Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast.
And because these techniques consist mainly of analyzing objective or hard data, they usually avoid personal biases that sometimes contaminate qualitative methods.
Unlike qualitative methods which permit subjective inputs and the inclusion of soft information such as human factors, personal opinions and hunches which are difficult or impossible to quantify, quantitative methods involve either the projection of historical data or the development of associative models that attempt to utilize causal variables to make a forecast.
Elements of a good forecast:
The forecast should be timely.
The forecast should be accurate.
The forecast should be reliable.
The forecast should be expressed in meaningful units.
The forecast should be in writing.
The forecast technique should be simple to understand and use.
The forecast should be cost-effective: The benefits should outweigh the costs.
To learn more, see a Microsoft PowerPoint presentation of Operations Management Forecasting from CSUS.edu.