ارزیابی میزان موفقیت مدیریت دانش در زنجیره تأمین سبز: رویکرد خبره محور ترکیبی تصمیم‌گیری چندشاخصه فازی
محورهای موضوعی :
مدیریت صنعتی
Fatemeh Zahra Rajabi kafshgar
1
(MSc. in Industrial Management, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran)
Mehrdad Madhoushi2
2
(Prof. of Industrial Management, Mazandaran University, Babolsar, Iran)
Hamidreza Fallah Lajimi3
3
(Assistant Prof. of Industrial Management, Mazandaran University, Babolsar, Iran)
تاریخ دریافت : 1397/09/28
تاریخ پذیرش : 1397/11/28
تاریخ انتشار : 1397/12/19
کلید واژه:
استراتژی سازمان,
دیمتل فازی,
فرهنگ سازمان,
مدیریت دانش,
زنجیره تأمین سبز,
چکیده مقاله :
امروزه موفقیت سازمان‌ها، با دانشی که می‌توانند تولید و توزیع کنند، سرعت تولید این دانش و میزان انباشت و استفاده از این دانش تولیدشده، ارتباط مستقیم دارد. از طرفی دغدغه‌های محیط زیستی پیرامون عملیات تولیدی صنایع، سازمان‌ها را بر آن داشته تا از اقدامات سبز در فرآیندهای مختلف زنجیره تأمین استفاده کنند. پیاده‌سازی موفق مدیریت دانش در زنجیره تأمین سبز موجب افزایش کارایی، اثربخشی، سود و مزیت رقابتی برای سازمان‌هاست. ازآنجایی‌که پیاده‌سازی مدیریت دانش نیازمند وقت و هزینه زیادی می‌باشد، مطالعه حاضر به بررسی میزان موفقیت مدیریت دانش در زنجیره تأمین سبز، بر اساس تصمیم‌گیری فازی پرداخته است. در این مقاله پس از مرور ادبیات، پنج بعد اصلی شامل صفات کارمندان، استراتژی سازمانی، فرهنگ سازمان، عوامل تکنولوژیکی و عوامل مدیریتی و 25 شاخص جهت بررسی میزان موفقیت مدیریت دانش در زنجیره تأمین سبز تعیین گردید. با استفاده از نظرات ۱۰ خبره شرکت فراصنعت و بومی‌سازی، 21 شاخص موفقیت مدیریت دانش در زنجیره تأمین سبز شناسایی و با استفاده از روش FDEMATEL وزن هریک از شاخص‌ها محاسبه شد. در مرحله بعد امتیاز موفقیت هر شاخص در شرکت با کمک FSAW محاسبه و درنهایت با استفاده از وزن محاسبه‌شده و امتیاز موفقیت شاخص‌ها، میزان موفقیت پیاده‌سازی مدیریت دانش در زنجیره تأمین سبز مشخص گردید. نتایج نشان دهنده این بود که شرکت فرا صنعت به میزان ۰٫۷۲ در پیاده‌سازی مدیریت دانش در زنجیره تأمین سبز موفق می‌باشد.
چکیده انگلیسی:
Todays, the success of organizations, is directly related to the knowledge that they can generate and distribute, the speed of producing, the amount of accumulation and usage of this knowledge. On the other hand, environmental concerns about manufacturing industries induce organizations to use green practices in various supply chain processes. The successful implementation of knowledge management in the green supply chain improves the efficiency, effectiveness, profitability and competitive advantage of organizations. Since the implementation of knowledge management requires a lot of time and cost, the present study examines the success rate of knowledge management in the green supply chain based on fuzzy decision making. In this paper, after reviewing the literature, five main dimensions including employee attributes, organizational strategy, organizational culture, technological factors and management factors and 25 indicators for assessing the success of knowledge management in the green supply chain are determined. Using the opinions of 10 experts of Farasanat company, 21 indicators of knowledge management success in the green supply chain have been identified and the weight of each index is calculated by using the FDEMATEL method. In the next step, the rank of each index is calculated with by FSAW. Finally, by using the calculated weight and index rank, the success rate of knowledge management implementation in the green supply chain is determined. The results show that Farasanat company has a success rate of 0.72 in implementing knowledge management in the green supply chain.
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Abdullah, L., Adawiyah, C. W. R., & Kamal, C. W. (2018). A decision making method based on interval type-2 fuzzy sets: An approach for ambulance location preference. Applied computing and informatics, 14(1), 65-72.
Aggestam, L., Soderstrom, E., & Persson, A. (2010). Seven Types of Knowledge Loss in the Knowledge Capture Process. In ECIS.
Ahmadi, S., Afshari, M. A., & Shekari, H. (2013). A model for evaluating the success of green supply chain management with green supplier approach (case study: Iran alloy steel). Trade studies, 17, 127-95. (in persian).
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Atoufi, N. A. M., & Banihashemi, S. A. (2013). Submit of Knowledge management model in the industry based on contextual factors. Journal of Public Administration, 5, 72-55. (in persian).
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Jasimuddin, S. M., & Zhang, Z. (2011). Transferring stored knowledge and storing transferred knowledge. Information Systems Management, 28(1), 84-94.
Khalfan, M. M., Kashyap, M., Li, X., & Abbott, C. (2010). Knowledge management in construction supply chain integration. International Journal of Networking and Virtual Organisations, 7(2-3), 207-221.
Kumar, S., & Thondikulam, G. (2006). Knowledge management in a collaborative business framework. Information Knowledge Systems Management, 5(3), 171-187.
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Li, Y. H., & Huang, J. W. (2017). The moderating role of relational bonding in green supply chain practices and performance. Journal of Purchasing and Supply Management, 23(4), 290-299.
Liao, S. H., Chen, C. M., & Wu, C. H. (2008). Mining customer knowledge for product line and brand extension in retailing. Expert systems with Applications, 34(3), 1763-1776.
Lim, M. K., Tseng, M. L., Tan, K. H., & Bui, T. D. (2017). Knowledge management in sustainable supply chain management: Improving performance through an interpretive structural modelling approach. Journal of cleaner production, 162, 806-816.
Lin, C., Hung, H. C., Wu, J. Y., & Lin, B. (2002). A knowledge management architecture in collaborative supply chain. Journal of Computer Information Systems, 42(5), 83-94.
Lin, C. J., & Wu, W. W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213.
Lin, K. P., Tseng, M. L., & Pai, P. F. (2018). Sustainable supply chain management using approximate fuzzy DEMATEL method. Resources, Conservation and Recycling, 128, 134-142.
Malhotra, A., Gosain, S., Sawy, O.A.E., 2005. Absorptive capacity configurations in supply chains: gearing for partner-enabled market knowledge creation. MIS Q. 145e187.
Maqsood, T., Walker, D., & Finegan, A. (2007). Extending the “knowledge advantage”: creating learning chains. The Learning Organization, 14(2), 123-141.
Marra, M., Ho, W., & Edwards, J. S. (2012). Supply chain knowledge management: A literature review. Expert systems with applications, 39(5), 6103-6110.
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Morovati, S. A., & Jamshidi, Z. (2016). Analyzing the Factors Affecting the Satisfaction of Domestic Tourists in Yazd Province Using Fuzzy DEMATEL. Tourism Management Studies (Tourism Studies), (33), 85-104. (in persian).
Müller, M., Seuring, S., (2007). Reducing information technology-based transaction costs in supply chains. Ind. Manag. Data Syst. 107 (4), 484e500.
Nagurney, A.& Yu, M. (2012). Sustainable Fashion Supply Chain Management Under Oligopolistic. International Journal of Production Economics, 135(2):532-540.
Nätti, S., & Ojasalo, J. (2008). Loose coupling as an inhibitor of internal customer knowledge transfer: findings from an empirical study in B-to-B professional services. Journal of Business & Industrial Marketing, 23(3), 213-223.
Pandey, A., & Kumar, A. (2017). Commentary on “Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach”. Applied Soft Computing, 51, 351-352.
Patil, S. K., & Kant, R. (2014). A Hybrid Approach Based on Fuzzy DEMATEL and FSAW to Predict Success of Knowledge Management Adoption in Supply Chain. Applied Soft Computing, 18, 126-135.
Paton, R. A., & McLaughlin, S. (2008). Services innovation: Knowledge transfer and the supply chain. European Management Journal, 26(2), 77-83.
Pezzillo Iacono, M., Martinez, M., Mangia, G., & Galdiero, C. (2012). Knowledge creation and inter-organizational relationships: the development of innovation in the railway industry. Journal of Knowledge Management, 16(4), 604-616.
Pirannejad, A. (2016). A Meta-analysis Study of Knowledge Management Researches in Iran's Public Organizations. Journal of Public Administration, 80, 299-324.
Plesa, S., & Prostean, G. (2018). Business Process Management for Model Based Design Automotive Projects. Procedia-Social and Behavioral Sciences, 238, 313-322.
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