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Leading fashion retailer with more than 3000 physical stores and an e-commerce website that serves over 20 million active users.
Retail
Our machine learning solution helped solve critical logistics challenges by accurately predicting order picking and automating shift scheduling. We addressed issues stemming from inaccurate shipping estimates that disrupted workforce scheduling. Our approach integrated sales forecasts into a predictive framework, leveraging advanced metrics to significantly enhance operational efficiency across distribution centers. This solution optimized staffing levels, minimized labor costs, ensured timely shipments, and improved overall customer satisfaction through precise order picking forecasts.
The logistics team faced significant issues with the accuracy of their shipping estimates, which directly impacted workforce scheduling at distribution centers. The primary challenges were:
Enhance operational efficiency and cost-effectiveness by implementing a machine-learning model to accurately predict shipping volumes and automate shift scheduling.
We implemented a machine learning-based approach focusing on time series modeling. We evaluated ARIMA (AutoRegressive Integrated Moving Average), SARIMA (Seasonal ARIMA), and Prophet models, using metrics like MAPE (Mean Absolute Percentage Error), R-squared (R²), and MAE (Mean Absolute Error). Prophet emerged as the most effective model.
The logistics team faced significant issues with the accuracy of their shipping estimates, which directly impacted workforce scheduling at distribution centers. The primary challenges were:
Enhance operational efficiency and cost-effectiveness by implementing a machine-learning model to accurately predict shipping volumes and automate shift scheduling.
We implemented a machine learning-based approach focusing on time series modeling. We evaluated ARIMA (AutoRegressive Integrated Moving Average), SARIMA (Seasonal ARIMA), and Prophet models, using metrics like MAPE (Mean Absolute Percentage Error), R-squared (R²), and MAE (Mean Absolute Error). Prophet emerged as the most effective model.