on-time delivery rates leading to enhanced customer satisfaction and loyalty.
- Scalable solution capable of adapting to seasonal and promotional variations.
Client Profile
Challenges
QBurst Solution
Technical Highlights
Sales Forecasting Graph
Impact