IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i16p6470-d397414.html
   My bibliography  Save this article

Analysis of Variance Amplification and Service Level in a Supply Chain with Correlated Demand

Author

Listed:
  • Ahmed Shaban

    (Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt)

  • Mohamed A. Shalaby

    (Department of Mechanical Design and Production, Faculty of Engineering, Cairo University, Giza 12613, Egypt)

  • Giulio Di Gravio

    (Mechanical and Aerospace Engineering Department, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy)

  • Riccardo Patriarca

    (Mechanical and Aerospace Engineering Department, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy)

Abstract

The bullwhip effect reflects the variance amplification of demand as they are moving upstream in a supply chain, and leading to the distortion of demand information that hinders supply chain performance sustainability. Extensive research has been undertaken to model, measure, and analyze the bullwhip effect while assuming stationary independent and identically distributed (i.i.d) demand, employing the classical order-up-to (OUT) policy and allowing return orders. On the contrary, correlated demand where a period’s demand is related to previous periods’ demands is evident in several real-life situations, such as demand patterns that exhibit trends or seasonality. This paper assumes correlated demand and aims to investigate the order variance ratio (OVR), net stock amplification ratio (NSA), and average fill rate/service level (AFR). Moreover, the impact of correlated demand on the supply chain performance under various operational parameters, such as lead-time, forecasting parameter, and ordering policy parameters, is analyzed. A simulation modeling approach is adopted to analyze the response of a single-echelon supply chain model that restricts return orders and faces a first order autoregressive demand process AR(1). A generalized order-up-to policy that allows order smoothing through the proper tuning of its smoothing parameters is applied. The characterization results confirm that the correlated demand affects the three performance measures and interacts with the operating conditions. The results also indicate that the generalized OUT inventory policy should be adopted with the correlated demand, as its smoothing parameters can be adapted to utilize the demand characteristics such that OVR and NSA can be reduced without affecting the service level (AFR), implying sustainable supply chain operations. Furthermore, the results of a factorial design have confirmed that the ordering policy parameters and their interactions have the largest impact on the three performance measures. Based on the above characterization, the paper provides management with means to sustain good performance of a supply chain whenever a correlated demand pattern is realized through selecting the control parameters that decrease the bullwhip effect.

Suggested Citation

  • Ahmed Shaban & Mohamed A. Shalaby & Giulio Di Gravio & Riccardo Patriarca, 2020. "Analysis of Variance Amplification and Service Level in a Supply Chain with Correlated Demand," Sustainability, MDPI, vol. 12(16), pages 1-27, August.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6470-:d:397414
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/16/6470/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/16/6470/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jaksic, Marko & Rusjan, Borut, 2008. "The effect of replenishment policies on the bullwhip effect: A transfer function approach," European Journal of Operational Research, Elsevier, vol. 184(3), pages 946-961, February.
    2. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    3. Kim, Jeon G. & Chatfield, Dean & Harrison, Terry P. & Hayya, Jack C., 2006. "Quantifying the bullwhip effect in a supply chain with stochastic lead time," European Journal of Operational Research, Elsevier, vol. 173(2), pages 617-636, September.
    4. Zhang, Xiaolong, 2004. "The impact of forecasting methods on the bullwhip effect," International Journal of Production Economics, Elsevier, vol. 88(1), pages 15-27, March.
    5. Nesim Erkip & Warren H. Hausman & Steven Nahmias, 1990. "Optimal Centralized Ordering Policies in Multi-Echelon Inventory Systems with Correlated Demands," Management Science, INFORMS, vol. 36(3), pages 381-392, March.
    6. Pastore, Erica & Alfieri, Arianna & Zotteri, Giulio, 2019. "An empirical investigation on the antecedents of the bullwhip effect: Evidence from the spare parts industry," International Journal of Production Economics, Elsevier, vol. 209(C), pages 121-133.
    7. Chiang, Chung-Yean & Lin, Winston T. & Suresh, Nallan C., 2016. "An empirically-simulated investigation of the impact of demand forecasting on the bullwhip effect: Evidence from U.S. auto industry," International Journal of Production Economics, Elsevier, vol. 177(C), pages 53-65.
    8. Duc, Truong Ton Hien & Luong, Huynh Trung & Kim, Yeong-Dae, 2008. "A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process," European Journal of Operational Research, Elsevier, vol. 187(1), pages 243-256, May.
    9. Chatfield, Dean C. & Pritchard, Alan M., 2013. "Returns and the bullwhip effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 159-175.
    10. Frank Chen & Jennifer K. Ryan & David Simchi‐Levi, 2000. "The impact of exponential smoothing forecasts on the bullwhip effect," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(4), pages 269-286, June.
    11. Patriarca, Riccardo & Costantino, Francesco & Di Gravio, Giulio & Tronci, Massimo, 2016. "Inventory optimization for a customer airline in a Performance Based Contract," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 206-216.
    12. Zotteri, Giulio, 2013. "An empirical investigation on causes and effects of the Bullwhip-effect: Evidence from the personal care sector," International Journal of Production Economics, Elsevier, vol. 143(2), pages 489-498.
    13. Luong, Huynh Trung, 2007. "Measure of bullwhip effect in supply chains with autoregressive demand process," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1086-1097, August.
    14. Gaalman, Gerard & Disney, Stephen M., 2009. "On bullwhip in a family of order-up-to policies with ARMA(2,2) demand and arbitrary lead-times," International Journal of Production Economics, Elsevier, vol. 121(2), pages 454-463, October.
    15. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    16. Dominguez, Roberto & Cannella, Salvatore & Framinan, Jose M., 2015. "On returns and network configuration in supply chain dynamics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 73(C), pages 152-167.
    17. Patra Shovityakool & Piyachat Jittam & Namkang Sriwattanarothai & Parames Laosinchai, 2019. "A Flexible Supply Chain Management Game," Simulation & Gaming, , vol. 50(4), pages 461-482, August.
    18. Luong, Huynh Trung & Phien, Nguyen Huu, 2007. "Measure of bullwhip effect in supply chains: The case of high order autoregressive demand process," European Journal of Operational Research, Elsevier, vol. 183(1), pages 197-209, November.
    19. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & 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, Elsevier, vol. 153(3), pages 727-750, March.
    20. Disney, S.M. & Farasyn, I. & Lambrecht, M. & Towill, D.R. & de Velde, W. Van, 2006. "Taming the bullwhip effect whilst watching customer service in a single supply chain echelon," European Journal of Operational Research, Elsevier, vol. 173(1), pages 151-172, August.
    21. Sterman, John D., 1989. "Misperceptions of feedback in dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(3), pages 301-335, June.
    22. Dominguez, Roberto & Cannella, Salvatore & Barbosa-Póvoa, Ana P. & Framinan, Jose M., 2018. "OVAP: A strategy to implement partial information sharing among supply chain retailers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 122-136.
    23. Chandra, Charu & Grabis, Janis, 2005. "Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand," European Journal of Operational Research, Elsevier, vol. 166(2), pages 337-350, October.
    24. Costantino, Francesco & Di Gravio, Giulio & Patriarca, Riccardo & Petrella, Lea, 2018. "Spare parts management for irregular demand items," Omega, Elsevier, vol. 81(C), pages 57-66.
    25. Juan Huang & Yuhong Shuai & Qi Liu & Hang Zhou & Zhenggang He, 2018. "Synergy Degree Evaluation Based on Synergetics for Sustainable Logistics Enterprises," Sustainability, MDPI, vol. 10(7), pages 1-18, June.
    26. Ying Qu & Ying Yu & Andrea Appolloni & Mengru Li & Yue Liu, 2017. "Measuring Green Growth Efficiency for Chinese Manufacturing Industries," Sustainability, MDPI, vol. 9(4), pages 1-18, April.
    27. Disney, S. M. & Towill, D. R., 2003. "On the bullwhip and inventory variance produced by an ordering policy," Omega, Elsevier, vol. 31(3), pages 157-167, June.
    28. John D. Sterman, 1989. "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment," Management Science, INFORMS, vol. 35(3), pages 321-339, March.
    29. Disney, Stephen M. & Lambrecht, Marc R., 2008. "On Replenishment Rules, Forecasting, and the Bullwhip Effect in Supply Chains," Foundations and Trends(R) in Technology, Information and Operations Management, now publishers, vol. 2(1), pages 1-80, April.
    30. 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, Elsevier, vol. 147(3), pages 567-590, June.
    31. Wang, Xun & Disney, Stephen M., 2016. "The bullwhip effect: Progress, trends and directions," European Journal of Operational Research, Elsevier, vol. 250(3), pages 691-701.
    32. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    33. Paolo Priore & Borja Ponte & Rafael Rosillo & David de la Fuente, 2019. "Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments," International Journal of Production Research, Taylor & Francis Journals, vol. 57(11), pages 3663-3677, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dejian Yu & Zhaoping Yan, 2021. "Knowledge diffusion of supply chain bullwhip effect: main path analysis and science mapping analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8491-8515, October.
    2. Wensheng Yang & Yinyuan Si & Jinxing Zhang & Sen Liu & Andrea Appolloni, 2021. "Coordination Mechanism of Dual-Channel Supply Chains Considering Retailer Innovation Inputs," Sustainability, MDPI, vol. 13(2), pages 1-22, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pastore, Erica & Alfieri, Arianna & Zotteri, Giulio & Boylan, John E., 2020. "The impact of demand parameter uncertainty on the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 283(1), pages 94-107.
    2. Sodhi, ManMohan S. & Tang, Christopher S., 2011. "The incremental bullwhip effect of operational deviations in an arborescent supply chain with requirements planning," European Journal of Operational Research, Elsevier, vol. 215(2), pages 374-382, December.
    3. Wang, Xun & Disney, Stephen M., 2016. "The bullwhip effect: Progress, trends and directions," European Journal of Operational Research, Elsevier, vol. 250(3), pages 691-701.
    4. Ciancimino, Elena & Cannella, Salvatore & Bruccoleri, Manfredi & Framinan, Jose M., 2012. "On the Bullwhip Avoidance Phase: The Synchronised Supply Chain," European Journal of Operational Research, Elsevier, vol. 221(1), pages 49-63.
    5. K. Devika & A. Jafarian & A. Hassanzadeh & R. Khodaverdi, 2016. "Optimizing of bullwhip effect and net stock amplification in three-echelon supply chains using evolutionary multi-objective metaheuristics," Annals of Operations Research, Springer, vol. 242(2), pages 457-487, July.
    6. Rupesh Kumar Pati, 2014. "Modelling Bullwhip Effect in a Closed Loop Supply Chain with ARMA Demand," IIM Kozhikode Society & Management Review, , vol. 3(2), pages 149-164, July.
    7. Ponte, Borja & Costas, José & Puche, Julio & Pino, Raúl & de la Fuente, David, 2018. "The value of lead time reduction and stabilization: A comparison between traditional and collaborative supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 165-185.
    8. Gaalman, Gerard & Disney, Stephen M. & Wang, Xun, 2022. "When bullwhip increases in the lead time: An eigenvalue analysis of ARMA demand," International Journal of Production Economics, Elsevier, vol. 250(C).
    9. Junhai Ma & Xiaogang Ma, 2017. "Measure of the bullwhip effect considering the market competition between two retailers," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 313-326, January.
    10. Nepal, Bimal & Murat, Alper & Babu Chinnam, Ratna, 2012. "The bullwhip effect in capacitated supply chains with consideration for product life-cycle aspects," International Journal of Production Economics, Elsevier, vol. 136(2), pages 318-331.
    11. Chatfield, Dean C. & Pritchard, Alan M., 2013. "Returns and the bullwhip effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 159-175.
    12. Dejian Yu & Zhaoping Yan, 2021. "Knowledge diffusion of supply chain bullwhip effect: main path analysis and science mapping analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8491-8515, October.
    13. Lin, Junyi & Huang, Hongfu & Li, Shanshan & Naim, Mohamed M., 2023. "On the dynamics of order pipeline inventory in a nonlinear order-up-to system," International Journal of Production Economics, Elsevier, vol. 266(C).
    14. Dominguez, Roberto & Cannella, Salvatore & Barbosa-Póvoa, Ana P. & Framinan, Jose M., 2018. "Information sharing in supply chains with heterogeneous retailers," Omega, Elsevier, vol. 79(C), pages 116-132.
    15. Rostami-Tabar, Bahman & Disney, Stephen M., 2023. "On the order-up-to policy with intermittent integer demand and logically consistent forecasts," International Journal of Production Economics, Elsevier, vol. 257(C).
    16. Enrique Holgado de Frutos & Juan R Trapero & Francisco Ramos, 2020. "A literature review on operational decisions applied to collaborative supply chains," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-28, March.
    17. Erkan Bayraktar & Kazim Sari & Ekrem Tatoglu & Selim Zaim & Dursun Delen, 2020. "Assessing the supply chain performance: a causal analysis," Annals of Operations Research, Springer, vol. 287(1), pages 37-60, April.
    18. Ponte, Borja & Framinan, Jose M. & Cannella, Salvatore & Dominguez, Roberto, 2020. "Quantifying the Bullwhip Effect in closed-loop supply chains: The interplay of information transparencies, return rates, and lead times," International Journal of Production Economics, Elsevier, vol. 230(C).
    19. Chiang, Chung-Yean & Lin, Winston T. & Suresh, Nallan C., 2016. "An empirically-simulated investigation of the impact of demand forecasting on the bullwhip effect: Evidence from U.S. auto industry," International Journal of Production Economics, Elsevier, vol. 177(C), pages 53-65.
    20. Cannella, Salvatore & Dominguez, Roberto & Ponte, Borja & Framinan, Jose M., 2018. "Capacity restrictions and supply chain performance: Modelling and analysing load-dependent lead times," International Journal of Production Economics, Elsevier, vol. 204(C), pages 264-277.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6470-:d:397414. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.