The Impact of Time and Time Delay on the Bullwhip Effect in Supply Chains

Document Type : Research Paper


1 Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

2 Department of Management, Meshkinshahr Branch, Islamic Azad University, Meshkinshahr, Iran.



Modeling and measuring the demand variance propagation from the initial hours of the supply chain (SC) to the final hours is one of the most important challenges facing the logestics experts. Factors such as time delay increase demand fluctuations over the time in SC networks. This problem often referred to the Bullwhip Effect (BWE) in production systems. In this paper, a flight scheduling network as a supply chain are designed using Inverse Data Envelopment Analysis (IDEA). The effects of the arrival time of the aircraft (landing time) to the airport, as well as the delayed flights (depart time with delay) to the next destination on the demand variance were examined. The results show that demand fluctuations increased significantly by delaying flights in the closing hours. Also, the bullwhip effect due to landing time, at the beginning of the day are more than the final flight hours. This means that due to the higher demand during office hours (beginning of the day), there are many fluctuations in the variance of orders. Comparing the results with the control engineering approach, we found that the proposed method shows the hidden points (effect of flight shifts on involuntary oscillations) of the size of the bullwhip effect. While these hidden points have not been identified in previous methods.


Amirteioori, A., Khoshandam, L. (2011). A Data Envelopment Analysis Approach to Supply Chain Efficiency. Advances in Decision Sciences. V. 2011, 608324, 8 pages, doi:10.1155/2011/608324.
Asif F M, Bianchi C, A, Rashid & Niclescu C, M. (2012). Performance analysis of the closed - loop supply chain. Journal remanufacturing.vol.2, no.1, pp.1-21.
Aslani Khiavi, S., Skandari, S. (2021). The Design of Inverse Network DEA Model for  Measuring the Bullwhip Effect in Supply Chains with Uncertain Demands. Journal of Optimization in Industrial Engineering, 14(2), 203-214.
Aslani Khiavi S, Khaloozadeh H, Soltanian F. (2021) Regulating Bullwhip Effect in Supply Chain with Hybrid Recycling Channels using Linear Quadratic Gaussian Controller. IJIEPR; 32 (1) :13-27.
Beatty, R., Hsu, R., Berry, L., Rome, J., (1998). Preliminary Evaluation of Flight Delay Propagation through an Airline Schedule. In: Proceedings of the 2nd USA Europe Air Traffic Management R &D Seminar, Orlando, Fl.
Cannella, S., LoĢpez-Campos, M., Dominguez, R., Ashayeri, J. and Miranda, P.A., (2015). A simulation model of a coordinated decentralized supply chain. International Transactions in Operational Research, 22(4), pp. 735-756.
Costantino, F., Di Gravio, G., Shaban, A. and Tronci, M., (2015). The impact of information sharing on ordering policies to improve supply chain performances. Computers & Industrial Engineering, 82, pp.127-142.
Dejonckheere, J., Disney, S., Lambrecht, M, N., Towill, D, R. (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European journal of operational research, vol.147, pp.567-590.
Disney, S, M., Towill, D, R. (2003). On the bullwhip and the inventory variance produced by an ordering policy. Omega. vol. 31, pp.157-167.
Dejonckheere, J., Disney, S.M., Lambrecht, M.R. and Towill, D.R., (2004). The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective. European Journal of Operational Research, 153(3), pp. 727-750.
Duc, T.T.H., Luong, H.T. and Kim, Y.D., (2008a). A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process. European Journal of Operational Research, 187(1), pp. 243-256.
Duc, T.T.H., Luong, H.T. and Kim, Y.D., (2008b). A measure of the bullwhip effect in supply chains with stochastic lead time. International Journal of Advanced Manufacturing Technology, 38(11-12), pp. 1201-1212.
Fu, D., Ionescu, C., Aghezzaf, E.H. and 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, pp.46-57.
Forrester, J.W. (1958). Industrial Dynamics, A major breakthrough for decision makers, Harvard Business Review, pp. 67-96.
Goodarzi, M., Farzipoor, R, (2018). How to measure bullwhip effect by network data envelopment analysis? Accepted in Computers & Industrial Engineering
Hashemi, V., Chen, M., Fang, L. (2014). Process planning for closed-loop aerospace manufacturing supply chain and environmental impact reduction. Computers & Industrial Engineering, 75; 87–95.
Ho, k., Weck, O., Hoffman, J., Shishko, R. (2014).Dynamic modeling and optimization for space logistics using time-expanded networks. Acta Astronautica, 105; 428–443.
Kao, C., Hwang, S.N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan, European Journal of Operational Research, 185, 418–429.
Kao, C., Liu, S.T. (2011). Efficiencies of two-stage systems with fuzzy data. Fuzzy Sets and Systems, 176, 20–35.
Kafle, N., Zou, B. (2016). Modeling flight delay propagation: A new analytical econometric approach. Transportation Research Part B, 93; 520-542.
Khalili, K., Shahmir, Z., (2015). Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes. Computers & Industrial Engineering,
Kim, J.G., Chatfield, D., Harrison, T.P. and Hayya, J.C., (2006). Quantifying the bullwhip effect in a supply chain with stochastic lead time. European Journal of Operational Research, 173(2), pp. 617-636.
Li, Y., Chen, Y., Liang, L., Xie, J. (2012). DEA models for extended two stage network structures, Omega. 40(5), 611–618.
Khiavi, S.A., Khaloozadeh, H., Soltanian, F. (2019). Nonlinear modelling and performance analysis of a closed-loop supply chain in the presence of stochastic noise. Mathematical and Computer Modelling of Dynamical Systems 25 (5), 499-521.
Khiavi, S.A., Hashemzadeh, F., Khaloozadeh, H. (2021).  Sensitivity analysis of the bullwhip effect in supply chains with time delay, International Journal of Systems Science: Operations & Logistics, DOI: 10.1080/23302674.2021.1968064.
Khiavi, S.A., Khaloozadeh, H. & Soltanian, F. (2021). Suboptimal sliding manifold For nonlinear supply chain with time delay. J Comb Optim 42, 151–173.
Li T & Ma J (2014) Complexity analysis of the dual-channel supply chain model with delay decision. Nonlinear Dyn, 78, 2617–2626.
Liang, L., Cook, W.D., Zhu, J. (2008). DEA models for two-stage processes: game approach and efficiency decomposition, Naval Research Logistics, 55, 643–53.
Moreno, P., Lozano, S. (2014). A network DEA assessment of team efficiency in the NBA. Annals of Operations Research, 214 (1), 99-124.
Olivia, J, P. and Fry, K. (2009). The Air Transportation System as a Supply Chain, AIAA Guidance, Navigation, and Control Conference, Chicago, Illinois. 10 – 13.
Pin-Ho, L., Shan-Hill, D., Shi, Shang, J. (2004). Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis. Journal of process control, vol.14, pp.487-499.
Pyrgiotis, N., Malone, K., Odoni, A. (2013). Modelling delay propagation within an airport network. Transportation Research Part C, 27; 60–75.
Rezaiei, A, H,. Adressi, A. (2015). Supply chain performance evaluation using DEA. Vol. 2. 2. 748-458.
Wang, X. and Disney, S.M., (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operational Research, 250(3), pp. 691-701.
Wu, W.,  Cheng-Lung, Wu.,  Feng, T., Zhang, H. and Qiu, Sh. (2018). Comparative Analysis on Propagation Effects of Flight Delays: A Case Study of China Airlines, Journal of Advanced Transportation. Volume 2018, Article ID 5236798, 10 pages.