Derivation of the revenue generation through postponement

According to the proposed model, the industry logistics customer service would increase permanently until it reaches 100 percent, which is not possible. To counteract that development, a disconfirmation of expectations has to be integrated which leads to a relative decrease of iudustiy logistics customer service (Oliver, 1980 460f. 2009 107f.). 

Besides customer expectation, perception has to be considered (Gupta Zeithaml, 2006 720). Usually customers need time to notice improvements in a company s logistics customer service, which delays their reaction (Pisharodi Langley, 1990 28). Therefore, the complete revenue-enhancement potential is not fully realized immediately after the implementation of a supply chain. This effect may be integrated in terms of a weighting factor for the revenue increase over time. 

Besides logistics customer service, the market product, promotion and price were relevant Actors that affect customer satis iction and enhance revenues (Lambert Stock, 1993). These fectors were not adequately considered within the introduced model. An increase in logistics customer service level often goes along with an increase in price, which also effects customer satisfaction in an inverted way (Homburg et al., 2009 36). To get a comprehensive approach, the 

This chapter addresses the correlation between revenues and SCM. Thus, it goes further into the question of the extent to which SCIs contribute to a company s revenues. Quite contrary to costs and capital commitment, the impact of SCIs on revenues is of an indirect nature. In respect of SCM, the central value driver for revenues is logistics customer service, which provides a basis for customer satisfaction (e.g., Ballou, 2006 Ellram Liu, 2002 Pohlen Coleman, 2005). Customer satisfaction in turn affects the financial performance and the revenues of a company (e.g., Dresner Xu, 1995 Yeung, 2008). The known relations between logistics customer service and customer satisfaction, customer satisfaction and customer loyalty and customer loyalty and revenues is used to derive the nature of the correlation between logistics customer service and customer satisfaction. 

The introduced fuzzy model computes the required components for the definition of the logistics customer service-revenue curve. The calculation includes a standardization of logistics customer service, by operationalizing into the four service factors lead time, delivery quality, reliability and flexibility. This ensures comparability due to widely different comprehensions of logistics customer service widely in practice and theory. Furthermore, the model considers external factors ftom a company s perspective in terms of industry characteristics, as well as customer s and competitor s reactions to alterations in logistics customer service. 

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Figure 5-11 Derivation of the revenue generation through postponement in period P=1...

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