Bullwhip Effect – Logistic Stability Examination in Serial and Arborescent Topologies with Demand Uncertainty and Delay

Przemysław Ignaciuk; Adam Dziomdziora

doi:10.52846/stccj.2021.1.1.13

Authors

Przemysław Ignaciuk Lodz University of Technology https://orcid.org/0000-0003-4420-9941
Adam Dziomdziora Lodz University of Technology https://orcid.org/0000-0002-9667-8689

DOI:

https://doi.org/10.52846/stccj.2021.1.1.13

Keywords:

Supply chain control, stability analysis, time-delay systems, Transportation Networks, Bullwhip Effect

Abstract

The paper analyzes the formation of the bullwhip effect in logistic systems as a significant threat to preserving stability in the face of non-negligible goods transport delay and uncertainty of demand and stock records. The popular order-up-to policy is selected as the method governing the goods flow. A dynamic model of entity interaction is constructed and examined, first, analytically, then in numerical tests for various scenarios of practical significance, e.g., a supply chain with external and local demand signals or real-world European goods distribution system. It has been found that the order-up-to policy does not trigger the bullwhip effect despite the delays in the goods delivery in the nominal operating conditions in supply chains. However, in networked environments, even the basic configuration triggers the bullwhip effect.

References

J. Forrester, “Industrial dynamics: A major breakthrough for decision makers,” Harvard Business Review, vol. 36, pp. 37–66, 1958.

J. Kahn, “Inventories and the volatility of production,” The American Economy Review, vol. 77, no. 4, pp. 667–679, 1987.

M. Baganha and M. Cohen, “The stabilizing effect of inventory in supply chains,” Operations Research, vol 36, no. 3, pp. S72–S83, 1995.

H. Lee, P. Padmanabhan, and S. Whang, “The bullwhip effect in supply chains,” Sloan Management Review, vol. 38, no.3, pp. 93–102, 1997.

H. Lee, P. Padmanabhan, and S. Whang, “Information distortion in a supply chain: The bullwhip effect,” Management Science, vol. 43, no. 4, pp. 546–558, 1997.

R. Croson and K. Donohue, “Behavioral causes of the bullwhip effect and the observed value of inventory information,” Management Science, vol. 52, no. 3, pp. 323–336, 2006.

S. Geary, S. Disney, and D. Towill, “On bullwhip in supply chains - Historical review, present practice and expected future impact,” International Journal of Production Economics, vol. 101, no. 1, pp. 2–18, 2006.

G. Miragliotta, “Layers and mechanisms: A new taxonomy for the bullwhip effect,” International Journal of Production Economics, vol. 104, no. 2, pp. 365–381, 2006.

M. Naim, L. Spiegler, J. Wikner, and D.R. Towill, “Identifying the causes of the bullwhip effect by exploiting control block diagram manipulation with analogical reasoning,” European Journal of Operational Research, vol. 263, no. 1, pp. 240–246, 2017.

M. Naim, L. Spiegler, J. Wikner, and D.R. Towill, “Dynamic analysis and design of a semiconductor supply chain: a control engineering approach,” International Journal of Production Research, vol. 56, no. 13, pp. 4585–4611, 2018.

A. Shaban and M. Shalaby, “Modeling and optimizing of variance amplification in supply chain using response surface methodology,” Computers and Industrial Engineergin, vol. 120, pp. 392–400, 2018.

S. Gupta and A. Saxena, “Classification of operational and financial variables affecting the bullwhip effect in Indian sectors: A machine learning approach,” Recent Patents on Computer Science, vol. 12, no. 3, pp. 171–179, 2019.

M. Boccadoro, F. Martinelli, and P. Valigi, “Supply chain management by H-infinity control,” IEEE Transactions on Automation Science and Engineering, vol. 5, no. 4, pp. 703–707, 2008.

P. Ignaciuk, “Nonlinear inventory control with discrete sliding modes in systems with uncertain delay,” IEEE Transactions on Industrial Informatics, vol. 10, no. 1, pp. 559–568, 2014.

P. Ignaciuk, “Discrete inventory control in systems with perishable goods – a time-delay system perspective,” IET Control Theory & Applications, vol. 8, no. 1, pp. 11–21, 2014.

A. Bartoszewicz and P. Latosinski, “Reaching law based discrete time sliding mode inventory management strategy,” IEEE Access, vol. 4, pp. 10051–10058, 2016.

P. Latosiński and A. Bartoszewicz, “Inventory management strategies with higher relative degree sliding variables,” in 2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017, 2017, pp. 425–430.

R. Dominguez, S. Cannella, and J. M. Framinan, “The impact of the supply chain structure on bullwhip effect,” Applied Mathematical Modelling, vol. 39, pp. 7309–7325, 2015.

P. Ignaciuk and Ł. Wieczorek, “Application of continuous genetic algorithms for optimization of logistic networks governed by order-up-to inventory policy,” International Journal of New Computer Architectures and their Applications, vol. 7, no. 1, pp. 29–36, 2017.

P. Ignaciuk and Ł. Wieczorek, “Optimization of mesh-type logistic networks for achieving max service rate under order-up-to inventory policy,” Advances in Intelligent Systems and Computing, vol. 657, pp. 118–127, 2018.

P. Ignaciuk and Ł. Wieczorek, “Order-up-to networked policy for periodic-review goods distribution systems with delay,” in 2015 12th International Conference on Informatics in Control, Automation and Robotics, Proceedings, 2015, vol. 2, pp. 498–503.

P. Ignaciuk and A. Dziomdziora, “Bullwhip effect - supply chain stability examination in the presence of demand uncertainty and delay”, in 2020 24th International Conference on System Theory, Control and Computing, Proceedings, 2020, pp. 624–629.

P.H. Zipkin, Foundations of Inventory Management, McGraw-Hill, New York, 2000.

S. Axsäter, Inventory Control, Springer, Switzerland, 2015.

P. Ignaciuk and Ł. Wieczorek, “Networked base-stock inventory control in complex distribution systems,” Mathematical Problems in Engineering, vol. 2019, pp. 1–14, 2019.

J. Dejonckheere, S.M. Disney, M.R. Lambrecht, and D.R. Towill, “Measuring and avoiding the bullwhip effect: A control theoretic approach,” European Journal of Operational Research, vol. 147, no. 3, pp. 567–590, 2003.

F. Chen, Z. Drezner, J. Ryan, and D. Simchi-Levi, "Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information,” Management Science, vol. 46, no. 3, pp. 436–443, 2000.

P. Ignaciuk, “State-space modeling and analysis of order-up-to goods distribution networks with variable demand and positive lead time,” in Advances in Intelligent Systems and Computing, 2017, vol. 524, pp. 55–65.

T. Niranjan, M. Wagner, and V. Aggarwal, “Measuring information distortion in real-world supply chains,” International Journal of Production Research, vol. 49, no. 11, pp. 3343–3362, 2011.

P. Ignaciuk, “LQ optimal and robust control of perishable inventory systems with multiple supply options,” IEEE Transactions on Automatic Control, vol. 58, no. 8, pp. 2108–2113, 2013.

A. Pugliese, and R. Nilchiani, “Developing spectral structural complexity metrics,” IEEE Systems Journal, vol. 13 no. 4, pp. 3619–3626, 2019.

Z. Michna, S.M. Disney, P. Nielsen, “The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts,” Omega, vol. 93, Article ID 102033, 2020.

D. Ivanov, A. Dolgui, and B. Sokolov, Handbook of Ripple Effects in the Supply Chain, Springer, Switzerland, 2019.

P. Ignaciuk and A. Dziomdziora, “Quantifying the bullwhip effect in networked structures with nontrivial topologies,” in ACM International Conference Proceeding Series, May 2020, pp. 62–66.