Decision Support Systems: Managerial Level
2.4.2 Decision Support Systems – on a Modelling Level Analytical Hierarchy Process (AHP)
Ho (2008) declared the AHP being one of the most used methodologies concerning Multi Criteria Decision Making (MCDM). According to Loken (2007), AHP and Multi-Attribute Utility Theory (MAUT) “[…] rest on different assumptions on value measurements and AHP is developed independently of other decision theories. […]The major characteristic of the AHP method is the use of pair-wise comparisons, which are used both to compare the alternatives with respect to the various criteria and to estimate criteria weights”. Saaty (1980) stated that the Analytical Hierarchy Process (AHP) methodology is almost as well known as MAUT, which will be discussed below. Effectively, in his work, Ho (2008) listed some authors, who concentrated on specific areas of, beyond others, logistics, environment, manufacturing or higher education. What is common to all those authors is that they used AHP combined with another methodology. As resource allocation plays an essential role in maintaining and improving performances, decision makers face multiple and often opposed objectives (Ho et al., 2006).
Three main processes are inherent in AHP, namely [1] the hierarchy construction, [2] the priority analysis, and [3] the consistency verification. Ho et al. (2006) structured the AHP process as shown on Figure 16 below. Saaty (2008) explained that the “Analytic Hierarchy Process (AHP) is a theory of measurement through pairwise comparisons and relies on the judgements of experts to derive priority scales. It is these scales that measure intangibles in relative terms”. In fact, each pairwise comparison requires answering on how much an attribute A is more important than an attribute B, relative to the overall objective (Kahraman, 2008). This pairwise comparison provides the advantage that AHP is easy to use as managers may weight coefficients and compare alternatives one by one and therefore relatively easily. In fact, its ability to structure complex, multi- attribute, multi-person, and multi-period problems hierarchically is one of the biggest advantages of AHP. In addition, it is simple to understand and therefore easy to use (Shahroodi, et al., 2012). The model’s structure “[…] facilitates communication of the problem and the recommended solutions”
(Shahroodi et al, 2012). Furthermore, as AHP is not as data intensive as other methodologies it is practicable in real life decision making problem analysis. Hilmola (2006) explained that AHP is not limited to tangible properties so that it can deal with qualitative and quantitative criteria. However, Hilmola (2006) pointed out that rank reversal exists in AHP as the order of superiority of the different alternatives a decision maker has, may change if a new one is added to the hierarchy. The fact of adding new alternatives may produce new information too, so that one may need to justify the order (Hilmola, 2006). Furthermore, because of that pairwise comparison, the model may become extremely large (Antil et al., 2013). The fact that AHP does not consider uncertainties or risks can be seen as further limitation of the AHP methodology (Yusuff & Poh, 2001).
Figure 26 – AHP Methodology by (Ho et al., 2006)
Fuzzy Logic (Fuzzy Set Theory)
Karwowski & Evans (1986) stated that a decision maker’s mental model of a problem he wants to solve is mostly imprecise and vague. This issue of vagueness and imprecision has been gathered in an extend range: the information provided by the production management environment may also be vague and therefore not measurable in a precise manner. The same problem can be verified as a result of experts’ subjective point of view considering the problem to be solved (Grabot & Caillaud, 1996). According to Karwowski and Evans (1986), the fuzzy logic can be applied to bypass the different modelling gaps occurring in production management’s decision models. The fuzzy logic has been used in many vague systems, which have no clear boundaries (Skubic, 1998). Chen, et al. (2006) have analysed supplier selection
feasible by using variables. In this same logic, Glushkovsky & Florescu (1996) described the use of fuzzy set theory in well-known quality tools, (such as Pareto Analysis, Cause-and-Effect diagrams, statistical control charts, etc.), when linguistic data is available. Contrary to the binary Boolean logic, the fuzzy logic, considers different ‘degrees of truth’, which allows a gradual membership to a given set. According to Borne et al. (1998) the first propositions going in the direction of non-crisp value analyses have appeared before the 1940’s. Natural language is hard to translate into absolute terms, as words like ‘almost’, ‘more or less’ etc. are in common use in everyday life. This kind of words cannot be translated into the absolute terms of a Boolean logic, thus, 0 or 1. Nevertheless, the fuzzy logic includes 0 and 1 as extreme values along with the values defining the various states of truth (Bouchon-Meunier, 2003). Serchuk (2005) explains that the fuzzy logic should be used in cases where a certain vagueness or uncertainty is given so that classical logic and probability theory are shown to be inappropriate for the required reasoning.
The Fuzzy Set Theory is closer to the way human brains are working as the Boolean logic, as
“one of the most important facets of human thinking is the ability to summarize information ‘into labels of fuzzy sets which bear an approximate relation to the primary data.’ Linguistic descriptions, which are usually summary descriptions of complex situations, are fuzzy in essence” (Dubois & Prade, 1980). One important disadvantage of the Fuzzy Set Theory is that the out coming results, which will always be a fuzzy set, may confuse laypersons. If a manager wants to get one single correct value, the fuzzy logic may not be used (Dubois and Prade, 1980; Bouchon-Meunier, 2003). Moreover, the methodology has severely been criticised by Elkan (1994) who stated that fuzzy logic had only been efficaciously used in control systems, but not in expert systems - an argument rejected by Serchuk (2005). Howbeit we agree on Elkan’s statement that experts are needed to define the meaning of a linguistic classification. In fact, the definition of what is to be seen as ‘good’, ‘medium’, or ‘poor’
needs to be done in a pertinent manner. Thus, when using linguistic assessments, a close collaboration between Fuzzy Set Users and experts is required.
Multi-Attribute Utility Theory (MAUT)
Many authors have suggested the Multi-Attribute Utility Theory (MAUT) to serve as decision support system (DSS) in real-world problems, since, in most cases, a decision maker needs to choose among several alternatives. If an alternative is considered being acceptable or not depends on how well it scores on each relevant characteristic, and the relative importance of these properties (Wallenius et al., 2008). The MAUT methodology involves in fact the comparison of different alternatives which have all own strengths and weaknesses (Gass & Fu, 2013). It is to be considered as a structured methodology used to handle the adjustments among multiple objectives, which can help decision makers to find the best solution for a given problem by determining a utility to every possible effect (Gass & Fu, 2013). The methodology is based on the theory of expected utility theory.
The latter states that “if an appropriate utility is assigned to each possible consequence and the expected utility of each alternative is calculated, then the best course of action is the alternative with the highest expected utility“ (Ananda & Herath, 2005). According to Von Winterfeldt & Fischer (1973), an alternative can be represented either as a vector of multi-attributed outcomes, or as a matrix.
The key benefit of this methodology is that it takes uncertainties into account which is not a common quality for most MCDM methodologies. The fact that it is a rather understandable and comprehensible model which enables to integrate the preferences of each alternative at every level of the calculation technique is equally important. A main drawback of MAUT is that it is rather complex and extremely data intensive due to the huge amount of data that may not be available for
obtainable, and if the considered problems analysed present significant uncertainties (Dyer, 2005; Wallenius et al., 2008).
Conclusion
Despite the fact that AHP might become extremely large due to the pairwise comparison, and despite the fact that risks and uncertainties are not considered by the AHP methodology, we consider this methodology being rewarding for our case study. Since we only have defined thirteen KPIs, the size of the model is supposed to remain manageable and easily understandable. In the same manner, the Fuzzy Logic is perceived as highly interesting because of its closeness to the human way of thinking. This is, above all, of great importance for the subjective and qualitative data albeit the latter will be converted into quantitative ones. We consider that some KPIs are highly subjective and need hence to be calculated in a manner enabling the use of the human way of thinking. In addition, as the system will not have clear boundaries this methodology may help to circumvent this issue. Nevertheless, the Fuzzy Logic will not be used as such. We will use the reasoning behind the methodology, i.e.
we will convert qualitative data into quantitative ones, and introduce fuzzy sets which will help to accurately survey the obtained results. Effectively, the Boolean logic adopting only the values 1 and 0, meaning “true” or “false” would not be helpful in this work. This point will be elucidated more in detail in section 2.5 . Even though, experts need to assess the linguistic classification, this is probably not as time consuming as the BSC methodology previously described.
Discussions with Kuehne + Nagel’s managers resulted in the agreements that the MAUT methodology is an interesting one, given the fact that this methodology has often been used to solve problems concerning the green concept, as explained before. On the other hand, it was also concluded that this method might be difficult to implement on real cases, as it requires strong assumptions at each step. The latter might falsify the real end results. In addition, as MAUT is considered being extremely data and hence time intensive, managers doubt its’ usability and realisation in daily business. A summary of the different methods’
advantages and disadvantages is given in Table 5.