تحلیل ارتباط میان توانمندسازهای سیستم ساخت انعطاف‌پذیر انسانی در صنایع خودروسازی با استفاده از رویکرد نقشه شناختی فازی
محورهای موضوعی : مدیریت صنعتی
غلامرضا جمالی
1
,
معصومه محمدی
2
1 - استادیار گروه مدیریت صنعتی، دانشگاه خلیج فارس، بوشهر، ایران
2 - دانش آموخته کارشناسی ارشد مدیریت صنعتی، دانشگاه خلیج فارس، بوشهر، ایران
کلید واژه: نقشه شناختی فازی, صنایع خودروسازی, سیستم ساخت انعطاف‌پذیر انسانی,
چکیده مقاله :
هدف مطالۀ حاضر تحلیل ارتباط میان توانمندسازهای سیستم ساخت انعطاف پذیر انسانی مبتنی بر نقشه شناختی فازی در صنعت خودروسازی ایران می باشد. مبانی نظری و تحلیل های مربوطه با استفاده از قضاوت گروه خبره انجام گرفت. از آنجایی که اعضای گروه تمایل داشتند با متغیرهای زبانی و لفظی منظور خود را برسانند؛ لذا از مجموعه گسسته ای از برچسب‌های لفظی در قالب نقشه های شناخت فازی، بمنظور تبیین ارتباط بین توانمندسازها بهره گیری شد. جامعۀ آماری دو نوع بوده است. جامعه اول شامل 20 نفر از خبرگان صنعت خودروسازی بوده که با سیستم ساخت انعطاف پذیر انسانی آشنایی داشته اند. که از بین آن ها 10 نفر به طور تصادفی انتخاب شده اند. جامعه دوم شامل 320نفر از کارکنان شرکت های شامل شرکت های ایران خودرو، پارس خودرو، زاگرس خودرو، سایپا، کرمان خودرو، گروه بهمن و مدیران خودرو بوده با استفاده از فرمول کوکران 174 نفر به شیوه نمونه گیری تصادفی برگزیده شدند. نتایج نشان داد کلیدی ترین توانمندسازهای سیستم ساخت انعطاف پذیر انسانی عبارتند از: تعهد کارکنان، پرسنل آموزش دیده، مشارکت و تعهد مدیریت ارشد، برنامه ریزی بلندمدت مؤثر، وضعیت مالی مطلوب سازمان، کاهش هزینه نگهداری، فرهنگ کار در سازمان، رضایت کارکنان، مشوق ها و پاداش ها، و تکنیک های عملیاتی و کنترل. پیشنهاد می گردد با توجه به نقش و اهمیت جایگاه عامل انسانی در سیستم ساخت انعطاف پذیر، یک نقشه راه در صنایع خودروسازی جهت جلوگیری از هدررفت منابع بویژه منابع انسانی تدوین گردیده تا بتوان در فرایند برنامه ریزی استراتژیک جهت بروز نمودن اهداف و چشم انداز شرکت از آن بهره گرفت.
This study aimed to analyze the relationship among Humanized Flexible Manufacturing System (HFMS) enablers in auto-manufacturing Industries using the Fuzzy Cognitive Map (FCM) approach. The theoretical foundation of the study was set and consolidated through a comprehensive literature review and based on views from 10 experts in Iran auto-manufacturing companies. The research population comprised 20 auto-manufacturing industry experts familiar with the HFMS system and 320 employees at car manufacturing companies including Iran Khodro, Pars, and Zagros, Saipa, Kerman, Bahman and Modiran Khodro companies. A sample of 10 from the experts and 174 from the employees were randomly selected using Cochran Formula. The relationship among the HFMS system enablers was investigated by analyzing verbal tags in the form of FCM. The findings revealed that the most important flexible HFMS system enablers comprised employees’ commitment, availability of trained personnel, top management involvement and commitment, effective long-term planning, sound favourable financial condition, reduced maintenance cost, organizational work culture, employees’ satisfaction, provision of incentives and rewards and operational and control techniques. The findings underscore the key role of human factor in the FMS and signify the need for developing a road map in car-manufacturing companies that can be employed to prevent the loss of resources including human resources and updat the company’s objectives and perspectives in the strategic planning process.
Amer, M., Tugrul, U. D., & Jetter, A. (2016). Technology Roadmap through Fuzzy Cognitive Mapbased Scenarios: The Case of Wind Energy Sector of a Developing Country. Technology Analysis & Strategic Management, 28(2), 131-155.
Arvan, M., Omidvar, A., & Ghodsi, R. (2016). Intellectual Capital Evaluation Using Fuzzy Cognitive maps: A Scenario-Based Development Planning. Expert Systems with Applications,55,21-36.
Bayazit, O. (2005). Use of AHP in Decision-Making for Flexible Manufacturing Systems. Journal of Manufacturing Technology Management, 16(7), 808-819.
Belassi, W., & Fadlalla, A. (1998). An Integrative Framework for FMS Diffusion.International Journal of Management Science,26(6),699-713.
Esmaelian, M., & Molavi, B. (2014). Prioritization and Selection Agility Capability Using Fuzzy TOPSIS and Fuzzy DEA Approach. Production and Operations Management, 5(2), 160-145, (In Persian).
Kosko, B. (1985). Adaptive Inference, Monograph, Verac Inc. Technical Report.
Kosko,B.(1992). Neural Networks and Fuzzy Systems. NY:Prentice-Hall.
Maniya, K., & Bhatt, M. (2011). The Selection of Flexible Manufacturing System Using Preference Selection Index Method. International Journal of Industrial and Systems Engineering, 9(3), 330-349.
Nagar, B., & Raj, T. (2012). An AHP-Based Approach for the Selection of HFMS: An Indian Perspective. International Journal of Operational Research, 13(3), 338-358.
Nagar, B., & Raj, T. (2012). Analysis of Critical Success Factors for Implementation of Humanized Flexible Manufacturing System in Industries. International Journal of Logistics Economics and Globalization, 4(4), 309-329.
Nagar, B., & Raj, T. (2013). Digraph and Matrix Evaluation for Shifting to Humanized Flexible Manufacturing System. International Journal of Logistics Economics and Globalization, 5(2), 149-165.
Prakash, R., Singhal, S., & Agrawal, A. (2018). An Integrated Fuzzy-Based Multi-Criteria Decision Making Approach for Selection of Effective Manufacturing System: A Case Study of an Indian Manufacturing Company. Benchmarking: An International Journal, 25(1), 280-296.
Raj, T., Shankar, R., & Suhaib, M. (2007). A Review of Some Issues and Identification of Some Barriers in the Implementation of FMS. The International Journal of Flexible Manufacturing Systems,19(1),1-40.
Raj, T., Shankar, R., & Suhaib, M. (2008). An ISM Approach for Modeling the Enablers of Flexible Manufacturing System: The Case for India. International Journal of Production Research, 46(24), 6883-6912.
Raj, T., Shankar, R., & Suhaib, M. (2010). GTA-Based Framework for Evaluating the Feasibility of Transition to FMS. Journal of Manufacturing Technology Management, 21(2), 160-187.
Raj, T., Shankar, R., Suhaib, M., & Khan, R. (2010). A Graph-Theoretic Approach to Evaluate the Intensity of Barriers in the Implementation of FMSs. International Journal of Services and Operations Management, 7(1), 24-52.
Raj, T., Shankar, R., Suhaib, M., Garg, S., & Singh, Y. (2008). An AHP Approach for Selection of Advanced Manufacturing System: A Case Study.International Journal of Manufacturing Research,3(4),471-498.
Rao, K., & Deshmukh, S. (1994). Strategic Framework for Implementing Flexible Manufacturing Systems in India. International Journal of Operations and Production Management, 14(4), 50-63.
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. (2007). Modelling IT Projects Success with Fuzzy Cognitive Maps. Expert Systems with Applications, 32, 543-559.
Safdary Ranjbar, M., Mansour, S., & Azami, A. (2015). Prioritizing and Analyzing the Interaction among Factors Effective on the Success of New Product Development projects by ISM and DEMATEL. Production and Operations Management, 6(1), 149-170, (In Persian).
Schneider, M., Shnaider, E., Kandel, A., & Chew, G. (1998), Automatic Construction of FCMs. Fuzzy Sets and Systems, 93, 161-172.
Talaie, H. R., Alem Tabriz, A., & Farsijani, H. (2017). Analysis the Enablers of Flexible Manufacturing System, Using Interpretive Structural Modelling and Interpretive Ranking Process. Industrial Management Studies, 15(44), 1-26, (In Persian).
Tao, F., Cheng, Y., Zhang, L., & Nee, A.Y.(2017). Advanced Manufacturing Systems: Socialization Characteristics and Trends. Journal of Intelligent Manufacturing, 28(5), 1079-1094.
Vasslides, J., & Jensen, O. (2016). Fuzzy Cognitive Mapping in Support of Integrated Ecosystem Assessments: Developing a Shared Conceptual Model among Stakeholders. Journal of Environmental Management, 166, 348-356.
_||_Amer, M., Tugrul, U. D., & Jetter, A. (2016). Technology Roadmap through Fuzzy Cognitive Mapbased Scenarios: The Case of Wind Energy Sector of a Developing Country. Technology Analysis & Strategic Management, 28(2), 131-155.
Arvan, M., Omidvar, A., & Ghodsi, R. (2016). Intellectual Capital Evaluation Using Fuzzy Cognitive maps: A Scenario-Based Development Planning. Expert Systems with Applications,55,21-36.
Bayazit, O. (2005). Use of AHP in Decision-Making for Flexible Manufacturing Systems. Journal of Manufacturing Technology Management, 16(7), 808-819.
Belassi, W., & Fadlalla, A. (1998). An Integrative Framework for FMS Diffusion.International Journal of Management Science,26(6),699-713.
Esmaelian, M., & Molavi, B. (2014). Prioritization and Selection Agility Capability Using Fuzzy TOPSIS and Fuzzy DEA Approach. Production and Operations Management, 5(2), 160-145, (In Persian).
Kosko, B. (1985). Adaptive Inference, Monograph, Verac Inc. Technical Report.
Kosko,B.(1992). Neural Networks and Fuzzy Systems. NY:Prentice-Hall.
Maniya, K., & Bhatt, M. (2011). The Selection of Flexible Manufacturing System Using Preference Selection Index Method. International Journal of Industrial and Systems Engineering, 9(3), 330-349.
Nagar, B., & Raj, T. (2012). An AHP-Based Approach for the Selection of HFMS: An Indian Perspective. International Journal of Operational Research, 13(3), 338-358.
Nagar, B., & Raj, T. (2012). Analysis of Critical Success Factors for Implementation of Humanized Flexible Manufacturing System in Industries. International Journal of Logistics Economics and Globalization, 4(4), 309-329.
Nagar, B., & Raj, T. (2013). Digraph and Matrix Evaluation for Shifting to Humanized Flexible Manufacturing System. International Journal of Logistics Economics and Globalization, 5(2), 149-165.
Prakash, R., Singhal, S., & Agrawal, A. (2018). An Integrated Fuzzy-Based Multi-Criteria Decision Making Approach for Selection of Effective Manufacturing System: A Case Study of an Indian Manufacturing Company. Benchmarking: An International Journal, 25(1), 280-296.
Raj, T., Shankar, R., & Suhaib, M. (2007). A Review of Some Issues and Identification of Some Barriers in the Implementation of FMS. The International Journal of Flexible Manufacturing Systems,19(1),1-40.
Raj, T., Shankar, R., & Suhaib, M. (2008). An ISM Approach for Modeling the Enablers of Flexible Manufacturing System: The Case for India. International Journal of Production Research, 46(24), 6883-6912.
Raj, T., Shankar, R., & Suhaib, M. (2010). GTA-Based Framework for Evaluating the Feasibility of Transition to FMS. Journal of Manufacturing Technology Management, 21(2), 160-187.
Raj, T., Shankar, R., Suhaib, M., & Khan, R. (2010). A Graph-Theoretic Approach to Evaluate the Intensity of Barriers in the Implementation of FMSs. International Journal of Services and Operations Management, 7(1), 24-52.
Raj, T., Shankar, R., Suhaib, M., Garg, S., & Singh, Y. (2008). An AHP Approach for Selection of Advanced Manufacturing System: A Case Study.International Journal of Manufacturing Research,3(4),471-498.
Rao, K., & Deshmukh, S. (1994). Strategic Framework for Implementing Flexible Manufacturing Systems in India. International Journal of Operations and Production Management, 14(4), 50-63.
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. (2007). Modelling IT Projects Success with Fuzzy Cognitive Maps. Expert Systems with Applications, 32, 543-559.
Safdary Ranjbar, M., Mansour, S., & Azami, A. (2015). Prioritizing and Analyzing the Interaction among Factors Effective on the Success of New Product Development projects by ISM and DEMATEL. Production and Operations Management, 6(1), 149-170, (In Persian).
Schneider, M., Shnaider, E., Kandel, A., & Chew, G. (1998), Automatic Construction of FCMs. Fuzzy Sets and Systems, 93, 161-172.
Talaie, H. R., Alem Tabriz, A., & Farsijani, H. (2017). Analysis the Enablers of Flexible Manufacturing System, Using Interpretive Structural Modelling and Interpretive Ranking Process. Industrial Management Studies, 15(44), 1-26, (In Persian).
Tao, F., Cheng, Y., Zhang, L., & Nee, A.Y.(2017). Advanced Manufacturing Systems: Socialization Characteristics and Trends. Journal of Intelligent Manufacturing, 28(5), 1079-1094.
Vasslides, J., & Jensen, O. (2016). Fuzzy Cognitive Mapping in Support of Integrated Ecosystem Assessments: Developing a Shared Conceptual Model among Stakeholders. Journal of Environmental Management, 166, 348-356.