Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.     

An empirical study of the effects of knowledge sharing and learning behaviors on firm performance

Knowledge sharing and learning behaviors in the workplace are believed to be very important to the success of firms. In this study, the relationships between knowledge sharing and learning behaviors, business process improvement, product and service offerings, and organizational performance are examined based on a sample of 134 firms engaged in manufacturing, and wholesale or retailing operations. Data analyses using the partial least squares statistical technique revealed that knowledge sharing and learning behaviors are positively associated with business process improvement, and product and service offerings. Business process improvement and product and service offerings are positively associated, and they in turn are positively related to organizational performance. The findings reinforce the importance of knowledge sharing and learning to companies. Executives should encourage knowledge management and organizational learning activities within their firms, and give proper considerations to the strategies and implementation of programs supporting these activities in order to enhance firm performance.     

Applying principles of mass customization to improve the empirical product development process

Applying methods of mass customization to the empirical process can improve product development process efficiency and reduce time and cost. Empirical methods are used to develop predictions of product behavior in conjunction with analytical methods or instead of analytical methods. These empirical methods represent a complete sub-product development process within the overall product development process. Application of process decomposition and planning used in mass customization can improve the efficiency, lower the time and cost of these empirical processes. This paper presents a method for applying principles of mass customization to the empirical sub-processes within a product development process. Two case studies are also presented to demonstrate the method.     

Applying fuzzy multiple attributes decision making for product configuration

Mass customization (MC) is an emergent concept in industry intended to provide customized products through flexible processes in high volumes and at reasonably low costs. The method of configuration is one of important ways to realize quickly product customization. But, in business, particularly through the Internet, a customer normally develops in his mind some sort of ambiguity, given the choice of similar alternative products. This paper proposes a new approach to product configuration by applying the theory of fuzzy multiple attribute decision making (FMADM), which focus on uncertain and fuzzy requirements the customer submits to the product supplier. The proposed method can be used either in the product data management system or e-commerce websites, with which it is easy for customers to get his preferred product according to the utility value with respect to all attributes. Finally, the digital camera is taken as an example to further verify the validity and the feasibility of the proposed method.     

一种针对商品数据记录的自动抽取方法

提出一种针对电子商务网站商品列表页数据记录的自动抽取方法。该方法根据商品记录的特点,通过商品记录中商品的文本、图片以及布局等节点类型信息计算节点对应的值,依据节点值的相似度对节点进行分组,再从不同分组中过滤出包含数据记录节点的集合,从而抽取整个页面的数据记录。实验结果证明该方法有效且抽取效率较高。     

Inventory management in a two-echelon closed-loop supply chain with correlated demands and returns

Reverse logistics or closed-loop supply chains where product returns are integrated with traditional forward supply chains have been one of the major topics of research since about the last one and a half decades. In this paper, we address the inventory management issue in closed-loop supply chains, and develop deterministic and stochastic models for a two-echelon system with correlated demands and returns under generalized cost structures. In particular, we address the following questions – Do closed-loop supply chains cost more than traditional forward supply chains? Does a higher rate of return always translate into lower demand variability and hence lower expected costs? What is the relationship between expected costs and correlations between demands and returns? Models developed and numerical examples shown in the paper reveal that although a higher rate of return and a higher correlation between demand and return reduce the variability of net demand, it may not necessarily lead to cost savings; rather the movement of costs will depend on the values of system parameters. We also quantify the cost savings in case the actual demand and return information is available at the time of decision-making. We conclude the paper by providing managerial implications and directions for future research.     

基于商空间的产品设计问题求解模型研究

针对产品设计约束网络模型的求解难问题,提出了基于商空间的产品设计问题求解模型。将产品设计问题的约束网络转换为拓扑空间,使用等价关系对约束满足问题中的变量进行划分,建立了基于商空间的产品设计问题分层求解模型。通过商映射实现不同层次之间的映射和回溯。这种采用分层次、逐步细化的问题求解方法,降低了问题求解的计算复杂性。     

多指标制品质量的正交实验方差分析及程序设计

本文采用MATLAB,对正交实验的多指标转化为单指标,以及单指标多因素交互作用的情况的方差分析进行了程序流程编制,并通过空调按键导座对程序进行实验验证,得到最优的参数组。     

基于Web评论的用户个人信息提取方法研究

企业收集和获取用户个人信息是其对用户行为进行分析以制定合理营销决策的前提。注意到当前,由于互联网的高度发展和普及,消费用户往往在Web上以评论文本的形式分享其消费习惯、消费偏好和消费体验,这些海量的评论文本中蕴含着极具价值的信息,为用户个人信息的收集提供良好的资源。针对传统企业收集用户个人信息的方法主要以人工为主导,自动化水平较低的问题,提出一种基于Web挖掘技术以网上评论文本为挖掘对象,对用户个人信息进行自动提取以自动分析用户行为的改进方法。企业可以通过此改进的用户个人信息提取方法对用户行为进行分析以自动获取消费用户对产品的反馈意见并制定有针对性的营销策略。