title = "Demand Forecasting Techniques"

content = """
Accurate demand forecasting enables proactive inventory management and
operational planning in wholesale distribution. Multiple forecasting methods
balance historical data with market intelligence to predict future requirements.

**Time Series Analysis:** Moving averages smooth out seasonal variations and
random fluctuations. Exponential smoothing gives more weight to recent data
points. Trend analysis identifies long-term growth or decline patterns.
Seasonal decomposition separates seasonal, trend, and residual components.

**Statistical Methods:** Linear regression correlates demand with external
factors like economic indicators or promotional activity. Multiple regression
incorporates multiple predictor variables. ARIMA models capture autocorrelation
patterns in time series data.

**Qualitative Approaches:** Market research and customer surveys provide
insights for new product introductions. Expert judgment from sales teams and
key customers. Delphi method aggregates expert opinions through iterative
rounds.

**Machine Learning:** Neural networks and random forests learn complex
patterns from historical data. Gradient boosting algorithms improve accuracy
through ensemble methods. Deep learning models handle high-dimensional data
with multiple influencing factors.

**Forecast Accuracy Measurement:** Mean Absolute Percentage Error (MAPE)
measures forecast accuracy as percentage deviation. Tracking forecast bias
identifies systematic over- or under-forecasting. Forecast value added
analysis determines which forecasting methods provide the most benefit.

**Integration with Planning:** Rolling forecasts update predictions as new
data becomes available. Scenario planning tests forecast sensitivity to
different market conditions. Safety stock calculations incorporate forecast
uncertainty and desired service levels.
"""

version = "0.0.1"
