芯片固件开发工程数字IC设计工程.doc

Mining customer knowledge for tourism new product development and customer relationship managementOriginal Research ArticleExpert Systems with ApplicationsIn recent years tourism has become one of the fastest growing sectors of the world economy and is widely recognized for its contribution to regional and national economic development. Tourism product design and development have become important activities in many areas/countries as a growing source of foreign and domestic earnings. On the other hand, customer relationship management is a competitive strategy that businesses need in order to stay focused on the needs of their customers and to integrate a customer-oriented approach throughout the organization. Thus, this paper uses the Apriori algorithm as a methodology for association rules and clustering analysis for data mining, which is implemented for mining customer knowledge from the case firm, Phoenix Tours International, in Taiwan. Knowledge extraction from data mining results is illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to the case firm for new product development and customer relationship management.Article Outline1. Introduction2. The case firm the Phoenix Tours International 2.1. Background of the case firm2.2. The new product development procedure of the case firm3. Methodology 3.1. Research framework3.2. Questionnaire design and data collection3.3. Relational database design3.4. Association rule Apriori algorithm3.5. Clustering analysis4. Research results 4.1. New product development 4.1.1. Travel area inbound travel (pattern A) 4.1.1.1. Inbound travel association analysis4.1.1.2. Inbound travel cluster analysis4.1.2. Travel area outbound travel Asia (pattern B) 4.1.2.1. Outbound travel association analysis4.1.2.2. Outbound travel cluster analysis Asia area4.2. Customer relationship management 4.2.1. Travel service 4.2.1.1. Travel service association analysis (pattern C)4.2.1.2. Travel service cluster analysis4.2.2. Direct marketing 4.2.2.1. Travel web site usage association analysis (pattern D)4.2.2.2. Direct marketing cluster analysis5. Discussion 5.1. In the regard of current market strategy5.2. In the regard of future market strategy5.3. In the regard of customer value and satisfaction5.4. In the regard of new business model6. ConclusionAcknowledgementsReferencesCustomer satisfaction driven quality improvement target planning for product development in automotive industryOriginal Research ArticleInternational Journal of Production EconomicsCustomer satisfaction targets for vehicle attributes are set at the corporate level with limited consideration of the engineering feasibility and interactions between different product features. This paper presents a comprehensive framework for target planning for customer satisfaction driven quality improvement efforts in the product development process. The proposed framework facilitates a link between corporate decision making and engineering decision making by integrating best practices and structuring technical activities. Potential vehicle attributes are classified and prioritized for further improvement using Kano model and quality function deployment. Customer satisfaction targets are established based on rigorous business analysis and trade-off studies. These targets are converted into objective engineering metrics using regression models. Transfer function equations are developed to provide a link between higher-level product characteristics and lower-level design variables. The mathematical models are formulated as optimization problems to cascade down top-level targets to lower-level elements within given constraints. A case example is presented to demonstrate the proposed methodology.Article Outline1. Introduction2. Target planning process3. Methodology 3.1. Identify and prioritize improvement opportunities 3.1.1. Customer requirements3.1.2. Corporate and regulatory requirements3.1.3. Classification of vehicle attributes3.1.4. Prioritization of improvement opportunities3.2. Set attribute-level CS targets3.3. Establish attribute-level objective metric (measurable) targets3.4. Target cascading process 3.4.1. Identify critical characteristics3.4.2. Develop transfer function model3.4.3. Target cascading 3.4.3.1. Mathematical model3.4.3.2. Vehicle-level target cascading3.4.3.3. System-level target cascading3.4.3.4. Sub-system-level target cascading3.5. Component-level design optimization4. Example 4.1. Vehicle-level target cascading model4.2. System-level target cascading model4.3. Sub-system-level target cascading model5. ConclusionAcknowledgementsReferencesManaging the trade-off between relationships and value networks. Towards a value-based approach of customer relationship management in business-to-business marketsOriginal Research ArticleIndustrial Marketing ManagementThe management of buyerseller relationships was an early antecedent to the development of customer relationship management (CRM) concepts. Currently, CRM concepts are being challenged by the rise of value networks. Value networks can and, often, do interfere with customer relationships and thereby call for a broader range of concepts to analyze and understand relationship management and the influence of value networks on relationships. This introductory article describes the nature of the problem between relationships and value networks, reviews the current state of research, and describes the contributions of the articles presented in this special issue on CRM in business-to-business markets.Article Outline1. Value networksA challenge for business-to-business relationships2. Value creation through cooperationThe evolution of cooperative buyerseller relationships in the realm of business-to-business markets3. Directions for relationship concepts 3.1. Value networksThe challenges for relationship management3.2. Towards a common understanding of relationship concepts3.3. Relationship-focused strategies in a network context3.4. Managing the customer interaction in a multiple channel network3.5. Knowledge management for network positioning4. The road aheadAcknowledgementsReferencesVitaeA new mixed integer linear programming model for product development using quality function deploymentOriginal Research ArticleComputers & Industrial EngineeringQuality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the values of DRs are often discrete instead of continuous. To the best of our knowledge, there is no mixed integer linear programming (MILP) model in which the discrete DRs values are considered. Therefore, in this paper, a new QFD optimization approach combining MILP model and Kano model is suggested to acquire the optimized solution from a limited number of alternative DRs, the values of which can be discrete. The proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete. Additionally, the problem of lack of solutions in integer and linear programming in the QFD optimization is overcome. Finally, the model is illustrated through an example.Article Outline1. Introduction2. Literature review 2.1. Optimization methods in QFD literature2.2. Kano model in QFD literature3. A new approach to QFD optimization 3.1. Kano model3.2. Proposed MILP model4. Illustration 4.1. Constructing the HOQ4.2. Optimizing the development problem5. Conclusion and discussionAppendix AReferencesVirtual product experience and customer participationA chance for customer-centred, really new productsOriginal Research ArticleTechnovationThis paper demonstrates how customers can be virtually integrated into a companys innovation process. New interaction tools allow companies to gain valuable input from customers via the Internet. First, we explain why too closely listening to customers may turn out to be problematic for the development of real new products. The KANO model shows that it is difficult for customers to express their latent needs as well as those which are taken for granted. New virtual interaction tools and virtual product experiences help to overcome these problems and enable customers to transfer their explicit and implicit knowledge to innovation teams. How to apply virtual interaction tools and how to virtually integrate customers into the innovation process in practice is illustrated in detail in the AUDI case study. Our case study findings show that virtual customer integration provides valuable input for new product development. This paper introduces virtual customer integration as a new means of coming up with customer-centred, really new products.Article Outline1. Introduction2. Customers problems to articulate their needs3. The virtual product experience4. Concept of virtual customer integration5. Virtual customer integration at AUDI6. Management considerations of virtual customer integration7. DiscussionReferencesVitae9,641 articles found for: pub-date 2003 and tak(WLAN product promotion) or (WLAN Customer Support) or (Specifications discussions) or (cooperative development) or (customer problems) or solutions) and (Network IC) or Development or (Driven Development Project) or (Windows driver development and maintenance) or (WLAN chip verification) Customer interactivity and new product performance: Moderating effects of product newness and product embeddednessOriginal Research ArticleIndustrial Marketing ManagementUsing AHP and TOPSIS approaches in customer-driven product design processOriginal Research ArticleComputers in IndustryNetwork-on-Chip design and synthesis outlookOriginal Research ArticleIntegration, the VLSI JournalIdentifying issues in customer relationship management at Merck-MedcoOriginal Research ArticleDecision Support SystemsA methodology to generate a belief rule base for customer perception risk analysis in new product developmentOriginal Research ArticleExpert Systems with ApplicationsResearch highlights A novel method to generate belief rule base for risk analysis in new product development is developed. A new way to quantify the influence of antecedent attributes on the consequence is proposed. Biases and inconsistencies can be reduced by the method during the belief rule base generation process. A case regarding customer perception risk analysis is then studied using the method proposed in the paper.知名上市集成电路设计公司-，苏州工业园区长期策略合作的集成电路设计公司，延续半导体以人为本的经营理念，在中国设立集成电路设计公司，以开创中国半导体产业蓬勃发展为愿景，跃升世界第一流集成电路设计公司为永续经营目标。微电子成立于2001年12月，位处苏州工业园区，总投资额美金1750万元，以集成电路设计为主要业务，主要产品为芯片。本着以人为本，企业与员工共同成长的信念，目前集成电路研发人员已占公司总人数的80%以上，同时，每位研发人员在进公司后，公司随即对其进行为期3个月至半年的集成电路设计教育训练，而后再以onjob training方式，让同仁透过学习获得成长，由此显见公司对于产品研发方面的重视程度。未来，在中国半导体产业蓬勃发展的基础上，微电子将致力于半导体集成电路的研发、芯片的销售与集成电路设计人才的培育，以期在全球半导体产业上占有一席之地。Influences of customer preference development on the effectiveness of recommendation strategiesOriginal Research ArticleElectronic Commerce Research and ApplicationsMost previous studies on recommendation agents have been restricted to the problems of uncovering customer preferences during the process of understanding customers. However, studies on consumer psychology have indicated that customer preferences are often unstable and developed over time. Therefore, we assert that it is necessary to observe the degree to which customer preferences are developed since effectiveness of recommendations is affected by customers preference development. This study presents a scheme to identify the status of customers preference development and analyzes the influences of customer preference development on the effectiveness of various recommendation strategies.Article Outline1. Introduction2. Theoretical background of customer preference development 2.1. Two perspectives of customer preference2.2. Customer preference development 2.2.1. Dimensions of customer preference development2.2.2. Customer segmentation by preference development3. Research model and hypotheses 3.1. Research model: recommendation strategies and customer preference development3.2. Hypotheses4. Experimental design 4.1. Overview4.2. Step 1: data collection and participants invitation 4.2.1. Movie dataset4.2.2. Participants4.3. Step 2: preference assessments4.4. Step 3: measurement of preference development 4.4.1. Stability4.4.2. Self-insight4.5. Step 4: customer segmentation by preference development4.6. Step 5: recommendation by various strategies and evaluation 4.6.1. Details of recommendation strategies 4.6.1.1. Recommendation by average opinion4.6.1.2. Recommendation by expert opinion4.6.1.3. Recommendation by collaborative filtering4.6.1.4. Recommendation by content-based filtering4.6.2. Evaluation metrics for recommendation performance5. Results6. Discussion 6.1. Theoretical discussion6.2. Practical implications7. ConclusionAcknowledgementsAppendix A. Detailed explanation for recommendation by collaborative filteringAppendix B. Detailed explanation for recommendation by content-based filteringReferencesPractices and functions of customer reference marketing Leveraging customer references as marketing assetsOriginal Research ArticleIndustrial Marketing ManagementEnabling through life product-instance management: Solutions and challengesOriginal Research ArticleJournal of Network and Computer ApplicationsOptimizing product assortment under customer-driven demand substitutionOriginal Research ArticleEuropean Journal of Operational Research工作项目： 1，从事芯片的系统设计开发和验证； 2，负责芯片固件程序的编写； 3，负责解决芯片开发和验证过程遇到的问题； 4，帮助解决客户的需求和遇到的问题； 工作经验： 1，有USB/PCIE，记忆卡开发经验优先； 2，熟悉芯片开发流程和芯片设计开发原理； 3，熟练掌握C语言编程； 4，了解数字电路和模拟电路原理； 其他要求： 1，很好的工作有主动性； 2，很好的工作合作性； 3，很快的学习能力和解决问题的能力；A process-oriented multi-agent system development approach to support the cooperation-activities of concurrent new product developmentOriginal Research ArticleComputers & Industrial EngineeringEnabling content-based publish/subscribe services in cooperative P2P networksOriginal Research ArticleComputer NetworksDeveloping integrated solutions: The importance of relationships within the networkOriginal Research ArticleIndustrial Marketing ManagementFirms that have traditionally focused on selling products, spare parts and services face difficulties with increasing competition and declining margins. They are therefore turning to new strategies where products and services are integrated into so-called integrated solutions. Research on the challenges this presents is sparse, but there is evidence that internal factors as well as external relationships play an important role. In this paper we investigate the relationships within the business network in order to uncover some of the complex issues related to integrated solutions, including how and to what extent these relationships facilitate or impede the development of integrated solutions. Two case studies of one more and one less successful initiative within the same firm are used to illustrate challenges and possible success factors for the development of integrated solutions in the capital goods industry. The paper identifies the following six factors as important when developing integrated solutions: the strength of the relationships between the different actors involved, the firms position in the network, the firms network horizon, the solutions impact on existing internal activities, the solutions impact on customers core processes, and external determinants. It shows that inter- and intra-firm relationships can both enable and obstruct the development of integrated solutions. For the firms involved in the development of integrated solutions, it becomes crucial to manage this duality.Article Outline1. Introduction2. Integrated solutions 2.1. Integrating product and service perspectives2.2. Increasing customer focus2.3. Organizing to deliver integrated solutions2.4. Importance of relationships within the business network3. Developing integrated solutions: a case study of the Dairy and Sludge projects 3.1. Methodology3.2. Optimizing the sludge treatment process the Sludge project3.3. Controlling the cream concentration the Dairy project4. Critical factors in the case study related to relationships within the network 4.1. Critical factors in the Sludge project4.2. Critical factors in the Dairy project5. Important factors and managerial implications for developing integrated solutions6. The strength of relationships and the solutions impact on internal activities 6.1. The firms position in the network and its network horizon6.2. External determinants and the solutions impact on the end customers core processes7. Conclusions and suggestions for further researchAppendix A. AppendixReferencesVitaeWLAN产品推广WLAN客户支援规格讨论，合作开发，客户问题,解决网络IC开发驱动开发工程Windows驱动开发及维护，WLAN芯片验证WLAN product promotion WLAN Customer Support Specifications discussions, cooperative development, customer problems, solutions Network IC Development Driven Development Project Windows driver development and maintenance, W