تحلیل زنجیره تامین با استفاده از رویکرد تئوری صف
محورهای موضوعی : مدیریت بازرگانی
مرتضی شفیعی
1
,
مهسا رفعت ماه
2
1 - دانشیار گروه مدیریت صنعتی، دانشکده اقتصاد و مدیریت، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
2 - دانشجوی دکتری مدیریت صنعتی، دانشکده اقتصاد و مدیریت، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
کلید واژه: بهره‌وری, زنجیره تامین, میانگین طول صف, میانگین زمان پاسخ گویی, میانگین زمان انتظار,
چکیده مقاله :
مسأله مهم کوتاهترین مسیر در شبکه زنجیره تأمین، فرستادن یک سفارش، از مبدأ به مقصد، در یک شبکه که بدون ساختار کامل و دائمی است، می باشد. در این مقاله برای پیدا کردن کوتاهترین مسیر از معیارهای تئوری صف استفاده شده است. ابتدا زنجیره تأمین و شبکه صف به طور خیلی خلاصه معرفی شدند و سپس یک زنجیره تأمین مربوط به شرکت بالان صنعت که دارای سه مرحله و دو ورودی برای انجام سفارش ها می باشد نشان داده شده است. هر ورودی توسط دو متغیر تصادفی نشان داده می شود، یکی برای زمان رویداد و دیگری برای مقدار اقلامی که در هر سفارش باید تحویل داده شود و در ادامه از طریق رویکرد شبکه صف معیارهای سنجش عملکرد و بهره وری استخراج گردید. هدف این مقاله محاسبه کمترین زمان پاسخ گویی برای تحویل اقلام در طول سه مرحله شبکه است. میانگین تعداد اقلامی که با این کمترین زمان پاسخ گویی تحویل داده می شوند، مطلوب ترین ظرفیت شبکه است. بعد از سرویس دهی به وسیله آخرین گره در هر مرحله از شبکه صف، تصمیمی برای مسیر کالاها به گره مناسب در مرحله بعد که می تواند کمترین زمان پاسخ را تولید کند، گرفته می شود.
An important issue in the supply chain concerns minimizing response time for the delivery of goods to the final destination, which can be achieved through selecting the correct route. The optimal path connecting the origin and destination nodes through the least intermediate nodes is called the shortest path. The shortest path in supply chain networks considered in this paper concerns the problem of sending an order from an original node to a destination node on a network which lacks a perfect and permanent fixed structure. The queuing theory measures were employed in the present enquiry to find out the shortest path. Initially, the supply chain and queuing network were concisely introduced and then, the two-input and three-stage supply chain of Balan Sanaat Company was displayed. Each input order to the supply chain is represented by two stochastic variables including the occurrence time and the number of commodities to be delivered. Further, the measures of the performance and productivity measures were extracted via the queuing network approach to serve the purpose of the study which was to compute the minimum response time for the delivery of items to the final destination along the three-stage network. The average number of items that can be delivered during this minimum response time constitutes the optimum capacity of the queuing network. At each stage of the queuing network, decisions regarding the most appropriate delivery route to the next node in the shortest possible time is made right at the preceding delivery node.
Abdulmalek, F. A., Rajgopal, J. (2017). Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study. International Journal of Production Economics, 107, 223–236.
Aitken J, (1998). Supply Chain Integration within the Context of a Supplier Association, Cranfield University, Ph.D. Thesis.
Alaghe Band A., Razavi M., (2008). Modeling and control of the supply chain using Linear Control Systems, International Management Conference, the fifth year, P 360.
Azar A., Mirghafoori A., (2004). Hierarchical model of the supply chain, Islamic Encyclopedia of Human Sciences Portal, P 32.
Bhaskar V., (2010), Modeling a supply chain using a network of queues, Applied mathematical modeling 34(1): 2074-2088.
Chang y. Harris M. (2001). Supply chain modeling using simulation, Ijsst, info. 2(1): 18-26.
Chao L., (2006). Supply chain modeling using fuzzy sets and possibility in an uncertain environment, Intelligent Control and Automation, The sixth word congress, 3608-3612.
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Cooper, M. C., Ellram, L. M., Gardner, J. T., Hanks, A. M., (1997). Meshing multiple alliances. J. Bus. Logist. 18, 67-89.
Cousins P.D., Mengus B.;"The implications of socialization and integration in supply chain management"; Journal of Operations Management, September 2005.
Daad A., Afshar Kazemi M., (2008). Improved layout East garment factory production line using simulation systems queue, Islamic Azad University, Central Tehran Branch, MS Thesis, P. 24.
Fleisch, E., Tellkamp, C. (2005). Inventory inaccuracy and supply chain performance: A Simulation study of a retail supply chain. International Journalof Production Economics, 95, 373–385.
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Hillier F.Lieberman G., (2005). Introduction to Operation Research, eighth ed., McGraw Hill, NY, USA.
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Hosseini S.M., Mohammadi A., Pishvaee M.S.; "Production systems and supply chain strategy selection"; Strategic Management Studies, No. 2, Summer 1389.
Hyun J.K., Jiyoon S.S., Wook K., (2016). Strategy for Improving Efficiency of Supply Chain Quality Management in Buyer-Supplier Dyads: The Suppliers’ Perspective.
Lambert, D. M., Cooper, M. C., (2000). Issues in supply chain management. Ind. Market Manag. 29, 65-83.
Manish K. Govil M. Fu C., (1999). Queuing theory in manufacturing: a survey, Journal of Manufacturing System, 4(2):457-469.
Miri Nejad M., Qadri S., (2009). Queuing systems and networks Petri use in modeling the supply chain, International Conference on Industrial Engineering, year VII.
Mosleh Shirazi A., Farhadi P., (2011). Gasoline supply chain modeling Oil Refining and Distribution Company using system dynamics, MA dissertation, School of Management, University of Shiraz.
Papoulis A., (1991). Probability, Random Variables and Stochastic Processes, Third ed, NewYork: McGraw Hill.
Rohatgi V.K., (1976). An introduction to probability theory, Mathematical Statistics, NewYork: Wiley.
Sadeghi Moghadam M.R., Momeni M., Nalchygr S.; "Integrated planning, supply, manufacturing and distribution supply chains using genetic algorithms"; Industrial Management, Vol. 1, No. 2, Summer 1388.
Sadeghimoghadam M., (2006) .Modeling of supply chain with genetic algorithm approach, the journal Science Teacher, 46(1): 226-211.
Shafiee M., Rezaee Z., Ebrahimi A.; "Strategic supply chain management"; Termeh Publication, Tehran, 1388.
Soleimani G., Timurid A., Makooye A., (2011). Dynamic system modeling natural gas supply chain, National Conference on Energy, the eighth year.
Tajima, M. (2016). Strategic value of RFID in supply chain management. Journal of Purchasing and Supply Management, 13, 261–273.
Teimoory, A., Ahmady, M., (1388). Supply Chain Management, Iran University of Science and Technology.(Persian)
Timuri A., Ahmad M.; "Supply chain management"; Tehran: Iranian Center for Science and Technology Press, 1388.
Trivedi K., (1982). Probability and Statistics with Reliability, Queuing and Computer Science Applications, New Jersey: Prentice Hall.
Tyndall G.; "The global supply chain challenge"; Supply Chain Management Review ,Vol. 3, No. 4, 2000.
Viswandham N., (2001). Performance modeling of supply chain using queueing networks, Robotics and automation, conference publication, Singapore, 529-534.
Vladislav Koksharov, (2016). Supply Chain Modelling, A Practical Approach, Applied Mathematical Modeling, 8(2):120-132.
Youngsu L., Suk-Chul R., (2017). Quantitative Model for Supply Chain Visibility: Process Capability Perspective, 2(1) - 14-25.
Zhu, J., (2014). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, Third Edition. Springer Cham Heidelberg New York Dordrecht London. ISBN 978-3-319-06647-9.
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Abdulmalek, F. A., Rajgopal, J. (2017). Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study. International Journal of Production Economics, 107, 223–236.
Aitken J, (1998). Supply Chain Integration within the Context of a Supplier Association, Cranfield University, Ph.D. Thesis.
Alaghe Band A., Razavi M., (2008). Modeling and control of the supply chain using Linear Control Systems, International Management Conference, the fifth year, P 360.
Azar A., Mirghafoori A., (2004). Hierarchical model of the supply chain, Islamic Encyclopedia of Human Sciences Portal, P 32.
Bhaskar V., (2010), Modeling a supply chain using a network of queues, Applied mathematical modeling 34(1): 2074-2088.
Chang y. Harris M. (2001). Supply chain modeling using simulation, Ijsst, info. 2(1): 18-26.
Chao L., (2006). Supply chain modeling using fuzzy sets and possibility in an uncertain environment, Intelligent Control and Automation, The sixth word congress, 3608-3612.
Conner, K.R., Prahalad, C.K., A resource-based theory of the firm: knowledge versus opportunism. Organizational science 7 (5): 477-501, 1996.
Cooper, M. C., Ellram, L. M., Gardner, J. T., Hanks, A. M., (1997). Meshing multiple alliances. J. Bus. Logist. 18, 67-89.
Cousins P.D., Mengus B.;"The implications of socialization and integration in supply chain management"; Journal of Operations Management, September 2005.
Daad A., Afshar Kazemi M., (2008). Improved layout East garment factory production line using simulation systems queue, Islamic Azad University, Central Tehran Branch, MS Thesis, P. 24.
Fleisch, E., Tellkamp, C. (2005). Inventory inaccuracy and supply chain performance: A Simulation study of a retail supply chain. International Journalof Production Economics, 95, 373–385.
Handfield R.B. Nichols E.L., (1999). Introduction to supply chain management, New Jersey: Prentice Hall Inc.
Heskett L., (1977). Logistics: essential to strategy, Harvard Business Review. 85 (6):85–96.
Hillier F.Lieberman G., (2005). Introduction to Operation Research, eighth ed., McGraw Hill, NY, USA.
Hosseini Baharanchi S.R.; "Investigation of the impact of supply chain integration on product innovation and quality"; Transaction: Industrial Engineering , Vol.16, No. 1, Sharif University of Technology, June 2009.
Hosseini S.M., Mohammadi A., Pishvaee M.S.; "Production systems and supply chain strategy selection"; Strategic Management Studies, No. 2, Summer 1389.
Hyun J.K., Jiyoon S.S., Wook K., (2016). Strategy for Improving Efficiency of Supply Chain Quality Management in Buyer-Supplier Dyads: The Suppliers’ Perspective.
Lambert, D. M., Cooper, M. C., (2000). Issues in supply chain management. Ind. Market Manag. 29, 65-83.
Manish K. Govil M. Fu C., (1999). Queuing theory in manufacturing: a survey, Journal of Manufacturing System, 4(2):457-469.
Miri Nejad M., Qadri S., (2009). Queuing systems and networks Petri use in modeling the supply chain, International Conference on Industrial Engineering, year VII.
Mosleh Shirazi A., Farhadi P., (2011). Gasoline supply chain modeling Oil Refining and Distribution Company using system dynamics, MA dissertation, School of Management, University of Shiraz.
Papoulis A., (1991). Probability, Random Variables and Stochastic Processes, Third ed, NewYork: McGraw Hill.
Rohatgi V.K., (1976). An introduction to probability theory, Mathematical Statistics, NewYork: Wiley.
Sadeghi Moghadam M.R., Momeni M., Nalchygr S.; "Integrated planning, supply, manufacturing and distribution supply chains using genetic algorithms"; Industrial Management, Vol. 1, No. 2, Summer 1388.
Sadeghimoghadam M., (2006) .Modeling of supply chain with genetic algorithm approach, the journal Science Teacher, 46(1): 226-211.
Shafiee M., Rezaee Z., Ebrahimi A.; "Strategic supply chain management"; Termeh Publication, Tehran, 1388.
Soleimani G., Timurid A., Makooye A., (2011). Dynamic system modeling natural gas supply chain, National Conference on Energy, the eighth year.
Tajima, M. (2016). Strategic value of RFID in supply chain management. Journal of Purchasing and Supply Management, 13, 261–273.
Teimoory, A., Ahmady, M., (1388). Supply Chain Management, Iran University of Science and Technology.(Persian)
Timuri A., Ahmad M.; "Supply chain management"; Tehran: Iranian Center for Science and Technology Press, 1388.
Trivedi K., (1982). Probability and Statistics with Reliability, Queuing and Computer Science Applications, New Jersey: Prentice Hall.
Tyndall G.; "The global supply chain challenge"; Supply Chain Management Review ,Vol. 3, No. 4, 2000.
Viswandham N., (2001). Performance modeling of supply chain using queueing networks, Robotics and automation, conference publication, Singapore, 529-534.
Vladislav Koksharov, (2016). Supply Chain Modelling, A Practical Approach, Applied Mathematical Modeling, 8(2):120-132.
Youngsu L., Suk-Chul R., (2017). Quantitative Model for Supply Chain Visibility: Process Capability Perspective, 2(1) - 14-25.
Zhu, J., (2014). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, Third Edition. Springer Cham Heidelberg New York Dordrecht London. ISBN 978-3-319-06647-9.