اثر برنامه‌های گردش شغلی سلولی بر عملکرد سلول ناب

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استادیار مدیریت تولید و عملیات، مرکز مطالعات مدیریت ایران، تهران، ایران

2 دانشیار دانشکدة مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران

چکیده

گردش شغلی، عملیات استاندارد سلول­های ناب شناخته می­شود. با این‌حال، گردش شغلی به‌عنوان تشریح‌کنندة عملکرد سلول ناب در پژوهش‌ها بررسی نشده ­است. فاصلة (تواتر) گردش شغلی از طریق اثرگذاری بر نحوة تخصیص وظایف به افراد، بر عملکرد سلول در خلال چند دورة گردش شغلی تأثیر می­گذارد. در این پژوهش، عملکرد سلول ناب از طریق عامل فاصلة گردش شغلی و به‌همراه عوامل مرتبط با سلول (شامل اندازة سلول، نوع وظایف سلول و زمان تکت) تشریح شده است که نوآوری اصلی تحقیق نیز به‌شمار می‌آید. پس از مدل‌سازی ریاضی و تحلیل آن، آزمایش‌های مناسب با استفاده از طراحی آزمایش‌های تاگوچی اجرا شد و داده­ها در قالب جواب‌های نزدیک به بهینه برای اهداف عملکردی سلول به‌دست آمد. سپس با تحلیل واریانس یک و چندمتغیره، اثر عوامل آزمون شد. نتایج تحقیق اثر عامل فاصلة گردش شغلی و روابط متقابل پیچیده میان آن و دیگر عوامل را تأیید کرد و الگوهایی را در زمینة رفتار معیارهای عملکرد سلولی تعیین کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Investigating the effects of Job Rotation Schedules on performance of lean cells

نویسندگان [English]

  • Ashkan ayough 1
  • Mostafa Zandieh 2
1 Assistant Prof., Iran Center for Management Studies(ICMS), Iran
2 Associate Prof., Faculty of Management and Accounting , Shahid Beheshti University, Tehran, Iran
چکیده [English]

Lean cell operates itself by what is called standard operations. Job rotation is known as the standard operation of a lean cell. However, researchers have not studied job rotation to demonstrate the performance of a lean cell. Job rotation decisions affect lean cell performance through influencing the manner in which tasks assigned to the selected operators during several rotation periods. This research studies the lean cell performance as a function of rotation interval, cell factors (size and type) and takt time. The performance targeted in term of 4 different measures. After modeling job rotation scheduling problem and developing an efficient algorithm to find near optimal solutions, data for lean performance against arrangement of factors was gathered by scenarios made based on Taguchi's approach to design of experiments. To analyze the model and impact of factors, ANOVA and MANOVA and interaction analysis were applied. The results shown that the effect of rotation interval and its complex interactions with other factors are statistically significant and some patterns for lean cell performance were obtained.

کلیدواژه‌ها [English]

  • Lean cell
  • Lean cell performance
  • Job rotation scheme
  • Taguchi's Design of experiments
  • ANOVA/MANOVA
Ahmed M. Deif .(2012). Dynamic analysis of a lean cell under uncertainty, International Journal of Production Research, 50(4): 1127- 1139.

 

Alem Tabriz, A., Tallaei, H. R. & Moradi, E. (2012). Evaluating the key factors of successful implementation of world class manufacturing using an integrated approach of interpretive structural modeling (ISM), Graph theory and Matrix approach (GTMA): A Case study for Iran Khodro and Saipa in Iran,  Journal of Industrial Management, 5(1): 63- 81. (In Persian)

 

Aryanezhad, M. B., Kheirkhah, A. S., Deljoo, V. & Mirzapour Al-e-hashem, S. M. J. (2009). Designing safe job rotation schedules based upon worker’s skills, International Journal of Advance Manufacturing Technology,  (41): 193– 199.

 

Ayough, A., Zandieh, M., Farsijani, H. & Dorri, B. (2014). Job rotation scheduling in the newly arranged lean cell through genetic algorithmapproach, Journal of industrial Management Persprctive, 15 33- 59. (In Persian)

 

Azizi, N., Zolfaghari, S. & Liang, M., (2010). Modeling job rotation in manufacturing systems: The study of employee’s boredom and skill variations, International Journal of Production Economics,  (123): 69–85.

 

Baradaran, S., Daraee, M. R. & Fattahi, D. (2015). Examining the readiness of Iran Transfo Rey Co., for performing the lean production system, Journal of Industrial management, Accepted Article online on April 2015. (In Persian)

 

Black, J. T. & Hunter, S. L. (2003). Lean Manufacturing Systems and Cell Design, Society of Manufacturing Engineers, Dearborn, Michigan.

 

Black, J. T. (2007). Design rules for implementing the Toyota production system, International Journal of Production Research, 45(16): 3639– 3664.

 

Carnahan, B. J., Redfern, M. S. & Norman, B. (2000). Designing safe job rotation schedules using optimization and heuristic search, Ergonomics, 43(4): 543– 560.

 

Fogarty, D. W., Blackstone, J. R. & Hoffmann, T. R. (1991). Production and Inventory management, Cincinnati, Ohio.

Hirano, H. (1987). JIT Factory Revolution, Productivity Press, Portland, OR.

 

Homan, H. A. (2001). Multi variate data analysis in behavioral researches, Published by Parsa, Tehran. (In Persian)

 

Huang, Y. (1999). Employee training and assignment for team-based manufacturing systems, PhD Thesis, Submitted to the department of systems and industrial engineering, University of Arizona.

 

Hyer, N. & Wemmerlov, U. (2002). Reorganizing the Factory, Productivity Press, Portland, OR.

 

Kannan, V. R. & Jensen, J. B. (2004). Learning and labor assignment in a dual constrained cellular shop, International Journal of Production Research, 42(7): 1455– 1470.

 

Kara, Y., Ozcan, U. & Peker, A. (2007). An approach for balancing and sequencing mixed-model JIT U-lines, International Journal Advanced Manuf Technol, 32(11): 1218- 1231.

Kasaee, M., Farrokh, M. & Tallaei, H. R. (2013). Ranking and selecting agile providers for achieving enterprise agility by using ANP and Dematel: A case study for Bahman Motor Group in Iran, Journal of Industrial management, 4(2): 135- 152. (In Persian)

 

Lian, K., Zhang, C., Gao, L. & Shao, X. (2012). A modified colonial competitive algorithm for the mixed-model Uline balancing and sequencing problem, International Journal of Production Research, 1–15.

 

Lu, J. Ch. & Yang, T. (2014). Implementing lean standard work to solve a low work-in-process buffer problem in a highly automated manufacturing environment, International Journal of Production Research, DOI: 10.1080/00207543.2014.937009.

 

McDonald, T. & Kimberly, P. E. (2009). Development and application of a worker assignment model to evaluate a leanmanufacturing cell, International Journal of Production Research, 47(9): 2427– 2447.

 

Miltenburg, J. (2002). Balancing and scheduling mixed-model U-shaped production lines, International Journal Flexible Manufacturing Systems,14(2): 119– 151

 

Monden, Y. (1993). Toyota production system: An integrated approach to just in time, Institute of Industrial Engineers, Norcross.

 

Montgomery, D. C. (2000). Design and Analysis of Experiments, 5th edition, John Wiley & Sons, New York, NY.

 

Nakade, K. & Nishiwaki, R. (2008). Optimal allocation of heterogeneous workers in a U-shaped production line, Computers & Industrial Engineering,(54): 432– 440

 

Needy, K. L., et al. (2001). Human capital assessment in lean manufacturing, Proceedings of 2001 American Society Forengineering Management Conference, Huntsville: AL.

 

Nembhard, D. A. & Osothsilp, N. (2005). Learning and forgetting-based worker selection for tasks of varying complexity, Journal of the Operational Research Society, (56): 576– 587.

 

Nembhard, D., A. & Norman, B. A. (2006). Cross-training in production systems with human learning and forgetting, Handbook of Industrial and Systems Engineering, Chapter 16, In Badiru, A. B. (Ed.), CRC Press, New York, 16-1-13 49(10): 2833– 2855.

 

Ozcan, U., Kellego, Z. T. & Toklu, B. (2011). A genetic algorithm for the stochastic mixed-model u-line balancing and sequencing problem, International Journal of Production Research, 49(6): 1605– 1626.

 

Ranjit, R. (1990). A primer on the Taguchi method, 1st edition, Van Nostard Reinhold, USA.

 

Rehab, M. A. & Ahmed, M. D. (2014). Dynamic Lean Assessment for Takt Time Implementation, Procedia CIRP, 17: 577– 581.

 

Shewchuk, J. P. (2008). Worker allocation in lean U-shaped production lines, International Journal of Production Research, 46(13): 3485– 3502.

 

Shingo, S. (1997). A Study of the Toyota Production System from an Industrial Engineering Viewpoint, Shingo, S.

 

Taghi Taghavi Fard, M.  (2011). A New Mathematical Model for Solving Multi-Product Assembly Line Balancing Problems, Journal of Industrial management, 3(6): 1– 16. (In Persian)

 

Taguchi, G., Wu, Y. & Chowdhury, S. (2004). Taguchi’s quality engineering handbook, Wiley, Hoboken.

 

Tharmmaphornphilas, W. & Norman, B. (2004). A quantitative method for determining proper job rotation nintervals, Annals of Operations Research (128): 251– 266.