Effectiveness Of Whipping Effect in Supply Chains of the Iranian Defense Industry and Modeling the Reduction of Its Effects Based on the Method of System Dynamics in the Direction of Resistance Economics

Document Type : Original Article

Authors

1 Ph.D. Candidate,Department of Management, Faculty of Management,Tabriz Branch, Islamic Azad University, Tabriz, Iran

2 Corresponding Author: Assistant Professor,,Department of Management, Faculty of Management,Tabriz Branch, Islamic Azad University, Tabriz, Iran

3 Assistant Professor,,Department of Management, Faculty of Management,Tabriz Branch, Islamic Azad University, Tabriz, Iran

Abstract

Considering the high importance of supply chains and their fundamental role in defense industries, improving the effect of the amount in reducing costs and increasing the performance of the defense industry of the Islamic Republic of Iran is undeniable. The present study tries to obtain an approach to improve the performance of the supply chain. To this end, the effect of pneumatic phenomenon on multi-dimensional supply chains was calculated based on a small time series and its mathematical model was obtained. Then, on the effect of the order period, the purpose of the order and the net storage value of the target was discussed and the stable and unstable areas of the equations were obtained. Also, the effect of pneumatic phenomenon on supply chains was calculated using simulation and attitude of time series based on appropriate algorithms. To validate the problem, the results were compared with a case study. Also, the effect of pneumatic phenomenon on the current case study, optimized by optimizer software. As a result, it turned out that the use of a time series model as a demand pattern and daily inventory inspection of distributor and shipping on a daily basis for completing their inventory reduces the decrease in the pure effect in the defense industries of the Islamic Republic of Iran. In addition the findings of the research indicated that the whipping error extent is variable between 0.17 and 11/40.The average error extent for the distribution and production sections is and 7.34.

Keywords

Smiley face

References

  1. Boute, R. N., Disney, S. M., Lambrecht, M. R., & Van Houdt, B. (2007). An integrated production and inventory model to dampen upstream demand variability in the supply chain. European journal of operational research, 178(1), 121-142.
  2. Cachon, G. P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management science, 46(8), 1032-1048.
  3. Chilmon, B., & Tipi, N. S. (2020). Modelling and simulation considerations for an end-to-end supply chain system. Computers & Industrial Engineering, 150, 106870.
  4. Dejonckheere, J., Disney, S. M., Lambrecht, M. R., & Towill, D. R. (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European journal of operational research, 147(3), 567-590.
  5. Disney, S. M., & Towill, D. R. (2003). On the bullwhip and inventory variance produced by an ordering policy. Omega, 31(3), 157-167.
  6. Fransoo, J. C., & Wouters, M. J. F. (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management: An International Journal.
  7. Haeussler, S., Stefan, M., Schneckenreither, M., & Onay, A. (2021). The lead time updating trap: Analyzing human behavior in capacitated supply chains. International Journal of Production Economics, 234, 108034.
  8. He, L., Xue, M., & Gu, B. (2020). Internet-of-things enabled supply chain planning and coordination with big data services: Certain theoretic implications. Journal of Management Science and Engineering, 5(1), 1-22.
  9. Kholidasari, I., Bidiawati, J. A., & Sari, M. E. (2019). The evaluation of bullwhip effect on distribution system of a supply chain using centralized demand information method. Paper presented at the IOP Conference Series: Materials Science and Engineering.
  10. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Sloan management review, 38, 93-102.
  11. Ma, Y., & Li, W. (2019). Application and research of fractional differential equations in dynamic analysis of supply chain financial chaotic system. Chaos, Solitons & Fractals, 130, 109417.
  12. Miragliotta, (2006). Layers and mechanisms: A new taxonomy for the bullwhip effect. International journal of production economics, 104(2), 365-381.
  13. Pereira, M. M., & Frazzon, E. M. (2021). A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains. International Journal of Information Management, 57, 102165.
  14. Ponte, B., Cannella, S., Dominguez, R., Naim, M. M., & Syntetos, A. A. (2021). Quality grading of returns and the dynamics of remanufacturing. International Journal of Production Economics, 236, 108129.
  15. Riddalls, C. E., & Bennett, S. (2001). The optimal control of batched production and its effect on demand amplification. International journal of production economics, 72(2), 159-168.
  16. Shaban, A., Shalaby, M. A., Di Gravio, G., & Patriarca, R. (2020). Analysis of Variance Amplification and Service Level in a Supply Chain with Correlated Demand. Sustainability, 12(16), 6470.
  17. Taylor, D. H. (2000). Demand amplification: has it got us beat? International Journal of Physical Distribution & Logistics Management.
  18. Wangphanich, P., Kara, S., & Kayis, B. (2010). Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems–a simulation approach. International journal of production Research, 48(15), 4501-4517.
  19. Warburton, R. D. H. (2004). An analytical investigation of the bullwhip effect. Production and operations management, 13(2), 150-160.
  20. Yang, Y., Lin, J., Liu, G., & Zhou, L. (2021). The behavioural causes of bullwhip effect in supply chains: A systematic literature review. International Journal of Production Economics, 236, 108120.

 

Defensive Researches and Management
Volume 20, Issue 93
Autumn Quarterly
November 2021
Pages 55-72

Files

History

  • Receive Date: 03 October 2021
  • Revise Date: 22 November 2021
  • Accept Date: 06 December 2021

Share

How to cite

Statistics