PurposeThe purpose of this paper is to describe the construction of a key model for knowledge management (KM) systems using AHP‐QFD for the semiconductor industry in Taiwan.Design/methodology/approachThe performance evaluation matrix was modified to set up a standard performance matrix for system introduction. The importance weights of models related to KM via the analytic hierarchy process (AHP) and after consulting experts' opinions. The method of quality function deployment (QFD) was integrated for the system models of a KM system and correlation weights of key objectives to be improved.FindingsSeven key objectives need to be improved. Correlations between the key objectives to be improved and the KM system models are located via QFD for eight critically important models to be improved.Research limitations/implicationsIn this study, the questionnaires were e‐mailed to respondents sampled from the list of the Taiwan Semiconductor Industry Association (TSIA).Practical implicationsActual cases are investigated and a KM system prototype is established in this research to provide reference for the semiconductor industry when introducing a KM system.Originality/valueCompanies can evaluate the performance of system introduction rapidly and regulate their investments in resources efficiently using the measurement, analysis and improvement methods provided here so that the performance of introducing the KM system will be increased effectively at the lowest cost.
According to the survey conducted by AIAG in 1999, significant effects of QS-9000 certification included average cost savings of over 6%, a 38% improvement in defect rate and a 23% increase in market share on OEM. However, few manufacturers in semiconductor related industries in Taiwan have been QS-9000 certified to date. The major reason is there is not a complete and efficient set of implementation procedures for introducing QS-9000 and the certifying process. Consequently, methods of performance evaluation based on multiple goals and principles must be applied to assess the performance of the process of introducing QS-9000 to the Taiwanese semiconductor industry. First, the performance index and the performance matrix presented by Hung, Huang, and Chen are applied to assess the certifying importance and ease of implementation of each directive item (goal). Next, related correlation and weighted values among directives (objectives to be improved) of low importance and high easiness, and also of high importance and low easiness, are established using fuzzy measures. Finally, the directives to be improved (goals to be improved) are transformed to an overall performance value during the implementation process (strategy) by fuzzy integrals to define the critical implementation process (strategy) when QS-9000 is introduced to the semiconductor industry, and also to increase the timeliness of system introduction and certification. Management is expected to find the objectives and strategies to be improved related to the introduction of the QS-9000 system, and to certify this process through the complete assessment model presented here. Subsequently, the performance of introducing QS-9000 and certification may be increased after taking into consideration cost and time factors.
“Multi-Intelligence Aptitude” includes linguistic, logical mathematics, space extensity, limbs mobility, musical sensitivity, public relation, introspection and nature observation. The 8 intelligence are complementary to each other, the strong intelligence could awake the weak intelligence and the relation between multi-intelligence aptitude and personal interest and working capability are so close that they could be used as an important reference index for selecting and culturing human resource. Based on the above viewpoint we will use the multi-intelligence aptitude to design the questionnaire and analyze the data by the 6-sigma methodology to develop a new talent selection model. Firstly we distributed the questionnaire to the qualified engineers and analyzed the interaction between the static and dynamic characteristics; and then survey the job applicant with the same questionnaire and draw out the “Radar chart” and “Performance Control chart” of qualified engineer and job applicant, respectively. Then after calculation we have the “similarity” and “differentiation” data of their multi-intelligence aptitude; and furthermore to find the job applicant that has higher similarity of multi-intelligence aptitude with the qualified engineer and have more relation of the intelligence needed for the job. The analysis model proposed in this article in addition to be used for selecting job applicant, it could provide to student as an important aptitude reference index of aptitude in job seeking.
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