Adwaid P

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Demand Forecasting Case Study

An end-to-end forecasting system built to predict future sales and improve inventory planning with trend and seasonality awareness.

Problem

Manual planning caused delayed inventory decisions and weak visibility into demand spikes and seasonal drops.

Tech Stack

  • Python, Pandas, NumPy for data preprocessing
  • ARIMA and Prophet for time-series forecasting
  • Matplotlib/Seaborn for diagnostics and forecast visualization
  • MAE and RMSE for performance validation

Approach

  • Performed exploratory time-series analysis to identify trend and seasonal components
  • Engineered lag features and rolling averages for stability
  • Trained and compared ARIMA and Prophet across validation windows
  • Selected best-performing model using error metrics and consistency checks

Results

  • Produced clearer demand projections for short-term planning
  • Reduced forecasting error compared with baseline estimates
  • Enabled data-driven planning decisions for inventory workflows

Challenges and Solutions

  • Handled noisy series with smoothing and outlier review
  • Managed seasonal shifts through model comparison and tuning
  • Improved feature quality using lag/rolling engineering