Enhanced Shipping Prediction for Retail Logistics

Leading fashion retailer with more than 3000 physical stores and an e-commerce website that serves over 20 million active users.

• 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.
   

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
   

• 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
   

Order Picking Forecasting Graph

• 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.