Qualitative models are forecasting techniques based on judgmental or subjective factors. When a totally new product such as the iPad is introduced, forecasting demand is very difficult due to the lack of any historical sales data on that particular product or on similar products. The company must rely on expert opinion, individual experiences and judgment, and other subjective factors.

Here is a brief overview of four different qualitative forecasting techniques:

1. Delphi method. This iterative group process allows experts, who may be located in different places, to make forecasts. There are three different types of participants in the Delphi process: decision makers, staff personnel, and respondents. The decision-making group usually consists of 5 to 10 experts who will be making the actual forecast. The staff personnel assist the decision makers by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results. The respondents are a group of people whose judgments are valued and are being sought. This group provides inputs to the decision makers before the forecast is made.

   In the Delphi method, when the results of the first questionnaire are obtained, the results are summarized, and the questionnaire is modified. Both the summary of the results and the new questionnaire are then sent to the same respondents for a new round of responses. The respondents, upon seeing the results from the first questionnaire, may view things differently and may modify their original responses. This process is repeated with the hope that a consensus is reached.

2. Jury of executive opinion. This method takes the opinions of a small group of high-level managers, often in combination with statistical models, and results in a group estimate of demand.

3. Sales force composite. In this approach, each salesperson estimates what sales will be in his or her region; these forecasts are reviewed to ensure that they are realistic and are then combined at the district and national levels to reach an overall forecast.

4. Consumer market survey. This method solicits input from customers or potential customers regarding their future purchasing plans. It can help not only in preparing a forecast but also in improving product design and planning for new products.

A variety of quantitative forecasting models are available when past numerical data are available. Forecasting models are identified as causal models if the variable to be forecast is influenced by or correlated with other variables included in the model. For example, daily sales of bottled water might depend on the average temperature, the average humidity, and so on. A causal model would include factors such as these in the mathematical model. Regression models (see Chapter 4) and other more complex models would be classified as causal models.

Time series are also quantitative models, and many time-series methods are available. A time-series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. These models are extrapolations of past values of that series. While other factors may have influenced these past values, the impact of those other factors is captured in the previous values of the variable being predicted. Thus, if we are forecasting weekly sales for lawn mowers, we use the past weekly sales for lawn mowers in making the forecast for future sales, ignoring other factors such as the economy, competition, and even the selling price of the lawn mowers.