Yannis Siskos

Preference disaggregation: 20 years of MCDA experience

Abstract The philosophy of preference disaggregation in multicriteria decision-aid systems (MCDA) is to assess/infer global preference models from the given preferential structures and to address decision-aiding activities. This paper presents a panorama of preference disaggregation methods and summarises the most important results and applications over the last 20 years.

Customer satisfaction measurement in the private bank sector

Abstract Customer satisfaction represents a modern approach for quality in enterprises and organisations and serves the development of a truly customer-focused management and culture. Measuring customer satisfaction offers an immediate, meaningful and objective feedback about clients’ preferences and expectations. In this way, company’s performance may be evaluated in relation to a set of satisfaction dimensions that indicate the strong and the weak points of a business organisation. This paper presents an original customer satisfaction survey in the private bank sector. The implemented methodology is based on the principles of multicriteria analysis and preference disaggregation modelling. The most important results are focused on the determination of the critical service dimensions and the segmentation to customer clusters with distinctive preferences and expectations.

A survey of customer satisfaction barometers: Some results from the transportation-communications sector

Abstract The most important efforts that have been reported for the development of generic satisfaction barometers during the last years refer to individual business organisations, industry sectors or the total of national economies. The main aim of these efforts is the data collection either for comparative analysis of companies’ performance regarding customer satisfaction or for monitoring the evolution of global and partial satisfaction indices. In addition, the generic satisfaction barometers provide the ability to correlate basic economical dimensions with customer satisfaction like productivity variations at a national level or changes in the general consumer price index. This study presents the most important efforts for the measurement of customer satisfaction at the transportation-communications sector within the frame of development and installation of national satisfaction barometers. Furthermore, the models, which constitute the basic methodological tools for the measurement and the analysis of the presented satisfaction barometers, are briefly presented through this paper.

Intelligent multicriteria decision support: Overview and perspectives

This paper presents a survey of the history and the recent status of the Multicriteria Decision Support Systems (MCDSSs). The last generation of MCDSSs is based on the synergistic operation of Artificial Intelligence and Multicriteria Analysis (MA) approaches. The contribution of these two different approaches into an integrated MCDSS is analysed through this paper, which concludes providing the recent software developments in this field.

Measuring Customer Satisfaction Using a Collective PreferenceDisaggregation Model

A new model to assess customer satisfaction is developed through this paper. The proposed model is based on the principles of multicriteria analysis, using ordinal regression techniques. The procedure uses surveys data on customer satisfaction criteria and disaggregates simultaneously all the global satisfaction judgments via a linear programming disaggregation formulation. The model provides collective global and partial satisfaction functions as well as average satisfaction indices. These results sufficiently describe customer behavior and they can be used in the strategic planning of an organization. The implementation of the model in three real world applications is used for illustration and for testing the models reliability. Finally, several extensions and future research in the area of customer satisfaction analysis are discussed.

Customers’ perspectives on the quality of organic olive oil in Greece

Consumers’ increasing need for safe and quality food has motivated this market research study. A customer satisfaction approach is followed to examine whether the quality of organic olive oil, the most important biological product that has recently entered the Greek food market, could affect consumers’ purchasing behavior. For this purpose a survey was conducted in the region of Thessaloniki, Greece. The implemented methodology is called MUlticriteria Satisfaction Analysis (MUSA) and takes into account that customers’ global satisfaction for organic olive oil depends on five criteria. The analysis showed that the average global satisfaction index was almost 78 percent. Health was considered to be the competitive advantage of organic olive oil. On the contrary, promotion and disposition seemed to be its greatest weaknesses. The price, along with the package, was also found to be significant. Finally, gives some recommendations for the continuing improvement of information and advertising, label information and image of the package, which are the weak characteristics of organic olive oil.

MARKEX: An intelligent decision support system for product development decisions

A new methodology for the development of new products and an intelligent DSS, named MARKEX, which is an implementation of this methodology, are presented in this paper. The system acts as a consultant for marketers, providing visual support to enhance understanding and to overcome lack of expertise. The databases of the system are the results of consumer surveys, as well as financial information of the enterprises involved in the decision making process. The systems model base encompasses statistical analysis, preference analysis, and consumer choice models. MARKEX incorporates partial knowledge bases to support decision makers in different stages of the product development process.

Intelligent Decision Support Methods

The effort to create machines with some sort of intelligence began almost 100 years ago with the ideas of Babbage (1884) as shown in Figure 1. In 1950, Alan Turin, the “father of Artificial Intelligence” (Barr and Feigenbaum, 1981), presented the famous Turing test, which gives an answer to the question if a machine is able to think as a human being (Rich, 1983). Turing not only developed a simple, general and non-arithmetic computational model, but he also supported that computational models could possibly behave with a way that could be deemed “intelligent”. In 1950, Shannon supported that someone could play a game of chess with the help of computer and in 1955 he proved his idea by developing a chess program while, later Samuel (1963) developed a checkers program. Wiener (1948), founder of cybernetics, contributed by recognizing the similarities in functions of humans and machines. In overall, the actual goal of this effort was to understand and find a solution on how to embody in a computer the ability of human beings to think and rationalize (Durkin, 1994). The term “Artificial Intelligence” was used for the first time by John McCarthy (1963; 1969; 1977; 1980; 1995) during a conference held in Dartmouth College (1956).

Evaluating country risk: A decision support approach

The international debt crisis of the last decade highlights the importance of country risk assessment for both academicians and practitioners. Country risk is generally defined as the probability that a country will fail to generate enough foreign exchange to service its foreign currency loans. Attempts to measure country creditworthiness have therefore centered on devising systems that give an early warning of potential debt servicing difficulties. Undoubtedly, commercial banks, which became major lenders on the intemational scene in the late 197Os, have devoted time and resources in developing such systems. However, surveys of country risk evaluation methods reveal that checklist systems are the most commonly used by bankers.1 Few banks rely upon statistical models, even though they have much data available to them through the Institute of International Finance (Washington, DC), for instance. One explanation for this lack of methodological sophistication is that reliable operational tools for the modelling of country risk assessment have not previously been available. This paper provides statistical procedures to help bankers assess their country risk evaluation model interactively and iteratively. The multicriterion decision support system, MINORA, based on the iterative use of an ordinal regression model and on a man-machine model, is used for this purpose. This decision support system rests on a trial and error learning process which is made operational through the UTA algorithm developed by Jacquet-Lag&e and Siskos [21]. The remainder of the paper is organized as follows. Section 2 presents the country risk evaluation criteria. The proposed method is developed in Section 3. Section 4 illustrates the use of this method for a hypothetical evaluator of country risk. Finally, in Section 5, we discuss directions for further research.

An integrated DSS for financing firms by an industrial development bank in Greece

Abstract This paper presents an integrated DSS for the analysis and financing of firms by an industrial development bank in Greece. Firstly, the system evaluates the financial performance of firms (financial ratios of profitability, managerial performance, solvency) during a 5-year period and allows inferences about their development tendencies. Furthermore, multivariate statistical techniques (discriminant analysis, principal components analysis) are available to aid in the identification of the most significant financial ratios and in the grouping of the firms in coherent categories. Finally, a multi-criteria method is used, which ranks the firms from the most dynamic to the bankrupt and in this way dynamic to the bank to select the less risky for financing. The capabilities of the system are illustrated with actual data provided by the bank.