ترکیب بهینه پیش بینی در زنجیره تأمین چهار سطحی با هدف کمینه نمودن اثر شلاق چرمی
الموضوعات :
Maryam Daneshmand-Mehr
1
,
Marzban Najafi
2
,
Ramin Sadeghian
3
1 - Industrial Engineering Department, Islamic azad university , branch of Lahijan, Lahijan, Iran
2 - Industrial Engineering, Faculty of Engineering, Payame Noor University, Tehran, Iran
3 - Industrial Engineering, Payam e Noor University, Tehran, Iran
الکلمات المفتاحية: اثر شلاق چرمی, سیستم نقطه سفارش, تقاضا ثابت, زنجیره تأمین چهارسطحی, روش های پیش بینی,
ملخص المقالة :
اثر شلاق چرمی که در زنجیره اتفاق می افتد، منجر به ناکارآمدی‌هایی همچون موجودی اضافی و سفارشات عقب افتاده در طول زنجیره می‌گردد. انجام پیش‌بینی مناسب می تواند تا حدود زیادی این مشکلات را مرتفع سازد. با توجه به اینکه زنجیره تأمین دارای سطوح مختلفی می‌باشد، لازم است پیش‌بینی در هر سطحی از زنجیره انجام شود. این مقاله به بررسی مسأله ترکیب بهینه پیش بینی جهت کاهش اثر شلاق چرمی در زنجیره تأمین چهار سطحی می پردازد. برای این منظور یک زنجیره تأمین چهار سطحی در نظر گرفته شده است که در هر یک از سطوح آن، یکی از روش-های میانگین متحرک، هموارسازی نمایی، رگرسیون خطی و شبکه عصبی مصنوعی پرسپترون چند لایه را برای پیش بینی مورد استفاده قرار می-دهد. برای این منظور نخست نسبت به شبیه سازی زنجیره تأمین مورد نظر اقدام و سپس نتایج با استفاده از روش تحلیل واریانس مورد آزمون قرار گرفته‌اند. از بین ترکیبات، دو ترکیب روش‌های پیش‌بینی با کمترین اثر شلاق چرمی بدست آمده است. میانگین متحرک، شبکه عصبی، هموارسازی نمایی و رگرسیون خطی به ترتیب برای سطح های خرده فروش، عمده فروش، تولید کننده و تامین کننده به عنوان یک جواب و دیگری به شکل میانگین متحرک، هموار سازی نمایی، شبکه عصبی و رگرسیون خطی با همان ترتیب سطوح یاد شده در زنجیره تامین می‌باشند و ترکیبات دیگر از مطلوبیت کمتری برخوردارند.
1- Amiri, Maqsood. (2011). A model for multi-product inventory management by seller with concurrent order limitation. Journal of Industrial Engineering and Management. 2, 9-14.
2- Azimi, Seyyed Ali (2013). Demand supply chain forecasting by using learning algorithm machines, Master's Thesis, Semnan University.
3- Badakhshan, Ehsan, Persia, Mirmasaman (2015). A model for the dynamic systems on the bullwhip effect and the bullwhip effect cash flow in the supply chain to reduce these effects. International conference on advanced research in management and economics.
4- Bahrami shah bekandi, Taher Kalantari, Zahra, ImanKhan, Niloofar (2018). The Effect of Supply Chain Merger on Brand Value (Case Study: Iran Khodro Diesel Co.). Journal of Industrial Management Faculty of Humanities Islamic Azad University, Sanandaj Branch. 13(43), 83-94.
5- Bani Hashemi, Seyed Ali, (2019). Valid Experts and Analyzes on the Whiplash of a Four-Levels Supply Chain. Twelfth International Conference of Student Management Association of Health Services, Babolsar, Mazandaran University of Science and Technology.
6- Barlas, Y., & Gunduz, B. (2011). Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains. Journal of the Operational Research Society. 62(3), 458-473.
7- Bayraktar, E., Koh, S. L., Gunasekaran, A., Sari, K., & Tatoglu, E. (2008). The role of forecasting on bullwhip effect for E-SCM applications. International Journal of Production Economics. 113(1), 193-204.
8- Buchmeister, B., Friscic, D., & Palcic, I. (2014). Bullwhip effect study in a constrained supply chain. Procedia Engineering. 69, 63-71.
9- Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research. 184(3), 1140-1154.
10- Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management science. 46(3), 436-443.
11- Chen, F., Ryan, J. K., & Simchi‐Levi, D. (2000). The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics (NRL). 47(4), 269-286.
12- Dehghani, Amir Ahmad, Piri, Mehdi, Hesam, Moosa and Dehghani, Navid (2010). Estimation of daily evaporation from evaporation pan using three multilayer perceptron neural networks, radial and alimentary base function. Journal of water and soil conservation studies. Vol. 17, No. 2.
13- Esfandiari, Fariba, Hosseini Asad, Azadi Mohammad and Hejazi, Zahra (2010). Monthly forecasting of synoptic station temperature in Sanandaj using multi-layer perceptron neural network model (MLP) geography. Journal of Iranian geographic society. 45-65.
14- Forrester, J.W. (1958). Industrial dynamics – a major break-through for decision making. Harvard Business Review 36 (4), PP. 37–66.
15- Golabi, Mohammad Reza, Akhund Ali, Ali Mohammad and Radmanesh, Fereydoun (2013). Multi-layer perceptron neural network algorithm performance in seasonal modeling, selected stations in Khuzestan province. Journal of applied geosciences research of the 13th, No. 30.
16- Ismaeili, Maryam, Tat, Roya and Akbarzadeh, Maryam (2012). Comparison of the effect of different methods forecasting on the bullwhip effect on the supply chain. Tehran: 8hth international industrial engineering conference. 102-88.
17- JafarNejad, Ahmad (2013). Modern Operations & Production Management, (4th Edition). Tehran: University of Tehran Press.
18- Lee, H.L., Padmanabhan, V. and Whang, S., spring (1997). The bullwhip effect in supply chains. Sloan Management Review. 38 (3), PP. 93–102.
19- Lotfi, Mohammad Reza, Hamedi Tabari, Habib (2015). The role of bullwhip effect analys on the four-level supply chain in the industry using statistical methods. International conference on industrial engineering and management.
20- Montgomery, D. C. (2008). Design and analysis of experiments. John Wiley & Sons.
21- Movahedi, Yaser, Zolfaghari, Rohallah and Julay, Fariborz (2011). An Analysis of the role of financial factors in the "Bullwhip Effect" in a Two-Level Supply Chain. Journal of industrial engineering. 2, 208-199.
22- Nadizade Ardakani, Ali, Ghafari, Atefe, (2019). Measuring the Whip Effect on a Multilayer Three-Levels Linear Supply Chain. 14th International Conference on Industrial Engineering, Tehran, Iranian Society of Industrial Engineering - Iran University of Science and Technology.
23- Najafi, Mehdi, Zanjariani Farahani, Reza (2007). Comparison of the effect of different methods forecastingon the bullwhip effect. Tehran: 5th International industrial engineering conference. 36-24.
24- Razavi Haji Agha, Seyed Hossein, Ekrami, Hadi and Alfat Laaia (2012). Investigate the effect of combined forecasting methods Due to the bullwhip effect in multi-level supply chains. Journal of management improvement. 4, 113-96.
25- Rezaei Kahkha, Zohre, Rajaei, Amir, Shahraki, Zahra, (2019). Application of Machine Learning Algorithms in Supply Chain to Predict Customer Demands (Saipa Company). 6th National Congress of Iranian Electrical and Computer Engineering New Applied Perspectives, Tehran - Kharazmi University, Permanent Secretariat of Congress.
26- Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research. 252(1), 1-26.
27- Wang, X., & Disney, S. M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operational Research. 250(3), 691-701.
28- Zhang, X. (2004). The impact of forecasting methods on the bullwhip effect. International Journal of Production Economics. 88(1), 15-27.
_||_1- Amiri, Maqsood. (2011). A model for multi-product inventory management by seller with concurrent order limitation. Journal of Industrial Engineering and Management. 2, 9-14.
2- Azimi, Seyyed Ali (2013). Demand supply chain forecasting by using learning algorithm machines, Master's Thesis, Semnan University.
3- Badakhshan, Ehsan, Persia, Mirmasaman (2015). A model for the dynamic systems on the bullwhip effect and the bullwhip effect cash flow in the supply chain to reduce these effects. International conference on advanced research in management and economics.
4- Bahrami shah bekandi, Taher Kalantari, Zahra, ImanKhan, Niloofar (2018). The Effect of Supply Chain Merger on Brand Value (Case Study: Iran Khodro Diesel Co.). Journal of Industrial Management Faculty of Humanities Islamic Azad University, Sanandaj Branch. 13(43), 83-94.
5- Bani Hashemi, Seyed Ali, (2019). Valid Experts and Analyzes on the Whiplash of a Four-Levels Supply Chain. Twelfth International Conference of Student Management Association of Health Services, Babolsar, Mazandaran University of Science and Technology.
6- Barlas, Y., & Gunduz, B. (2011). Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains. Journal of the Operational Research Society. 62(3), 458-473.
7- Bayraktar, E., Koh, S. L., Gunasekaran, A., Sari, K., & Tatoglu, E. (2008). The role of forecasting on bullwhip effect for E-SCM applications. International Journal of Production Economics. 113(1), 193-204.
8- Buchmeister, B., Friscic, D., & Palcic, I. (2014). Bullwhip effect study in a constrained supply chain. Procedia Engineering. 69, 63-71.
9- Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research. 184(3), 1140-1154.
10- Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management science. 46(3), 436-443.
11- Chen, F., Ryan, J. K., & Simchi‐Levi, D. (2000). The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics (NRL). 47(4), 269-286.
12- Dehghani, Amir Ahmad, Piri, Mehdi, Hesam, Moosa and Dehghani, Navid (2010). Estimation of daily evaporation from evaporation pan using three multilayer perceptron neural networks, radial and alimentary base function. Journal of water and soil conservation studies. Vol. 17, No. 2.
13- Esfandiari, Fariba, Hosseini Asad, Azadi Mohammad and Hejazi, Zahra (2010). Monthly forecasting of synoptic station temperature in Sanandaj using multi-layer perceptron neural network model (MLP) geography. Journal of Iranian geographic society. 45-65.
14- Forrester, J.W. (1958). Industrial dynamics – a major break-through for decision making. Harvard Business Review 36 (4), PP. 37–66.
15- Golabi, Mohammad Reza, Akhund Ali, Ali Mohammad and Radmanesh, Fereydoun (2013). Multi-layer perceptron neural network algorithm performance in seasonal modeling, selected stations in Khuzestan province. Journal of applied geosciences research of the 13th, No. 30.
16- Ismaeili, Maryam, Tat, Roya and Akbarzadeh, Maryam (2012). Comparison of the effect of different methods forecasting on the bullwhip effect on the supply chain. Tehran: 8hth international industrial engineering conference. 102-88.
17- JafarNejad, Ahmad (2013). Modern Operations & Production Management, (4th Edition). Tehran: University of Tehran Press.
18- Lee, H.L., Padmanabhan, V. and Whang, S., spring (1997). The bullwhip effect in supply chains. Sloan Management Review. 38 (3), PP. 93–102.
19- Lotfi, Mohammad Reza, Hamedi Tabari, Habib (2015). The role of bullwhip effect analys on the four-level supply chain in the industry using statistical methods. International conference on industrial engineering and management.
20- Montgomery, D. C. (2008). Design and analysis of experiments. John Wiley & Sons.
21- Movahedi, Yaser, Zolfaghari, Rohallah and Julay, Fariborz (2011). An Analysis of the role of financial factors in the "Bullwhip Effect" in a Two-Level Supply Chain. Journal of industrial engineering. 2, 208-199.
22- Nadizade Ardakani, Ali, Ghafari, Atefe, (2019). Measuring the Whip Effect on a Multilayer Three-Levels Linear Supply Chain. 14th International Conference on Industrial Engineering, Tehran, Iranian Society of Industrial Engineering - Iran University of Science and Technology.
23- Najafi, Mehdi, Zanjariani Farahani, Reza (2007). Comparison of the effect of different methods forecastingon the bullwhip effect. Tehran: 5th International industrial engineering conference. 36-24.
24- Razavi Haji Agha, Seyed Hossein, Ekrami, Hadi and Alfat Laaia (2012). Investigate the effect of combined forecasting methods Due to the bullwhip effect in multi-level supply chains. Journal of management improvement. 4, 113-96.
25- Rezaei Kahkha, Zohre, Rajaei, Amir, Shahraki, Zahra, (2019). Application of Machine Learning Algorithms in Supply Chain to Predict Customer Demands (Saipa Company). 6th National Congress of Iranian Electrical and Computer Engineering New Applied Perspectives, Tehran - Kharazmi University, Permanent Secretariat of Congress.
26- Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research. 252(1), 1-26.
27- Wang, X., & Disney, S. M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operational Research. 250(3), 691-701.
28- Zhang, X. (2004). The impact of forecasting methods on the bullwhip effect. International Journal of Production Economics. 88(1), 15-27.
