چابکی در زنجیره تامین رقابتی با در نظر گرفتن رفتار مشتریان استراتژیک

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد مهندسی صنایع، دانشگاه صنعتی امیرکبیر، تهران، ایران.

2 استاد گروه مهندسی صنایع، دانشگاه صنعتی امیرکبیر، تهران، ایران.

3 استادیار مهندسی صنایع؛ دانشگاه صنعتی مالک اشتر، تهران، ایران.

چکیده

امروزه توسعه فناوری و توجه به نوآوری باعث شده است ذائقه‏ی بازار به ویژه بازار مُد و پوشاک دستخوش تغییرات مداوم شود. در چنین بازارهایی خرده‌فروشان و تولیدکننده‏گان چابکی و انعطاف‏پذیری را به عنوان یکی از راهبردها در زنجیره تأمین خود مد نظر قرار داده‏اند. در این مقاله ابتدا یک مدل استکلبرگ دو-سطحی شامل خرده‏فروش و تولیدکننده که به‌صورت سنتی بر سر مقدار کالا و قیمت فروش آن باهم رقابت دارند ارائه شده است. سپس مدل دیگری با اضافه نمودن برخی از ویژگی‏های چابکی به مدل اول توسعه داده‌شده است. در این مدل علاوه بر در نظر گرفتن رقابت بین اعضای زنجیره تأمین، تأثیر رفتار مشتریان بر تصمیمات اعضای زنجیره تأمین نیز لحاظ شده است. هدف این مقاله ارائه راهکاری مناسب برای تعیین مقدار سفارش، مقدار تولید و قیمت با در نظر گرفتن شرایط رقابت، نیاز بازار و رفتار مشتریان برای حداکثر کردن سود تولیدکننده و خرده‌فروش و همچنین کاهش مقدار حراج می‌باشد. مدل استکلبرگ دو-سطحی ارائه‌شده ابتدا با رویکرد کاهن-تاکر تک‌-سطحی شده و کارایی مدل با یک مثال عددی بررسی و تحلیل شده است. نتایج مدل دوم نشان می‌دهد که خرده‌فروش و تولیدکننده با به‌کارگیری پاسخ‌دهی سریع و چابکی می‌توانند قیمت فروش را افزایش و مقدار حراج کالا در انتهای فصل فروش را کاهش دهند که در نهایت موجب افزایش سود هر دو عضو زنجیره تأمین ‌شده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Agility in a competitive supply chain with considering strategic customers

نویسندگان [English]

  • Mohammad Kaviyani Charati 1
  • S.H. Ghodsypour 2
  • jafar Gheidar-Kheljani 3
1 M.S. Industrial Engineering, Amirkabir University of Technology
2 Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
3 Department of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.
چکیده [English]

Growths of technology and innovation have made continual changes in fashion business and costumer tastes. In situations like that, retailers and manufacturers select agility and flexibility as their main supply chain strategies. In this study, a bi-level model including the retailer and manufacturer who traditionally compete on the product quantity and price is proposed; then another model is developed by adding some characteristics of agility to first model. In the proposed model, in addition to the competition between the supply chain members, influences of the customers’ behavior on the decisions of supply chain members are considered. This study is aimed at proposing efficient solutions for determining the price and quantity of ordering and production, considering the situations of competition, customers and market toward maximization of the manufacturers’ and retailers’ profit. The proposed bi-level model is converted to a single-level one using the Karush-Kuhn-Tucker (KKT) and the results of the model are investigated and discussed by employing in a numerical example. Results show that the retailer and manufacturer, by making proper and precise decisions, can increase their sale price. Further, by improving their decisions, they can reduce the product clearance sale at the end of the sales season, which ends in the growth of profit for both of the supply chain members.

کلیدواژه‌ها [English]

  • Competitive Supply Chain
  • Quick Response
  • Agile Manufacturing
  • Bi-level Stackelberg

 

[1]      Suri, R., (2016). “It's about time: the competitive advantage of quick response manufacturing”, CRC Press.

[2]      Yusuf, Y.Y., Sarhadi, M., Gunasekaran, A., (1999). “Agile manufacturing: The drivers, concepts and attributes”, International Journal of production economics, 62(1): 33-43.

[3]      Dubey, R., Gunasekaran, A., (2015). “Agile manufacturing: framework and its empirical validation”, The International Journal of Advanced Manufacturing Technology, 76(9-12): 2147-2157.

[4]      Gunasekaran, A., (2001). “Agile manufacturing: the 21st century competitive strategy”, Elsevier, 25(1): 809-810.

[5]      Cachon, G.P., Swinney, R., (2011). “The value of fast fashion: Quick response, enhanced design, and strategic consumer behavior”, Management Science, 57(4): 778-795.

[6]      Yang, D., Qi, E., Li, Y., (2015). “Quick response and supply chain structure with strategic consumers”, Omega, 52: 1-14.

[7]      Choi, T.M., Sethi, S., (2010). “Innovative quick response programs: a review”, International Journal of Production Economics, 127(1): 1-12.

[8]      Cachon, G.P., Swinney, R., (2009). “Purchasing, pricing, and quick response in the presence of strategic consumers”, Management Science, 55(3): 497-511.

[9]      Serel, D.A., (2009). “Optimal ordering and pricing in a quick response system”, International journal of production economics, 121(2): 700-714.

[10]   Li, Y., Ye, F., Lin, Q., (2015). “Optimal lead time policy for short life cycle products under Conditional Value-at-Risk criterion”, Computers & Industrial Engineering, 88: 354-365.

[11]   Choi, T.M., (2017). “Quick response in fashion supply chains with retailers having boundedly rational managers”, International Transactions in Operational Research, 24(4): 891-905.

[12]   Dong, J., Dash, W.U., (2017). “Two-period pricing and quick response with strategic customers”, International Journal of Production Economics.

[13]   Agarwal, A., Shankar, R., Tiwari, M.K., (2007). “Modeling agility of supply chain”, Industrial marketing management, 36(4), 443-457.

[14]   Sharifi, H., Zhang, Z., (1999). “A methodology for achieving agility in manufacturing organisations: An introduction”, International journal of production economics, 62(1): 7-22.

[15]   Gunasekaran, A., (1999). “Agile manufacturing: a framework for research and development”, International journal of production economics, 62(1): 87-105.

[16]   Čiarnienė, R., Vienažindienė, M., (2014). “Agility and Responsiveness Managing Fashion Supply Chain”, Procedia-Social and Behavioral Sciences, 150: 1012-1019.

[17]   Giri, B.C., Bardhan, S., Maiti, T., (2015). “Coordinating a two-echelon supply chain with price and promotional effort dependent demand”, International Journal of Operational Research, 23(2): 181-199.

[18]   Seifbarghy, M., Nouhi, K., Mahmoudi, A., (2015). “Contract design in a supply chain considering price and quality dependent demand with customer segmentation”, International Journal of Production Economics, 167: 108-118.

[19]   Ren, J., Bian, Y., Xu, X., He, P., (2015). “Allocation of product-related carbon emission abatement target in a make-to-order supply chain”, Computers & Industrial Engineering, 80: 181-194.

[20]   Yue, D., You, F., (2017). “Stackelberg-game-based modeling and optimization for supply chain design and operations: A mixed integer bilevel programming framework”, Computers & Chemical Engineering, 102: 81-95.

[21]   Golpîra, H., Najafi, E., Zandieh, M., Sadi-Nezhad, S., (2017). “Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk”, Computers & Industrial Engineering, 107: 301-312.

[22]   Yaghin, R.G., Torabi, S.A., Ghomi, S.F., (2012). “Integrated markdown pricing and aggregate production planning in a two echelon supply chain: A hybrid fuzzy multiple objective approach”, Applied Mathematical Modelling, 36(12): 6011-6030.

[23]   Christopher, M., Lowson, R., Peck, H., (2004). “Creating agile supply chains in the fashion industry”, International Journal of Retail & Distribution Management, 32(8): 367-376.

[24]   Ananth, V., Mark, E., (1997). “Quick Response in Manufacturing-Retailer Channels”, Management Science, 4(3): 559-570.

[25]   Wen, U.P., Hsu, S.T., (1991). “Linear bi-level programming problems—a review”, Journal of the Operational Research Society, 42(2): 125-133.

[26]   Bard, J.F., (2013). “Practical bilevel optimization: algorithms and applications” Springer Science & Business Media, 30: 45.

[27]   Zhang, G., Lu, J., Gao, Y., (2015). “Bi-level Programming Models and Algorithms” In Multi-Level Decision Making”, Springer Berlin Heidelberg, 82: 47-62.

[28]   Bard, J.F., Falk, J.E., (1982). “An explicit solution to the multi-level programming problem”, Computers & Operations Research, 9(1): 77-100.

[29]   Shi, C., Lu, J., Zhang, G., (2005). “An extended Kuhn–Tucker approach for linear bilevel programming”, Applied Mathematics and Computation, 162(1): 51-63.

[30]   Allende, G.B., Still, G., (2013). “Solving bilevel programs with the KKT-approach”, Mathematical programming, 138(1-2): 309-332.

[31]   Vidal, C.J., Goetschalckx, M., (2001). “A global supply chain model with transfer pricing and transportation cost allocation”, European Journal of Operational Research, 129(1): 134-158.

[32]   Esmaeili, M., Aryanezhad, M.B., Zeephongsekul, P., (2009). “A game theory approach in seller–buyer supply chain”, European Journal of Operational Research, 195(2): 442-448.

[33]   Mokhlesian, M., Zegordi, S.H., (2014). “Application of multidivisional bi-level programming to coordinate pricing and inventory decisions in a multiproduct competitive supply chain”, The International Journal of Advanced Manufacturing Technology, 71(9-12): 1975-1989.

[34]   Shah, N.H., Widyadana, G.A., Wee, H.M., (2014). “Stackelberg game for two-level supply chain with price markdown option”, International Journal of Computer Mathematics, 91(5): 1054-1060.

[35]   Sinha, A., Malo, P., Frantsev, A., Deb, K., (2014). “Finding optimal strategies in a multi-period multi-leader–follower Stackelberg game using an evolutionary algorithm”, Computers & Operations Research, 41: 374-385.

[36]   Chow, P.S., Choi, T.M., Cheng, T.C.E., (2012). “Impacts of minimum order quantity on a quick response supply chain”, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 42(4): 868-879.

[37]   Bard, J.F., (1991). “Some properties of the bilevel programming problem”, Journal of optimization theory and applications, 68(2): 371-378.

[38]    فرخی، محمدامین؛ راستی برزکی، مرتضی، (1394). «قیمت­گذاری در یک زنجیره تأمین دوسطحی با در نظر گرفتن رقابت تولیدکنندگان در تصاحب بازار در سیستم تولید بر اساس سفارش با استفاده از نظریه بازی»، نشریه پژوهش­های مهندسی صنایع در سیستم­های تولید، 3(6): 207-219.