Fuzzy Delphi: A Comprehensive Overview

Introduction:

Fuzzy Delphi is an extension of the traditional Delphi method that incorporates fuzzy logic to handle uncertainties and imprecise information during the decision-making process. It's widely employed in various fields to enhance the accuracy and reliability of forecasting and consensus-building.

Advantages:

1. Handling Uncertainty:

Explanation: Fuzzy Delphi excels in scenarios where information is vague or uncertain. It allows participants to express their opinions using linguistic terms, enabling a more nuanced representation of uncertainty.

Example: When predicting market trends, where factors may not have precise numerical values, fuzzy logic helps capture the uncertainty in participants' forecasts.

2. Incorporating Subjectivity:

Explanation: Fuzzy Delphi acknowledges and accommodates the inherent subjectivity in expert opinions. Participants can use linguistic variables, reflecting their subjective judgments more accurately.

Example: When assessing the feasibility of a new technology, experts may use terms like "highly likely" or "somewhat probable" to convey their opinions more subjectively.

3. Flexible Decision-Making:

Explanation: The flexibility of fuzzy logic allows for more adaptive decision-making. It accommodates changes in expert opinions over iterations, providing a dynamic approach to consensus building.

Example: In project management, if initial estimates are modified due to unforeseen circumstances, Fuzzy Delphi enables a smoother adjustment of forecasts.

Disadvantages:

1. Complexity in Implementation:

Explanation: The introduction of fuzzy logic adds a layer of complexity to the Delphi method. Participants may require additional training to understand and effectively use linguistic variables.

Example: In organizations unfamiliar with fuzzy logic concepts, implementing Fuzzy Delphi may require extra effort in educating participants about the methodology.

2. Increased Iteration Time:

Explanation: Fuzzy Delphi often involves multiple iterations to converge towards a consensus. This prolonged process may be impractical in situations where quick decisions are required.

Example: In fast-paced industries like technology, where rapid decision-making is crucial, the extended time required for Fuzzy Delphi iterations might be a drawback.

3. Potential for Ambiguity:

Explanation: The use of linguistic terms can introduce ambiguity in responses, leading to misinterpretation. Lack of standardized linguistic scales may result in varied interpretations of participants' inputs.

Example: Different participants may interpret terms like "moderately likely" differently, leading to potential misunderstandings in the consensus-building process.

In general, Fuzzy Delphi offers valuable advantages in handling uncertainty and subjectivity but comes with challenges related to complexity, time, and potential ambiguity. Its suitability depends on the specific context and the importance of accommodating fuzzy information in the decision-making process.

Although the Delphi Technique has been widely used in education, especially in anticipation of the future, this method has its drawbacks. Among the weaknesses of the Delphi method (S. Siraj 2008):

1) Reliability of the data depends on expertise; if the researcher fails to deliver real experts mean the study will lose credibility,

2) Experiments are repeated on a sample, and this will cause boredom to the sample,

3) A small number of experts are not able to resolve all the issues studied and

4) Less chance of getting a response from the emotional aspect.

To solve the problem of ambiguity in the consensus of experts, researchers from around the world have created new methods. Murray, T.J., Pipino, L. L & Gigch (1985) proposed the application of Fuzzy Delphi Method Theory into semantic variables used to solve the problem of ambiguity in the Delphi Technique. Fuzzy Delphi Method was derived to solve the problems of traditional Delphi Technique (Fuziah Rosman Mohd Nazri Ab Rahman, Saedah Siraj 2013; Glumac et al. 2011; Ishikawa, A., Amagasa, T., Tamizawa, G., Totsuta, R. and Mieno 1993; Saedah Siraj 2012).

The Fuzzy theory was introduced to improve time-consumption and solving the fuzziness of common understanding in experts’ opinions (Hwang, C.L. and Lin 1987; Noorderhaben 1995).

The Ishikawa works used the maximum-minimum method together with cumulative frequency distribution and fuzzy scoring to compile the expert opinions into fuzzy numbers. The experts’ interval value was then used to derive the fuzzy numbers resulting in the FDM. This method was based on group thinking of the qualified experts that assures the validity of the collected information.

Hsu, H., & Chen (1996) proposed a fuzzy aggregate equation. By using this similarity function, the similarity between experts can be collected and fuzzy numbers can be built directly into each expert to determine the degree of agreement between them. Then the coefficient of consensus is used to get value assessment fuzzy numbers for all specialists. If the degree of agreement is too low among experts, then the questionnaire must be administered again.

The advantages of Fuzzy Delphi Method are.

1) saves time on the questionnaire,

2) save costs,

3) reduce the total number of surveys, questionnaires increase the recovery rate,

4) experts can fully express their opinions, ensure completeness and consistency of opinion and

5) taking into account the ambiguity that cannot be avoided during the study. This method does not misinterpret original expert opinion and gives their real reactions.

The Fuzzy Delphi Method (FDM) is a modified and enhanced version of the classical Delphi technique. This method combines the traditional Delphi Method with Fuzzy Set Theory to address some of the ambiguity of the Delphi panel consensus. The FDM rectifies the imperfections of the traditional Delphi Method that can lead to low convergence in retrieving outcomes, loss of important information, and a lengthy progress of investigation. It utilizes triangulation statistics to determine the distance between the levels of consensus within the expert panel2. This approach has been employed in various application domains, including humanities, management, business, physical science, and engineering.