• Home
  • Analyzing the Drivers of Bullwhip Effect in Pharmaceutical Industry’s Supply Chain

Share To

Article Url


Manuscript ID : JSM-2209-1691 (R1) Visit : 511 Page: 97 - 117

10.30495/jsm.2022.1966147.1691

20.1001.1.23222301.2023.9.1.7.6

Article Type: Original Research

Analyzing the Drivers of Bullwhip Effect in Pharmaceutical Industry’s Supply Chain

Subject Areas : Business Strategy

Parvaneh Tavakkol 1 , Bijan Nahavandi 2 , Mahdi Homayounfar 3

1 - PhD Candidate, Department of Industrial Management, Bandar-e- Anzali International Branch, Islamic Azad University, Bandar-e-Anzali, Iran
2 - Assistant Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran

Received: 2022-09-22 Accepted : 2022-12-15 Published : 2023-01-01

Keywords: Delphi, DEMATEL, Inventory Fluctuation, Supply Chain, Fuzzy Cognitive Map, Bullwhip Effect,

Abstract :

The purpose of this study is to evaluate the drivers of bullwhip effect in supply chain of the pharmaceutical industry. This research is descriptive in terms of method and applied in terms of purpose. Conducting the research, first, based on the reviewing the literature on bullwhip effect in the supply chain, affecting drivers were extracted and were sent to 15 experts in form of a questionnaire. Then, using the fuzzy Delphi method, the final affecting criteria on bullwhip effect in the supply chain of the pharmaceutical industry were identified. Finally, in order to examine the relationships between the 13 basic drivers, another questionnaire was designed and asked the experts to fill it, where based on their answers and using DEMATEL and fuzzy cognitive map methods, critical drivers were determined. FCMapper software was used to conduct fuzzy cognitive map method and MATLAB was used for the DEMATEL method. In terms of centrality index in fuzzy cognitive map method, structured inventory control process, delivery time, inventory storage of chain elements, inventory policy and product return rate are 5 criteria of critical importance. In addition, the indicators of the number of echelons, forecasting (method) error and up-to-date demand forecast are in the eleventh to thirteenth ranks. Based on the results of DEMATEL method, inventory policy, price fluctuations, inventory storage of chain elements, structured inventory control process, differences with the desired inventory and information transparency were identified as the main drivers of bullwhip effect in the supply chain of the pharmaceutical industry.

References:


  1. Banihashemi, S.A., & Haji Molana, S.M. (2017). Analyzing Bullwhip Effect Sensitivity in a Four-level Supply Chain Using Average Moving Method to Forecast the Demand. Industrial Management Journal, 9(1), 43-58. 22059/imj.2017.223681.1007173
    2.
  2. Braz, A.C., Mello, A.M., de Vasconcelos Gomes, L.A., & Nascimento, P.T. (2018). The bullwhip effect in closed-loop supply chains: A systematic literature review. Journal of Cleaner Production, 202, 376-389. org/10.1016/j.jclepro.2018.08.042
    3.
  3. Cannella, S., Bruccoleri, M., & Framinan, J.M. (2016). Closed loop supply chains: What reverse logistics factors influence performance? International Journal of Production Economics, 175, 35-49. org/10.1016/j.ijpe.2016.01.012
    4.
  4. Chang, A.Y., Yu, H.F., Yang, T.H. (2013). A study of the key factors contributing to the bullwhip effect in the supply chain of the retail industry. Journal of Industrial and Production Engineering, 30(7), 433-442. org/10.1080/21681015.2013.849767
    5.
  5. Chen F., Drezner, Z., Ryan, J., K., Simchi-Levi, D. (2000). Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting. Management Science, 46 (3), 436-443. doi:0025-1909/00/4603/0436505.00
    6.
  6. Cheng, L.C. (2009). Impact of inventory policy consistency on the three-stage supply chain performance. Journal of Academy of Business and Economics, 9 (4), 33-53. doi:1016/j.cie.2008.02.002
    7.
  7. Corum, A., Vayvay, Ö., Bayraktar, E., (2014). The impact of remanufacturing on total inventory cost and order variance. Journal of Cleaner Production, 85, 442-452. org/10.1016/j.jclepro.2014.06.024
    8.
  8. Dominguez, R., Cannella, S., Barbosa-Povoa, A.P., Framinan, J.M. (2018). OVAP: A strategy to implement partial information sharing among supply chain retailers. Transportation Research Part E: Logistics and Transportation Review. 110, 122–136. org/10.1016/j.tre.2017.12.016
    9.
  9. Dominguez R., Cannella S., Framinan, J.M. (2015). On returns and network configuration in supply chain dynamics. Transportation Research Part E: Logistics and Transportation Review, 73, 152-167. org/10.1016/j.tre.2014.11.008
    10.
  10. Tai, P.D., Hien Duc, T.T., Buddhakulsomsiri, J. (2018). Measure of bullwhip effect in supply chain with price-sensitive and correlated demand. Computers & Industrial Engineering, 127, 408-419. org/10.1016/j.cie.2018.10.027
    11.
  11. Forrester, J.W. (1961) Industrial Dynamics, New York, MIT Press.
    12.
  12. Fu, D., Ionescu, C., Aghezzaf, E.H., De Keyser, R. (2015). Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy. Computers & Industrial Engineering, 81, 46–57. org/10.1016/j.cie.2014.12.024
    13.
  13. Gabus, A., Fontela, E. (1972). World problems, an invitation to further thought within the frame work of DEMATEL. Battelle Geneva Research Center, Geneva, Switzerland.
    14.
  14. Gavirneni, S. (2006). Price fluctuations, information sharing, and supply chain performance European Journal of Operational Research, 174, 1651-1663. org/10.1016/j.ejor.2005.04.037
    15.
  15. Goodarzi, M., Farzipoor Saen, R. (2020). How to measure bullwhip effect by network data envelopment analysis? Computers & Industrial Engineering, 139, 105431. org/10.1016/j.cie.2018.09.046
    16.
  16. Haines, R., Hough, J., Haines, D. (2017). A Metacognitive Perspective on Decision Making in Supply Chains: Revisiting the Behavioral Causes of the Bullwhip Effect. International Journal of Production Economics, 184, 7-20. org/10.1016/j.ijpe.2016.11.006
    17.
  17. Holland, W., Sodhi, M.S. (2004). Quantifying the effect of batch size and order errors on the bullwhip effect using simulation. International Journal of Logistics Research and Applications, 7 (3), 251-261. doi:10.1080/13675560412331298518
    18.
  18. Homayounfar, M., Goudarzvand chegini, M., Daneshvar, A. (2018). Prioritization of Green Supply Chain Suppliers Using a hybrid Fuzzy Multi-Criteria Decision-Making approach. Journal of Operational Research and Its Applications, 15(2), 41-6. doi: 1001.1.22517286.2018.15.2.5.9

  19. Hussain, M., Drake, P.R. (2011). Analysis of the bullwhip effect with order batching in multi‐echelon supply chains. International Journal of Physical Distribution & Logistics, 41 (8), 797-814.doi:1108/09600031111185248
    20.
  20. Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R., Mieno, H. (1993). The max–min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55, 241–253. org/10.1016/0165-0114(93)90251-C
    21.
  21. Jiang, Q., Ke, G. (2019). Information Sharing and Bullwhip Effect in Smart Destination Network system. Ad Hoc Networks, 87 (1), 17-25. org/10.1016/0165-0114(93)90251-C
    22.
  22. Kadivar, M., Akbarpour Shirazi, M. (2018). Analyzing the Behavior of the Bullwhip Effect considering Different Distribution Systems. Applied Mathematical Modelling, 59, 319-340. org/10.1016/j.apm.2018.01.028
    23.
  23. Kelle, P., Milne, A. (1999). The effect of (s, S) ordering policy on the supply chain. International Journal of Production Economics, 59, 113–122. org/10.1016/S0925-5273(98)00232-1
    24.
  24. Koso, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24, 65-75. org/10.1016/S0020-7373(86)80040-2
    25.
  25. Kumara, A., Shankarb, R., Aljohani, N.R. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial Marketing Management, 90, 493-507. org/10.1016/j.indmarman.2019.05.003
    26.
  26. Lee, H.L., Padmanabhan, V., Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43, 546–558. doi:1287/mnsc.1040.0266
    27.
  27. Lee, S., Klassen, R.D., Furlan, A., Vinelli, A. (2014). The green bullwhip effect: Transferring environmental requirements along a supply chain. International Journal of Production Economics, 156, 39-51. org/10.1016/j.ijpe.2014.05.010
    28.
  28. Li, C. (2013). Controlling the bullwhip effect in a supply chain system with constrained information flows. Applied Mathematical Modelling, 4, 1897-1909. org/10.1016/j.apm.2012.04.020
    29.
  29. Li Q., Disney S.M., Gaalman G. (2014). Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy. International Journal of Production Economics, 149, 3-16. org/10.1016/j.ijpe.2013.11.010
    30.
  30. Ma, J., Bao, B. (2017). Research on bullwhip effect in energy-efficient air conditioning supply chain. Journal of Cleaner Production, 143, 854-865. org/10.1016/j.jclepro.2016.12.035
    31.
  31. Metters, R. (1997). Quantifying the bullwhip effect in supply chains. Journal of Operations Management, 15, 89–100. org/10.1016/S0272-6963(96)00098-8
    32.
  32. Mirab Samiee, Z., Rostamzadeh, M., Fatahi Valilai, O. (2020). An Analysis of the BWE-Associated Costs: The issue of Demand Forecasting Accuracy. IFAC, 53 (2), 10836-10842. org/10.1016/j.ifacol.2020.12.2870
    33.
  33. Mostafaee, K., Azar, A., Moghbel, A. (2018). Identification and Analysis of Operational Risks: A Fuzzy Cognitive Map Approach. Journal of Asset Management and Financing, 6(4), 1-18. [In Persian] 1001.1.23831170.1397.6.4.2.9
    34.
  34. Najafi, M., Daneshmand-Mehr, M., Sadeghian, R. (2019). The Impact of Forecasting Methods Combination for Reducing Bullwhip Effect in a Four-level Supply Chain under Variable Demand. Journal of Operational Research in Its Applications, 16(3), 89-109. 1001.1.22517286.2019.16.3.4.7

  35. Narayanan, V.G., Raman, A. (2004). Aligning incentives in supply chains. Harvard business review, 82(11), 94-102.
    36.
  36. Nodoust, S., & Mirzazadeh, A. (2015). An Uncertain Time Horizon Inventory Management Model with Partial Backlogging Considering Deterioration Cost. Journal of System Management, 3(3), 67-83.
    37.
  37. Ojha, D., Sahin, F., Shockley, J., Sridharan, S.V. (2019). Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type. International Journal of Production Economics, 208, 529-543. org/10.1016/j.ijpe.2018.12.021
    38.
  38. Pastore, E., Alfieri, A., Zotteri, G., Boylan, J.E. (2020). The impact of demand parameter uncertainty on the bullwhip effect. European Journal of Operational Research, 283 (1), 94-107. org/10.1016/j.ejor.2019.10.031
    39.
  39. Pastore, E., Alfieri, A., Zotteri, G. (2019). An empirical investigation on the antecedents of the bullwhip effect: Evidence from the spare parts industry. International Journal of Production Economics, 209, 121-133. org/10.1016/j.ijpe.2017.08.029
    40.
  40. Ponte, B., Framinan, J.M., Cannella, S., Dominguez, R. (2020). Quantifying the Bullwhip Effect in closed-loop supply chains: The interplay of information transparencies, return rates, and lead times. International Journal Production Economics, 230, 107798. org/10.1016/j.ijpe.2020.107798
    41.
  41. Potter, A., Disney, S.M. (2006). Bullwhip and batching: An exploration. International Journal of Production Economics, 104 (2), 408-418. org/10.1016/j.ijpe.2004.10.018
    42.
  42. Reiner, G., Fichtinger, J. (2009). Demand forecasting for supply processes in consideration of pricing and market information. International Journal of Production Economics, 118(1), 55-62. org/10.1016/j.eswa.2006.01.032
    43.
  43. Rodríguez Repiso, L., Setchi, R., Salmeron, J. (2007). Modelling IT projects success with Fuzzy Cognitive Maps. Expert Systems with Applications, 32, 543-559. org/10.1016/j.eswa.2006.01.032
    44.
  44. TamjidYamcholo, A., & Toloie Eshlaghy, A. (2022). Subjectivity Reduction of Qualitative Approach in Information Security Risk Analysis. Journal of System Management, 8(1), 145-166. doi: 10.30495/jsm.2022.1945866.1578
    45.
  45. Trapero, J.R., Pedregal, D.J. (2016). A novel time-varying bullwhip effect metric: An application to promotional sales. International Journal of Production Economics, 182, 465–471. org/10.1016/j.ijpe.2016.10.004
    46.
  46. Wang, X., Disney, S.M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operation Research, 250 (3), 691–701. org/10.1016/j.ejor.2015.07.022
    47.
  47. Wu, W.W. (2012). Segmenting critical factors for successful knowledge management implementation using the fuzzy DEMATEL method. Applied Soft Computing, 12(1), 527-535. org/10.1016/j.asoc.2011.08.008
    48.
  48. Yadegari Taheri, T., Vakil Alroaia, Y., Faezi Razi, F., Heydariyeh, S.A. (2021). Identification and Prioritization of Organizational Intelligence Criteria in Production Cooperatives of Iran. Journal of System Management, 7 (4), 205-227. doi: 10.30495/jsm.2021.1942062.1531
    49.
  49. Yuan, X.G., Zhu, N. (2016). Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology. Asia-Pacific Journal of Operational Research, 33(3), 1650016. org/10.1142/S0217595916500160
    50.
  50. Zhang, X., Burke, G.J. (2011). Analysis of compound bullwhip effect causes. European Journal of Operational Research, 210(3), 514-526. org/10.1016/j.ejor.2010.09.030
    51.
  51. Zhao, Y., Cao, Y., Li, H., Wang, S., Liu, Y., Li, Y., Zhang, Y. (2018). Bullwhip effect mitigation of green supply chain optimization in electronics industry. Journal of Cleaner Production, 180, 888-912. org/10.1016/j.jclepro.2018.01.134
    52.
  52. Zhao, S., Zhu, Q. (2018). A risk-averse marketing strategy and its effect on coordination activities in a remanufacturing supply chain under market fluctuation. Journal of Cleaner production, 171, 1290-1299. org/10.1016/j.jclepro.2017.10.107
    53.
  53. Zhou, L., Naim, M.M., Disney, S.M. (2017). The impact of product returns and remanufacturing uncertainties on the dynamic performance of a multi-echelon. International Journal of Production Economics, 183, 487-502. org/10.1016/j.ijpe.2016.07.021
    54.
  54. Zhou, Q., Huang, W., Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety science, 49 (2), 243-252. org/10.1016/j.ssci.2010.08.005
    55.
  55. Zhu, T., Balakrishnan, J., da Silveira, G.J.C. (2020). Bullwhip Effect in the Oil and Gas Supply Chain: A Multiple-case Study. International Journal of Production Economics, 224, 107548. org/10.1016/j.ijpe.2019.107548
    56.
  56. Zotteri, G. (2013). An empirical investigation on causes and effects of the Bullwhip-effect: Evidence from the personal care sector. International Journal of Production Economics, 143 (2), 489-498. org/10.1016/j.ijpe.2012.06.006
    57.

 

Related articles

The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024

Home| Login| About Us| Contact Us|
[فارسی] [العربية] [fa] [ar]