solving process.
Step 4.1 Describe the specified problem with all the customer’s needs and expected requirements. Collect information on existing situations in the environment of the service operation, strip away the side issues and preconceptions, and analyze to identify the scope of the existing problem or the core requirements from the feedback of customers. Conducting a survey of a focus group is one of the more commonly used tools to accurately gather situation information. Another way to easily collect existing information from the possible problem is to consult the operators of the targeted service operation.
Step 4.2 Define an Ideality or Ideal Final Result (IFR) to achieve, with regard to the specified problem. First, break the problem down into its most elementary components and conceptualize the basic constituents of the specified problem. This involves expressing the components in their most fundamental state. Then identify the IFR as the ideal situation to achieve without using extra resources when the contradictions within the problem are resolved. There are seven questions which are very helpful in properly defining the IFR in this step. These are the following: what is the final goal of the system, what is the ideal final result, what will prevent us from achieving the ideal final result, why will it prevent us from achieving the ideal final result, how do we vanish those hindrances, what kinds of resources could be used to construct the ideal situation, and is there anyone who has been able to resolve the same problems.
Step 4.3 With the items of determinants developed from stage 2, we apply the relationship matrix of Fuzzy QFD to indicate the critical determinants relevant to the customer’s requirements specified in step 4.1, and the computational procedures for the fuzzy numbers in the relationship matrix are shown in the following steps:
Step 4.3.1 Identification of linguistic terms: In order to identify the correlative relationships between the customer requirements and the service quality determinants of
the sector, we describe the importance of the relationship through linguistic terms with five distinct levels, which are EI (extremely important), VI (very important), I (important), LI (a little important), and NI (not important).
Step 4.3.2 Fuzzification of input data: The triangular fuzzy number which is easier to interpret is used in this study and all membership functions for the linguistic input data are standardized in the interval [0,1]. The figure of the triangular fuzzy numbers is shown in Figure 3.2, and the membership functions are shown in Figure 3.3.
Figure 3.2 The Figure of the Triangular Fuzzy Numbers in the Interval [0,1]
x LI
NI I VI EI
0.0 0.25 0.5 0.75 1.00
1.00 Membership Function
Figure 3.3 The Membership Functions of the Triangular Fuzzy Number
Step 4.3.3 Applying fuzzy arithmetic: The fuzzy arithmetic is applied to the calculation of the priorities of relevant service quality determinants, and the addition and multiplication of fuzzy numbers will be performed for the calculation. Suppose Sitj = (qitj,oitj,pitj) is the triangular fuzzy number of the jth team member assessing the correlative importance between the tth customer requirement and the ith category of service quality determinants. Then Sit is defined as the average fuzzy number of the ith
µEI(x) = 4x – 3, 0.75< x <1
1, x=1
0, others
µVI(x) =
4x –2, 0.5< x < 0.75
1, x=0.75
–4x +4, 0.75< x < 1
0, others
µI(x) =
4x – 1, 0.25< x < 0.5
1, x= 0.5
–4x +3, 0.5 < x < 0.75
0, others
µLI(x) =
4x, 0< x < 0.25 1, x= 0.25 –4x +2, 0.25< x <0.5
0, others
µNI(x) = 1, x= 0
–4x +1, 0< x < 0.25
0, others
category of the service quality determinant for the tth customer requirement, where n is
Suppose there is no weighting difference considered among the determinants of service quality, and the integrated fuzzy number of each service quality determinant for k team members (Qi,Oi,Pi) can be calculated by the following equations:
∑
=Step 4.3.4 Defuzzification of output data: It is suggested that the output results be presented in crisp data as they are easier to interpret, and the defuzzification method used in Chen’s research (1996) is applied in the current study. Let X denote the defuzzified value of the integrated fuzzy number for each service quality determinant (Qi,Oi,Pi), and then the defuzzified values can be calculated with the following equation:
4
i i i
i O O P
X =Q + + + (3.8)
Step 4.3.5 Rank the defuzzified values of service quality determinants: According to the crisp data calculated from the step 4.3.4, the prioritized importance of each relevant determinant can be sequentially ranked.
Step 4.4 From the most important determinants selected from the rankings, we discuss to identify all the conflicting determinants which will enhance and prevent the ideal solution to be acquired.
Step 4.5 Detect the relative TRIZ engineering parameters which get worse and need to be improved from the parameter corresponding table which was developed in stage 3 based on the improving and worsening determinants which were identified from step 4.4.
Step 4.6 According to the TRIZ contradiction matrix, the denoted numbers of the 40 TRIZ inventive principles can be gathered from the intersection of the improving and worsening TRIZ parameters.
Step 4.7 When we indicate the 40 TRIZ inventive principles based on the content of the specified problem, we suggest that the appropriate reexplanations and examples of the 40 TRIZ inventive principles developed in distinct areas be examined and benchmarked.
For instance, when the specified problem is relating to the service sector, the studies of Mann and Domb (1999), Rea (2001), Retseptor (2003), Zhang et al. (2003), and Retseptor (2005) are relevant to service quality in the non-technical field.
Step 4.8 Following the indicated principles and suggested ways, all possible solutions may be generated through various discussing meetings.
Step 4.9 Examine to obtain the feasible solutions with concerned criteria such as cost,