Ahmad Morshedi, Navid Nezafati, Sajjad Shokouhyar, Mehrab Tanhaeean
{"title":"提出一种考虑供应链知识管理挑战的混合模糊模型:案例研究","authors":"Ahmad Morshedi, Navid Nezafati, Sajjad Shokouhyar, Mehrab Tanhaeean","doi":"10.1504/ijkms.2023.132046","DOIUrl":null,"url":null,"abstract":"The steel industry is considered the second most vital industry in Iran after oil and petrochemical industry. Correspondingly, KM implementation is encountered with challenges and obstacles leading to cost enhancement and resources annihilation. This study aims at reducing the uncertainty made by expert judgments in the procedure of KM implementation using linguistic degrees. In this paper, a fuzzy theory is used to decrease the linguistic inaccuracy and the vagueness of human judgment. Firstly, challenges of KM were identified in the literature. Then, challenges corresponding to steel industry SC were finalised, short-listed and merged by expert judgment through Delphi fuzzy method. Also, new solutions were proposed by experts to cope with KM challenges. Afterwards, the fuzzy analytical hierarchy process (FAHP) is exploited to rank and weight determination of challenges and solutions. Using the fuzzy inference system (FIS) for improving KM implementation and managing the challenges is the next stage.","PeriodicalId":39285,"journal":{"name":"International Journal of Knowledge Management Studies","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Proposing a hybrid fuzzy model to consider knowledge management challenges in supply chain: case study\",\"authors\":\"Ahmad Morshedi, Navid Nezafati, Sajjad Shokouhyar, Mehrab Tanhaeean\",\"doi\":\"10.1504/ijkms.2023.132046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The steel industry is considered the second most vital industry in Iran after oil and petrochemical industry. Correspondingly, KM implementation is encountered with challenges and obstacles leading to cost enhancement and resources annihilation. This study aims at reducing the uncertainty made by expert judgments in the procedure of KM implementation using linguistic degrees. In this paper, a fuzzy theory is used to decrease the linguistic inaccuracy and the vagueness of human judgment. Firstly, challenges of KM were identified in the literature. Then, challenges corresponding to steel industry SC were finalised, short-listed and merged by expert judgment through Delphi fuzzy method. Also, new solutions were proposed by experts to cope with KM challenges. Afterwards, the fuzzy analytical hierarchy process (FAHP) is exploited to rank and weight determination of challenges and solutions. Using the fuzzy inference system (FIS) for improving KM implementation and managing the challenges is the next stage.\",\"PeriodicalId\":39285,\"journal\":{\"name\":\"International Journal of Knowledge Management Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Knowledge Management Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijkms.2023.132046\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Knowledge Management Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijkms.2023.132046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
Proposing a hybrid fuzzy model to consider knowledge management challenges in supply chain: case study
The steel industry is considered the second most vital industry in Iran after oil and petrochemical industry. Correspondingly, KM implementation is encountered with challenges and obstacles leading to cost enhancement and resources annihilation. This study aims at reducing the uncertainty made by expert judgments in the procedure of KM implementation using linguistic degrees. In this paper, a fuzzy theory is used to decrease the linguistic inaccuracy and the vagueness of human judgment. Firstly, challenges of KM were identified in the literature. Then, challenges corresponding to steel industry SC were finalised, short-listed and merged by expert judgment through Delphi fuzzy method. Also, new solutions were proposed by experts to cope with KM challenges. Afterwards, the fuzzy analytical hierarchy process (FAHP) is exploited to rank and weight determination of challenges and solutions. Using the fuzzy inference system (FIS) for improving KM implementation and managing the challenges is the next stage.
期刊介绍:
“Knowledge as a key resource will contribute to improved organisational performance if it is properly leveraged and harnessed." IJKMS is a refereed and authoritative source of information in the field of knowledge management and related aspects. Topics covered include: -Knowledge creation, acquisition, codification, classification, organisation -Knowledge sharing, transfer, application, protection, retention -KM design and development -KM management and implementation -Measurement of knowledge management performance and benefits -Techniques and methods for managing knowledge -Technological tools for knowledge management, e.g. -knowledge bases, collaborative tools -expert/intelligent systems, knowledge mining/extraction -content/document management -portals, search and retrieval -e-learning, virtual reality, business intelligence, etc. -Human, organisational, strategic, behavioural, socio-cultural aspects -Public policy, economics, education policy, intellectual capital, ethics -Other related aspects of KM