Workforce Optimization in Logistics Warehouses
Technology:
Time Series Ensemble, NeuralProphet, Airflow, Streamlit
Operating a logistics warehouse presented a unique set of difficulties. With a wide array of products experiencing intermittent demand, workforce allocation became a constant challenge. Their internal procedures, though established, were agile enough to cope with daily fluctuations but wanted a bit more efficiency. Ensuring the right amount of labor on the right days, without wasting resources or falling short, was paramount.
We introduced a data-driven forecasting model tailored to their specific needs. Dealing with sparse data, we leveraged imputation methods and, critically, combined client domain knowledge with iterative adjustments. Our approach didn’t just stop at forecasting; we integrated optimization methods to guide workforce scheduling. This combination of refined time series models and optimization gave them the tools to better anticipate and respond to varying demand, resulting in more effective and efficient operations.