Qualitative Forecasting Methods: Delphi Method

The Delphi Method
Originally developed by the RAND Corporation in 1969 for technological forecasting, the Delphi Method is a group decision process about the likelihood that certain events will occur. Today it is also used for environmental, marketing and sales forecasting.

The Delphi Method makes use of a panel of experts, selected based on the areas of expertise required. The notion is that well-informed individuals, calling on their insights and experience, are better equipped to predict the future than theoretical approaches or extrapolation of trends. Their responses to a series of questionnaires are anonymous, and they are provided with a summary of opinions before answering the next questionnaire. It is believed that the group will converge toward the "best" response through this consensus process. The midpoint of responses is statistically categorized by the median score. In each succeeding round of questionnaires, the range of responses by the panelists will presumably decrease and the median will move toward what is deemed to be the "correct" answer.

One distinct advantage of the Delphi Method is that the experts never need to be brought together physically, and indeed could reside anywhere in the world. The process also does not require complete agreement by all panelists, since the majority opinion is represented by the median. Since the responses are anonymous, the pitfalls of ego, domineering personalities and the "bandwagon or halo effect" in responses are all avoided. On the other hand, keeping panelists for the numerous rounds of questionnaires is at times difficult. Also, and perhaps more troubling, future developments are not always predicted correctly by iterative consensus nor by experts, but at times by "off the wall" thinking or by "non-experts".

Quantitative Forecasting Techniques vs Qualitative Forecasting Techniques

QUANTITATIVE TECHNIQUES :-

Quantitative forecasting techniques are generally more objective than their qualitative counterparts. Quantitative forecasts can be time-series forecasts (i.e., a projection of the past into the future) or forecasts based on associative models (i.e., based on one or more explanatory variables). Time-series data may have underlying behaviors that need to be identified by the forecaster. In addition, the forecast may need to identify the causes of the behavior. Some of these behaviors may be patterns or simply random variations.

Among the patterns are: Trends, which are long-term movements (up or down) in the data.

Seasonality, which produces short-term variations that are usually related to the time of year, month, or even a particular day, as witnessed by retail sales at Christmas or the spikes in banking activity on the first of the month and on Fridays.

Cycles, which are wavelike variations lasting more than a year that are usually tied to economic or political conditions.

Irregular variations that do not reflect typical behavior, such as a period of extreme weather or a union strike.

Random variations, which encompass all non-typical behaviors not accounted for by the other classifications.

QUALITATIVE TECHNIQUES :-
Qualitative forecasting techniques are generally more subjective than their quantitative counterparts. Qualitative techniques are more useful in the earlier stages of the product life cycle, when less past data exists for use in quantitative methods. Qualitative methods include the Delphi technique, Sales Force Forecast (Opinions of Sales Force members), Nominal Group Technique (NGT), Jury of Executive Opinions, Users Expectations (via surveys, questionnaires, and other tools) and market research.

When to use Qualitative Forecasting

As we already know qualitative information is descriptive in nature, relating to, or involving quality or kind. It has no numerical values which we can use in certain mathematical formulae.

We use Qualitative Forecasting :-
- if past data cannot be used reliably to predict the future.
- in case of technological trends
- for predicting Policies, Regulations
- When no past data is available, usually because the situation is very new.
- in case of entrance into new markets
- for Development of totaly new products

There are different methods for Qualitative Forecasting that will be described at different topic.

What is Forecasting in Engineering?

Forecasting is a systematic effort to anticipate future events or conditions. The most well known type of forecast may be that of the meteorologist who prepares daily weather forecasts that help us decide how to dress each day and whether to take an umbrella when we leave for work in the morning. Other common forecasts are those that anticipate future economic conditions, traffic patterns, and even the size and number of classrooms that will be needed in local schools.

An engineering manager must be concerned with both future markets and future technology and must therefore understand both sales and technological forecasting and the most important premise or assumption in planning and decision making is the future sales. almost everything for which we plan is based on this assumption.

Forecasting is the most important part of planning since the results of both lie in the future. The fierce nature of today's competitive landscape has forced engineering organizations to operate more efficiently, not just on a day-to-day basis but also in planning for the future. Competitive advantage requires more than estimates and educated guesses. Even business expertise can take you only so far.

Engineering managers as well as other managers need a wide range of integrated capabilities for time series analysis and forecasting, econometrics and systems modeling, and financial analysis and reporting, with direct access to the internal/external commercial data warehouses/marts. With the power of an effective forecasting, managers can:

• Forecast demand related to their products and services.
• Decide staffing and resource needs.
• Predict customer and market behavior.
• Analyze investment options.
• Perform production plant site selection analysis.
• Make effective pricing decisions.
• Plan and understand markets for their products and services.

Things that can be forecast:

• Production levels.
• Technological developments.
• Needed manpower.
• Governmental regulations.
• Needed funds.
• Traning needs.
• Resource needs.
• Sales levels (The most critical information to forecast)

There are many techniques for forecasting. But all depend on two different types of data and/or information namely Qualitative Information, and Quantitative Information. They will be mentioned in detail in a different topic.