بکارگیری گسترش کارکرد کیفیت فازی برای اولویت بندی راه حل های مدیریت دانش برای یک بندر بین المللی در تایوان

The purpose of this paper is to apply a fuzzy quality function deployment (QFD) approach to prioritize knowledge management (KM) solutions for an international port in Taiwan. First, the paper examines house of quality (HOQ) matrices to facilitate handling of the ‘what’ (i.e., KM requirements) and ‘how’ (KM solutions) aspects of the QFD problem, and proposes procedures for the use of a fuzzy QFD method. A case study concerning port K in Taiwan is then used to demonstrate a systematic appraisal process for prioritizing KM solutions, and twenty attributes with sixteen feasible KM implementation solutions are measured employing an HOQ matrix. Finally, the top five feasible solutions for implementing KM at port K are identified. The empirical results show that ‘establishment of a data storage and data mining system’ in the technology dimension is the most urgent requirement for KM implementation at port K in Taiwan.

Traditionally, labor, land, capital, and an entrepreneurial spirit are seen by businesses and organizations as the most fundamental factors of production. In an age of exploding information, however, an enterprise needs to integrate its knowledge and transform it into useful resources that can help it maintain or create a competitive edge. In his Post-Capitalist Society, management guru Drucker [18] pointed out that in today’s society, the key factor of production providing real dominance is not capital, land, or labor, but rather knowledge, highlighting the reality that knowledge has become an important production element. Knowledge is an unlimited resource and asset [39] that can grow as it is used, and organizations can continue to innovate and progress through internal and external competition and friction. Both in the present and the future, knowledge will remain an important tool by which enterprises can boost their competitiveness. The key to the success of an enterprise lies in the continued application and accumulation of knowledge, and the steady growth of knowledge through continuous learning and innovation. Knowledge management (KM) [2] involves the conversion of data into useful information which can be applied as knowledge. It is a process in which data and information are processed and analyzed to become valuable knowledge, which can help businesses and organizations make profits, reduce costs, boost competitiveness, and generate both tangible and intangible assets [2], [21] and [48]. Zack [48] suggests that knowledge is a strategic resource that is difficult to copy or acquire, and the time needed to obtain knowledge cannot necessarily be shortened by increased investment. As a result, knowledge can offer both synergetic effects and increasing returns. Thus, the acquisition, integration, storage, and sharing of knowledge are the basis for an enterprise’s creation and maintenance of a competitive edge [27] and [31]. Nonaka and Takeuchi [33] and Polanyi [37] contend that tacit knowledge is a subjective concept that requires personal experience and can only be accumulated over time, and is also a form of knowledge that cannot be transferred through language and can only be shared via either interpersonal interactions or personal experience. Due to its higher production cost and lower chance of repeated use, tacit knowledge is normally applied by businesses to higher added value production activities. In contrast, since explicit knowledge can be transmitted to others via other media, it can be spread in a more effective and faster manner, leading people to believe that explicit knowledge is the most important factor of production in a knowledge-based economy. Because explicit knowledge has a wider range of application and can be repeatedly used, duplicated and learned, it can be passed on and spread through tabulation, generalization, classification and storage. The Organization for Economic Cooperation and Development (OECD) [36] argues that a knowledge-based economic framework will bring about changes in the pattern of global economic development. In a knowledge economy age, the creation of enterprise value hinges on technology, entrepreneurial spirit, and innovation. With the development and advance of various technologies, a new economic era has now arrived. To enhance competitiveness, improve efficiency, and lower operating costs, organizations should treat their knowledge as an asset and manage it accordingly, share and transfer correct know-how, and transform themselves into knowledge-oriented organizations. KM involves the use of a series of knowledge creation, acquisition, and application processes for the improvement of organizational performance. Hence, the success of an enterprise requires both innovation and application of knowledge to gain competitive advantages and promote KM [4]. A growing range of organizations are thus focusing attention on KM. In short, knowledge management treats knowledge as an asset and manages it in a systematic way to achieve the goal of enhancement of organizational performance and competitiveness. In recent years, a growing body of literature [2], [4], [8], [10], [11], [12], [20], [21], [26], [27], [28], [31], [40], [41], [45], [46] and [48] has addressed the subject of KM, which underscores the widespread attention this topic has received. However, a review of relevant literature suggests that the application of KM is still limited to manufacturing, high-tech industry, knowledge-intensive industry, service industries having frequent interactions with customers, and some government agencies. Despite the fact that there have been studies on the application of KM to the maritime transport industry [47], discussion of its application to port management remains scanty. The integration of port service functions and their connection with the country’s industrial development not only result in beneficial economic clustering effects but also boost the market effectiveness of the production sector. Moreover, the development of global trade has further changed the management of traditional maritime transport and led to critical changes in the international maritime transport service market system. Haynes et al. [24] suggested that factors connected with the success of a fourth-generation port operation [44] include technology, human resources, resource integration strategies, and application of knowledge. With the development of KM and the knowledge-based economy, these competitive port knowledge service capabilities have expanded to play a functional role, which further demonstrates the important role played by KM in modern seaport operations. Ports are a type of service industry [3], but there are discrepancies in perceived importance and satisfaction when KM is implemented by port operators. In order to enhance KM implementation performance, successful, concrete, feasibility solutions must be proposed in order to resolve the issue of these discrepancies. The quality function deployment (QFD) method offers just such a solution. There are situations in which information is incomplete or imprecise, or when views subjective or endowed with linguistic characteristics [50], creating a ‘fuzzy’ [49] decision-making environment. The fuzziness-based quality function deployment (fuzzy QFD) approach can be used to evaluate the relationships between the requirements and solutions of implementing KM for port operators. By using the fuzzy QFD approach, KM implementation requirements can be transformed into technical requirements able to improve KM performance. In summary, the purpose of this paper is to apply the fuzzy QFD method to prioritize technical KM solutions for an international port in Taiwan. The first section provides background information. The following section presents the research methodology, while the third section describes the fuzzy QFD procedures. The fourth section consists of an empirical case study, and the final section offers our conclusions.

This paper uses a fuzzy QFD approach to prioritize KM solutions for an international port in Taiwan. To facilitate resolution of the main issue, the basic concepts of the QFD model are presented first, and the HOQ method is then proposed as an insightful procedure for solving the ‘what’ and ‘how’ questions in the QFD model. However, there are situations in which information is incomplete or imprecise, or when views are subjective or endowed with linguistic characteristics, creating a ‘fuzzy’ decision-making environment. A fuzzy QFD approach was therefore used to resolve the relationship between customer requirements (i.e., ‘what’ KM requirements) and technical specifications (i.e., ‘how’ the KM solutions have to be designed). Systematic procedures for using the fuzzy QFD method were then proposed in Section 3.2. Empirical analysis was conducted, using port K as a case study, to demonstrate a systematic appraisal process for prioritizing KM solutions. Two questionnaires administered in different stages were designed to help illustrate the operational processes of the proposed appraisal model. In the first questionnaire, we used 20 KM requirement implementation attributes to evaluate the discrepancies between levels of perceived importance and satisfaction. A total of 74 valid questionnaires were collected from one hundred respondents. We next constructed a matrix table to evaluate the fuzzy relationship degree in the second questionnaire. Five valid stage-two questionnaires were collected and used to calculate the fuzzy relationship matrix. The results of the empirical study were as follows: 1. The top five attributes for implementing KM requirements are respectively ‘reduction of port costs and expenses (C7)’, ‘transforming the port’s non-knowledge-oriented organization into a knowledge-oriented organization and increasing knowledge sharing within the organization (C19)’, ‘implementation of a substitute system to enable any replacement employee to swiftly take over a new job in the event of a job transfer (C8)’, ‘enhancement of knowledge sharing between different departments (C3)’, and ‘improvement of organizational and individual knowledge learning ability (C17)’. 2. The top five feasible solutions for implementation of KM at port K are respectively ‘establishment of a data storage and data mining system (A1)’, ‘establishment of a decision-making support system (A3)’, ‘construction of an information and communication infrastructure (A2)’, ‘establishment of a database for document management (A5)’, and ‘use of groupware and other software (A4)’. 3. Thus, the technical requirements of KM implementation at port K should begin with technology, followed by personnel, and then workflow. Although KM implementation can significantly benefit port operations, its benefits cannot be realized without comprehensive planning. Therefore, in the process of KM implementation, such aspects as the establishment of systems and the promotion of learning and a sharing culture need to be taken into consideration in the construction of technological infrastructure. Furthermore, this paper can be used as a reference by port K in its implementation of KM, as it can help port personnel understand the purpose and importance of KM. While this paper adopts a case study approach to discussing port KM, follow-up researchers may apply other methods to investigate patterns and advantages/disadvantages of port KM implementation.