Discuss why exponential smoothing is not recommended as a forecasting tool in situation. 

Time series analysis and forecasting

Exponential smoothing is a forecasting technique which involves using past data in order to predict future values. This can be helpful when trying to forecast things such as customer demand or sales trends over time, however it is not recommended for use in all situations.

One of the primary reasons why exponential smoothing should not be used as a forecasting tool is because it does not take into account any outside influences (such as market conditions or political events) that could potentially affect outcomes. Additionally, this method also relies heavily upon historical data when making predictions which means it may not accurately represent current trends and therefore could lead to inaccurate forecasts if there are too many extremes present within dataset being analyzed.

Moreover, since exponential smoothing only looks at one variable (e.g., sales quantity) at a time it can’t account for relationships between different factors – thus making its utility limited when attempting more complex predictions. Finally, while this approach is fairly straightforward implement/understand it doesn’t always produce high quality results either so best exercise caution before deciding whether or not use such techniques.

All in all then, exponential smoothing can provide useful insights into certain types operations but should never relied upon exclusively make decisions.