Logistics customer service-revenue curve in period

The temporal differentiation of created revenue alterations enables a net present value consideration, as shown in Eq. (5-7). 

4 A numerical example from the consumer goods industry 

The introduced model was improved in a stepwise fashion by conducting exploratory in-depth interviews. Subsequently, the results of one numerical example that was created with an international producer of consumer goods are presented. The in-depth interviews were held in November 2008 in Basel (Switzerland). The two interviewees came from the company s sales and logistics department. In practice, the interviews consisted of three meetings. First, the concept of the fuzzy model was presented and a potential SCI was identified. Second, the adapted questionnaire was completed (cf. questionnaire in Appendix A). Third, the results were evaluated and suggestions for improvement of the model were collected. 

The company s logistics customer service is determined by the weighting of the performance of the four factors lead time, delivery reliability, quality and flexibility according to their importance. The fiizzy comparison matrix reflects the judgment of the interviewees. The values of a and p are both assumed to be 0.5. After applying Eq. (5-3) and (5-4) the frizzy comparison matrix can be transferred to the second part shown in Eq. (5-8) from which eigenvalues and one eigenvector are derived. 

The calculation of s is processed analogously to that of s. The relative importance of the four service factors stays the same it is only the factor performance that varies. Table 5-5 covers the corresponding data. In period t=l the industry logistics customer service has a level of 95.31%. 

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Figure 5-7 Logistics customer service-revenue curve in period t= ...

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