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.
PurposeThe purpose of this paper is to identify and evaluate the relationships among the flexibility enablers and to prepare a hierarchy of these enablers to know their influences over each other in global supply chain. The framework suggests that the priority of enablers in supply chain should be determined on the basis of their driving power and dependency.Design/methodology/approachVarious enablers used by researchers and practitioners for flexibility management of global supply chain have been identified. These enablers have been classified as strategic, operational and performance‐based enablers. Interpretive structural modeling (ISM) is used to establish mutual relationships among the flexibility enablers and to prepare a hierarchy‐based model.FindingsIt has been observed that some enablers having high‐driving power and low dependency are of strategic importance. These enablers require more attention while other enablers based on operations and performances are dependents of strategic enablers.Practical implicationsThe index of enablers based on driving power and dependency provide an insight for supply chain managers to make the entire supply chain highly flexible, that would help them to respond to global uncertainties.Originality/valuePresentation of enablers in the form of hierarchy using ISM and ranking them into various driving power and dependent categories is a good effort to make flexible global supply chain.
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