The main purpose of the study is to compare the differences of marketing strategies of the two-dimension classification, which classified by degree of customization and benefit duration period. And to explore the influences of the strategic selection of the services industries by different organization¡¦s features and different strategies maker¡¦s characters. The research takes eight industries as population to investigate their marketing strategies. After collected, the data is processed with frequency analysis, factor analysis, discriminant analysis, one-way ANOVA, and two-way ANOVA. The key findings are: 1. The result indicates that two dimension classification method is more efficiently to reflect the differences of the marketing strategic between different taxonomies of service. 2. Service of ¡§high degree of customization and long benefit duration period¡¨ focuses on the marketing strategies such as ¡§public relationship strategy,¡¨ ¡§strategy of emphasizing the importance of employees,¡¨ and ¡§building the intangible quality image strategy, The strategies such as ¡§low price strategy,¡¨ ¡§advertisement strategy,¡¨ and ¡§non-personal promotion,¡¨ which only can bringing short-term effect, are hardly used. 3. Service of ¡§low degree of customization and short benefit duration period¡¨ is just the opposite, emphasizing the strategies which can bring the instant effect. 4. Service of ¡§high degree of customization and short benefit duration period¡¨ prefers the use of ¡§strategy under smoothing the unbalance of supply and demand.¡¨

Mass customization (MC) was developed to capitalize on the combined benefits of economies of scale and economies of scope. Balancing the tradeoffs involved in an MC system warrants the determination of the degree or the extent of customization. Most of the literature views the degree of customization as how early or how far the customer is integrated in the production cycle, which is defined as the order decoupling point. In this study we are addressing the degree of customization from a product structural perspective. There are two objectives in this research. The first is to develop a unit of measurement for the degree of customization of a product in an MC system. The second is to construct an optimization model to determine the level of customization that would best satisfy the organizational goals. The term "Magnitude of Customization" (MOC) has been introduced as a measuring unit for the degree of customization on a customization scale (CS). The MOC is based on the number of module options or the extent of customizable features per component in a product. To satisfy the second objective, an analytical model based on preemptive goal programming was developed. The model optimizes the solution as to how far an organization should customize a product to best satisfy its strategic goals. The model considers goals such as increasing the market share, and attaining a higher level of customer satisfaction, while keeping the risk or budget below a certain amount. A step-by-step algorithm is developed for the model application. A case study of an aluminum windows and doors company is used to verify and validate the model. A double panel sliding window is selected as the subject of our study. Information related to company goals and objectives vis-a-vis customization is gathered, through interviews and questionnaires, from the upper management including Operations, Marketing, and Finance Departments. The Window design and technical information are collected from the Manufacturing Department. The model and its solution provided specific recommendations on what to customize and to what degree to best satisfy primary strategic goals for the organization. Results from the model application shows that the company is able to meet the five goals that they had identified with two goals having a deviation of 4.7% and 6.6% from the targets. To achieve the stated goals, the model recommends an overall degree of customization of approximately 32.23% and delineates that to the component and feature levels. For validation, the model results are compared to the actual status of the company and the manufacturer's recommendation without prior information about the model outcome. The average difference, for attaining the same goals, is found to be 6.05%, at a standard deviation of 6.02% and variance of 36.29%, which is considered adequately close. The proposed model presents a framework that combines various research efforts into a flexible but encompassing method that can provide decision-makers with essential production planning guidelines in an MC setup. Finally, suggestions are provided as to how the model can be expanded and refined to include goal formulations that accommodate potential MC systems and technology advances. To the best of our knowledge, this research is a pioneer in quantifying customization in an MC environment and relating it to the organizational goals through modeling and optimization.