طراحی مدل هوشمند نگهداری و تعمیرات پیشگیرانه در صنعت نساجی و پوشاک به کمک تجزیه و تحلیل آماری (مطالعه موردی کارخانجات نساجی بروجرد)
محورهای موضوعی :
مدیریت صنعتی
Sayyed Shahram fatemi
1
(Department of Industrial Engineering, Technical and Engineering College, Azad University of Science and Research, Tehran, Iran)
Mehrdad Javadi
2
(Associated Professor, Department of Mechanical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.)
Amir Azizi
3
(Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Sayyed Esmail Najafi
4
(Department of Industrial Engineering, Technical and Engineering College, Azad University of Science and Research, Tehran, Iran)
تاریخ دریافت : 1401/07/01
تاریخ پذیرش : 1401/11/05
تاریخ انتشار : 1401/12/01
کلید واژه:
نگهداری و تعمیرات پیشگیرانه,
هوشمند,
کارخانجات نساجی بروجرد,
چکیده مقاله :
در پژوهش حاضر تعیین مهمترین عوامل تاثیر گذار بر نگهداری و تعمیرات پیش گیرانه هوشمند در صنعت نساجی و پوشاک در تعامل با تولید به کمک نرم افزار Spss25 پرداخته شد. دیتاست تحقیق بر اساس یک نمونه ۲۰۰۰ تایی از داده‌ها و گزارش‌های مدیرکل صنایع نساجی و پوشاک وزارت صنعت ، معدن و تجارت کشور و کارخانجات صنعت نساجی بروجرد به عنوان محل اجرای طرح طی سال‌های ۱۳۹۴ تا ۱۳۹۹ به صورت نیم‌سال تنظیم شده است. این تحقیق با جمع آوری این حجم از اطلاعات با همکاری متخصصان نگهداری و صنعت نساجی (جمع آوری اطلاعات ۲۴۰ واحد صنعتی از۶۳۰ واحد ریسندگی و بافندگی کشور) کم نظیر است. حجم نمونه تحقیق شامل اساتید، مدیران و کارشناسان صنایع نساجی بوده که پس ازتکمیل پرسش نامه قبل از اجرای تحقیق (وضعیت عملکردی) و مقایسه آن با پرسش نامه بعد از اجرای تحقیق (وضعیت ایده آل) و انجام محاسبات تجزیه و تحلیل آماری نتایج زیر حاصل شده است. با بررسی ارتباط بین متغیرهای تحقیق وکلاس بندی عوامل موثر در نگهداری و تعمیرات پیش گیرانه به ترتیب عبارتند ازعامل استراتژی با امکان تعیین 34 درصد، عامل فن‌‌آوری 30 درصد، عامل محیط کار 16 درصد، عامل کارکنان 10 درصد، عامل کیفیت 10 درصد مهمترین عوامل بهبود نگهداری و تعمیرات پیشگیرانه در تعامل با تولید در صنعت نساجی و پوشاک تعیین شدند.
چکیده انگلیسی:
In this study, the identification of the most important influencing factors for intelligent preventive maintenance and repair in the textile industry in interaction with manufacturing was performed using Spss 25 software. The research data set is based on the 2000 sample data and reports from the Director General of Textile Industry under the Ministry of Industry, Mine and Trade and the factories in the textile industry of Borujerd where the project was implemented on a semiannual basis as of 2015 to 2020. This survey is significant in that it collects information in cooperation with specialists in the maintenance industry and the textile industry (information was collected from 240 establishments of 630 textile factories nationwide). The study sample size included professors, managers, and professionals from the textile industry. A comparison of the pre-test questionnaire (functional status) and the post-test questionnaire (ideal status) and statistical analysis calculations yielded the following results. The relationships between the study variables and the most important preventive maintenance and improvement factors in interaction with production in the textile industry have been classified as follows: strategy factor with the possibility of determining 34%, technology factor 30%, work environment factor 16%, staff factor 10%, the quality factor was identified by 10%.
منابع و مأخذ:
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Alrabghi, A., & Tiwari, A. (2016). A novel approach for modelling complex maintenance systems using discrete event simulation. Reliability Engineering & System Safety, 154, 160-170.
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Alsyouf, I. (2009). Maintenance practices in Swedish industries: Survey results. International Journal of Production Economics, 121(1), 212-223.
Bashiri, M., Badri, H., & Hejazi, T. H. (2011). Selecting optimum maintenance strategy by fuzzy interactive linear assignment method. Applied mathematical modelling, 35(1), 152-164.
Ulaganathan, J., & Sadyojatha, K. M. (2021). A Review On Maintenance Techniques For Industrial Equipment And Its Machine Learning Algorithms. International Journal of Advanced Research in Engineering and Technology (IJARET), 12(4), 183-194.
Campbell, J. D., Reyes-Picknell, J. V., & Kim, H. S. (2015). Uptime: Strategies for excellence in maintenance management. CRC Press.
Cappanera, P., Manfrida, G., Nicoletti, A., Pacini, L., Romagnoli, S., & Rossi, R. (2019, December). Digital model of a gas turbine performance prediction and preventive maintenance. In AIP Conference Proceedings(Vol. 2191, No. 1, p. 020033). AIP Publishing LLC.
Garcia, M. C., Sanz-Bobi, M. A., & Del Pico, J. (2006). SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox. Computers in industry, 57(6), 552-568.
Conrads, A., Scheffer, M., Mattern, H., König, M., & Thewes, M. (2017). Assessing maintenance strategies for cutting tool replacements in mechanized tunneling using process simulation. Journal of Simulation, 11(1), 51-61.
Nezami, F. G., & Yildirim, M. B. (2013). A sustainability approach for selecting maintenance strategy. International Journal of Sustainable Engineering, 6(4), 332-343.
García, F. J. Á., & Salgado, D. R. (2022). Analysis of the Influence of Component Type and Operating Condition on the Selection of Preventive Maintenance Strategy in Multistage Industrial Machines: A Case Study. Machines, 10(5), 385.
Gharoun, H., Hamid, M., & Torabi, S. A. (2021). An integrated approach to joint production planning and reliability-based multi-level preventive maintenance scheduling optimisation for a deteriorating system considering due-date satisfaction. International Journal of Systems Science: Operations & Logistics, 1-23.
Hax, A. C., & Majluf, N. S. (1996). The strategy concept and process: a pragmatic approach(Vol. 2, pp. 360-375). Upper Saddle River, NJ: Prentice hall.
Ierace, S., & Cavalieri, S. (2013). An analytic hierarchy process based model for the selection of decision categories in maintenance systems. Management and Production Engineering Review, 4.
Kang, K., & Subramaniam, V. (2018). Integrated control policy of production and preventive maintenance for a deteriorating manufacturing system. Computers & Industrial Engineering, 118, 266-277.
Wang, K. H., Wu, C. H., & Yen, T. C. (2022). Comparative cost-benefit analysis of four retrial systems with preventive maintenance and unreliable service station. Reliability Engineering & System Safety, 221, 108342.
Laks, P., & Verhagen, W. J. (2018). Identification of optimal preventive maintenance decisions for composite components. Transportation Research Procedia, 29, 202-212.
Lapa, C. M., Pereira, C. M., & e Melo, P. F. F. (2003). Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints. Reliability Engineering & System Safety, 81(1), 103-109.
Linnéusson, G., Ng, A. H., & Aslam, T. (2018). Relating strategic time horizons and proactiveness in equipment maintenance: a simulation-based optimization study. Procedia CIRP, 72, 1293-1298.
Mishra, S., Vanli, O. A., Kakareko, G., & Jung, S. (2019). Preventive maintenance of wood-framed buildings for hurricane preparedness. Structural safety, 76, 28-39.
Ozcan, S., & Simsir, F. (2019). A new model based on Artificial Bee Colony algorithm for preventive maintenance with replacement scheduling in continuous production lines. Engineering Science and Technology, an International Journal, 22(6), 1175-1186.
Seiti, H., Hafezalkotob, A., Najafi, S. E., & Khalaj, M. (2019). Developing a novel risk-based MCDM approach based on D numbers and fuzzy information axiom and its applications in preventive maintenance planning. Applied Soft Computing, 82, 105559.
Shaker, F., Shahin, A., & Jahanyan, S. (2019). Developing a two-phase QFD for improving FMEA: an integrative approach. International journal of quality & reliability management.
Shi, H., & Zeng, J. (2016). Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Computers & Industrial Engineering, 93, 192-204.
Patil, S. S., Bewoor, A. K., Kumar, R., Ahmadi, M. H., Sharifpur, M., & PraveenKumar, S. (2022). Development of Optimized Maintenance Program for a Steam Boiler System Using Reliability-Centered Maintenance Approach. Sustainability, 14(16), 10073.
Tsai, Y. T., Wang, K. S., & Teng, H. Y. (2001). Optimizing preventive maintenance for mechanical components using genetic algorithms. Reliability engineering & system safety, 74(1), 89-97.
Woodhouse, J. (2001). Combining the best bits of RCM, RBI, TPM, TQM, Six-Sigma and other ‘solutions’. The Woodhouse Partnership Ltd.
Xie, H., Shi, L., & Xu, H. (2013). Transformer Maintenance Policies Selection Based on an Improved Fuzzy Analytic Hierarchy Process. Comput., 8(5), 1343-1350.
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Alrabghi, A., Tiwari, A., & Savill, M. (2017). Simulation-based optimisation of maintenance systems: Industrial case studies. Journal of Manufacturing Systems, 44, 191-206.
Alrabghi, A., & Tiwari, A. (2016). A novel approach for modelling complex maintenance systems using discrete event simulation. Reliability Engineering & System Safety, 154, 160-170.
Alrabghi, A., & Tiwari, A. (2015). State of the art in simulation-based optimisation for maintenance systems. Computers & Industrial Engineering, 82, 167-182.
Alsyouf, I. (2009). Maintenance practices in Swedish industries: Survey results. International Journal of Production Economics, 121(1), 212-223.
Bashiri, M., Badri, H., & Hejazi, T. H. (2011). Selecting optimum maintenance strategy by fuzzy interactive linear assignment method. Applied mathematical modelling, 35(1), 152-164.
Ulaganathan, J., & Sadyojatha, K. M. (2021). A Review On Maintenance Techniques For Industrial Equipment And Its Machine Learning Algorithms. International Journal of Advanced Research in Engineering and Technology (IJARET), 12(4), 183-194.
Campbell, J. D., Reyes-Picknell, J. V., & Kim, H. S. (2015). Uptime: Strategies for excellence in maintenance management. CRC Press.
Cappanera, P., Manfrida, G., Nicoletti, A., Pacini, L., Romagnoli, S., & Rossi, R. (2019, December). Digital model of a gas turbine performance prediction and preventive maintenance. In AIP Conference Proceedings(Vol. 2191, No. 1, p. 020033). AIP Publishing LLC.
Garcia, M. C., Sanz-Bobi, M. A., & Del Pico, J. (2006). SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox. Computers in industry, 57(6), 552-568.
Conrads, A., Scheffer, M., Mattern, H., König, M., & Thewes, M. (2017). Assessing maintenance strategies for cutting tool replacements in mechanized tunneling using process simulation. Journal of Simulation, 11(1), 51-61.
Nezami, F. G., & Yildirim, M. B. (2013). A sustainability approach for selecting maintenance strategy. International Journal of Sustainable Engineering, 6(4), 332-343.
García, F. J. Á., & Salgado, D. R. (2022). Analysis of the Influence of Component Type and Operating Condition on the Selection of Preventive Maintenance Strategy in Multistage Industrial Machines: A Case Study. Machines, 10(5), 385.
Gharoun, H., Hamid, M., & Torabi, S. A. (2021). An integrated approach to joint production planning and reliability-based multi-level preventive maintenance scheduling optimisation for a deteriorating system considering due-date satisfaction. International Journal of Systems Science: Operations & Logistics, 1-23.
Hax, A. C., & Majluf, N. S. (1996). The strategy concept and process: a pragmatic approach(Vol. 2, pp. 360-375). Upper Saddle River, NJ: Prentice hall.
Ierace, S., & Cavalieri, S. (2013). An analytic hierarchy process based model for the selection of decision categories in maintenance systems. Management and Production Engineering Review, 4.
Kang, K., & Subramaniam, V. (2018). Integrated control policy of production and preventive maintenance for a deteriorating manufacturing system. Computers & Industrial Engineering, 118, 266-277.
Wang, K. H., Wu, C. H., & Yen, T. C. (2022). Comparative cost-benefit analysis of four retrial systems with preventive maintenance and unreliable service station. Reliability Engineering & System Safety, 221, 108342.
Laks, P., & Verhagen, W. J. (2018). Identification of optimal preventive maintenance decisions for composite components. Transportation Research Procedia, 29, 202-212.
Lapa, C. M., Pereira, C. M., & e Melo, P. F. F. (2003). Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints. Reliability Engineering & System Safety, 81(1), 103-109.
Linnéusson, G., Ng, A. H., & Aslam, T. (2018). Relating strategic time horizons and proactiveness in equipment maintenance: a simulation-based optimization study. Procedia CIRP, 72, 1293-1298.
Mishra, S., Vanli, O. A., Kakareko, G., & Jung, S. (2019). Preventive maintenance of wood-framed buildings for hurricane preparedness. Structural safety, 76, 28-39.
Ozcan, S., & Simsir, F. (2019). A new model based on Artificial Bee Colony algorithm for preventive maintenance with replacement scheduling in continuous production lines. Engineering Science and Technology, an International Journal, 22(6), 1175-1186.
Seiti, H., Hafezalkotob, A., Najafi, S. E., & Khalaj, M. (2019). Developing a novel risk-based MCDM approach based on D numbers and fuzzy information axiom and its applications in preventive maintenance planning. Applied Soft Computing, 82, 105559.
Shaker, F., Shahin, A., & Jahanyan, S. (2019). Developing a two-phase QFD for improving FMEA: an integrative approach. International journal of quality & reliability management.
Shi, H., & Zeng, J. (2016). Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Computers & Industrial Engineering, 93, 192-204.
Patil, S. S., Bewoor, A. K., Kumar, R., Ahmadi, M. H., Sharifpur, M., & PraveenKumar, S. (2022). Development of Optimized Maintenance Program for a Steam Boiler System Using Reliability-Centered Maintenance Approach. Sustainability, 14(16), 10073.
Tsai, Y. T., Wang, K. S., & Teng, H. Y. (2001). Optimizing preventive maintenance for mechanical components using genetic algorithms. Reliability engineering & system safety, 74(1), 89-97.
Woodhouse, J. (2001). Combining the best bits of RCM, RBI, TPM, TQM, Six-Sigma and other ‘solutions’. The Woodhouse Partnership Ltd.
Xie, H., Shi, L., & Xu, H. (2013). Transformer Maintenance Policies Selection Based on an Improved Fuzzy Analytic Hierarchy Process. Comput., 8(5), 1343-1350.
