**Quantitative forecasting** can be characterized by one of the two basic **techniques**: Time Series – The future will tend to look and behave like the past. For example, gasoline prices for the next six months will continue along the same lines as they have over the past six months. We devote the rest of this chapter to **quantitative forecasting**. While our variable of interest throughout the example is the volume of sales, the ideas, concepts, and methods can be applied to any other variable. Characteristics of **Forecasting Techniques**. All **forecasting techniques** have three main characteristics in common. 1. **Forecasting** **techniques** can be classified as either qualitativ e or **quantitative**. This distinction may have no bearing on the accuracy of the forecast Self-Driven **Forecasting** **Techniques**. Statistical (stochastic) **techniques**. These **techniques** focus entirely on patterns, pattern changes, and. It has found its applications in varied domains such as Economic **Forecasting**, Technology **Forecasting**, Weather **Forecasting**, Sales **Forecasting**, Demand **Forecasting** etc. The paper attempts to understand various **Quantitative Forecasting Techniques** and explores reasons for using a particular method in a given study scenario. **techniques** in which time varying effects can be explicitly considered. Examples: Forvestor model, Mesarovic model, Meadows model (club of Rome), etc. Some of the methods are simple variants of **techniques** used in other areas. These methods are mainly used to eliminate subjective errors associated with intuitive **forecasting techniques**. . Step 6 Monitor the forecast Step 5 Prepare the forecast Step 4 Gather and analyze data Step 3 Select a **forecasting** **technique**. Step 2 Establish a time horizon Step 1 Determine purpose of forecast. Fixed order quantity models ?Economic order quantity ?Production order quantity ?.