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Knowledge diffusion of supply chain bullwhip effect: main path analysis and science mapping analysis

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Abstract

Bullwhip effect (BWE), as a demand amplification phenomenon in supply chain, has attracted widespread interest from researchers in the past few decades, and the literature in this field has also increased. Therefore, it is necessary to explore the development of BWE by bibliometrics. This paper focuses on exploring knowledge diffusion and thematic evolution in BWE. 522 articles are analyzed through main path analysis and science mapping analysis. By main path analysis, this paper finds that most studies focus on investigating the causes and mitigation strategies of BWE. To show the strategic diagrams and conceptual evolution of BWE, three consecutive periods are chosen. The paper identifies that the BWE researches focus on seven main thematic areas, i.e., ordering-policies, inventory-variance-and-control-chart, information-sharing-and-simulation, inventory, strategies, inventory-control-system-and-lead-time and divergent-supply-chain. This paper comprehensively analyzes the development process of this field, which is helpful for scholars to grasp the development of this domain and draw their inspiration.

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References

  • Babai, M. Z., Boylan, J. E., Syntetos, A. A., & Ali, M. M. (2016). Reduction of the value of information sharing as demand becomes strongly auto-correlated. International Journal of Production Economics, 181, 130–135.

    Article  Google Scholar 

  • Bai, Y., Li, H., & Liu, Y. (2020). Visualizing research trends and research theme evolution in E-learning field: 1999–2018. Scientometrics, 126(2), 1389–1414.

    Article  Google Scholar 

  • Batagelj, V. (2003). Efficient algorithms for citation network analysis. Retrieved from https://arxiv.org/abs/cs.DL/0309023.

  • 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.

    Article  MathSciNet  MATH  Google Scholar 

  • Braz, A. C., De Mello, A. M., de Vasconcelos Gomes, L. A., & de Souza Nascimento, P. T. (2018). The bullwhip effect in closed-loop supply chains: A systematic literature review. Journal of Cleaner Production, 202, 376–389.

    Article  Google Scholar 

  • Campuzano-Bolarín, F., Frutos, A. G., Abellón, M. D. C. R., & Lisec, A. (2013). Alternative forecasting techniques that reduce the bullwhip effect in a supply chain: A simulation study. Promet-Traffic & Transportation, 25(2), 177–188.

    Article  Google Scholar 

  • Cannella, S. (2014). Order-up-to policies in information exchange supply chains. Applied Mathematical Modelling, 38(23), 5553–5561.

    Article  MathSciNet  MATH  Google Scholar 

  • Cannella, S., & Ciancimino, E. (2010). On the bullwhip avoidance phase: Supply chain collaboration and order smoothing. International Journal of Production Research, 48(22), 6739–6776.

    Article  MATH  Google Scholar 

  • Cantor, D. E., & Katok, E. (2012). Production smoothing in a serial supply chain: A laboratory investigation. Transportation Research Part e: Logistics and Transportation Review, 48(4), 781–794.

    Article  Google Scholar 

  • Carmen, B. (2008). The effects of the lack of coordination within the supply chain. The Amfiteatru Economic Journal, 10(24), 26–40.

    Google Scholar 

  • Cetinkaya, S., & Lee, C. Y. (2000). Stock replenishment and shipment scheduling for vendor-managed inventory systems. Management Science, 46(2), 217–232.

    Article  MATH  Google Scholar 

  • Chatfield, D. C., Kim, J. G., Harrison, T. P., & Hayya, J. C. (2004). The bullwhip effect—impact of stochastic lead time, information quality, and information sharing: A simulation study. Production and Operations Management, 13(4), 340–353.

    Article  Google Scholar 

  • Chatfield, D. C., & Pritchard, A. M. (2013). Returns and the bullwhip effect. Transportation Research Part e: Logistics and Transportation Review, 49(1), 159–175.

    Article  Google Scholar 

  • Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000a). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46(3), 436–443.

    Article  MATH  Google Scholar 

  • Chen, F., Ryan, J. K., & Simchi-Levi, D. (2000b). The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics, 47(4), 269–286.

    Article  MathSciNet  MATH  Google Scholar 

  • Ciancimino, E., Cannella, S., Bruccoleri, M., & Framinan, J. M. (2012). On the bullwhip avoidance phase: The synchronised supply chain. European Journal of Operational Research, 221(1), 49–63.

    Article  MATH  Google Scholar 

  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.

    Article  Google Scholar 

  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630.

    Article  Google Scholar 

  • Cobo, M. J., Martínez, M. Á., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: A bibliometric analysis. Knowledge-Based Systems, 80(may), 3–13.

    Article  Google Scholar 

  • Costa, A., Cannella, S., Corsini, R. R., Framinan, J. M., & Fichera, S. (2020). Exploring a two-product unreliable manufacturing system as a capacity constraint for a two-echelon supply chain dynamic problem. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1852480

    Article  Google Scholar 

  • 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.

    Article  MATH  Google Scholar 

  • Disney, S. M., Farasyn, I., Lambrecht, M. R., Towill, D. R., & Van De Velde, W. (2007). Controlling bullwhip and inventory variability with the golden smoothing rule. European Journal of Industrial Engineering, 1(3), 241–265.

    Article  Google Scholar 

  • Disney, S. M., Ponte, B., & Wang, X. (2020). Exploring the nonlinear dynamics of the lost-sales order-up-to policy. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1790687

    Article  Google Scholar 

  • Disney, S. M., & Towill, D. R. (2003). On the bullwhip and inventory variance produced by an ordering policy. Omega, 31(3), 157–167.

    Article  Google Scholar 

  • Dominguez, R., Framinan, J. M., & Cannella, S. (2014). Serial vs. divergent supply chain networks: A comparative analysis of the bullwhip effect. International Journal of Production Research, 52(7), 2194–2210.

    Article  Google Scholar 

  • Dominguez, R., Ponte, B., Cannella, S., & Framinan, J. M. (2019). On the dynamics of closed-loop supply chains with capacity constraints. Computers & Industrial Engineering, 128, 91–103.

    Article  Google Scholar 

  • Du, H. S., Xu, J., Li, Z., Liu, Y., & Chu, S. K. W. (2020). Bibliometric mapping on sustainable development at the base-of-the-pyramid. Journal of Cleaner Production, 281, 125290.

    Article  Google Scholar 

  • Elisabetta, R., Gabriele, M., Massimo, S., Sofia, B., & Giuseppe, S. M. (2018). Structural trend and conceptual evolution of research on genetically modified organisms using a science mapping approach. Journal of Cleaner Production, 205, 329–338.

    Article  Google Scholar 

  • Forrester, J. W. (1958). Industrial dynamics. A major breakthrough for decision makers. Harvard Business Review, 36(4), 37–66.

    Google Scholar 

  • Holgado de Frutos, E., Trapero, J. R., & Ramos, F. (2020). A literature review on operational decisions applied to collaborative supply chains. PloS One, 15(3), e0230152.

    Article  Google Scholar 

  • Hosoda, T., & Disney, S. M. (2006). On variance amplification in a three-echelon supply chain with minimum mean square error forecasting. Omega, 34(4), 344–358.

    Article  Google Scholar 

  • Hummon, N. P., & Dereian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63.

    Article  Google Scholar 

  • Kim, J. G., Chatfield, D., Harrison, T. P., & Hayya, J. C. (2006). Quantifying the bullwhip effect in a supply chain with stochastic lead time. European Journal of Operational Research, 173(2), 617–636.

    Article  MathSciNet  MATH  Google Scholar 

  • Kim, I., & Springer, M. (2008). Measuring endogenous supply chain volatility: Beyond the bullwhip effect. European Journal of Operational Research, 189(1), 172–193.

    Article  MathSciNet  MATH  Google Scholar 

  • Kouvelis, P., Chambers, C., & Wang, H. (2006). Supply chain management research and production and operations management: Review, trends, and opportunities. Production and Operations Management, 15(3), 449–469.

    Article  Google Scholar 

  • Lee, H. L., Padmanabhan, V., & Whang, S. (1997a). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558.

    Article  MATH  Google Scholar 

  • Lee, H. L., Padmanabhan, V., & Whang, S. (1997b). The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102.

    MATH  Google Scholar 

  • Lin, J., Naim, M. M., Purvis, L., & Gosling, J. (2017). The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015. International Journal of Production Economics, 194, 135–152.

    Article  Google Scholar 

  • Lin, J., Spiegler, V. L., & Naim, M. M. (2018). Dynamic analysis and design of a semiconductor supply chain: A control engineering approach. International Journal of Production Research, 56(13), 4585–4611.

    Article  Google Scholar 

  • Liu, J. S., & Lu, L. Y. Y. (2012). An integrated approach for main path analysis: Development of the Hirsch index as an example. Journal of the American Society for Information Science and Technology, 63(3), 528–542.

    Article  Google Scholar 

  • Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15.

    Article  Google Scholar 

  • Ma, J., & Bao, B. (2017). Research on bullwhip effect in energy-efficient air conditioning supply chain. Journal of Cleaner Production, 143, 854–865.

    Article  Google Scholar 

  • Maltseva, D., & Batagelj, V. (2019). Social network analysis as a field of invasions: Bibliographic approach to study SNA development. Scientometrics, 121(2), 1085–1128.

    Article  Google Scholar 

  • Meyer, P. S., Yung, J. W., & Ausubel, J. H. (1999). A primer on logistic growth and substitution: The mathematics of the Loglet Lab software. Technological Forecasting and Social Change, 61(3), 247–271.

    Article  Google Scholar 

  • Mishra, D., Dwivedi, Y. K., Rana, N. P., & Hassini, E. (2019). Evolution of supply chain ripple effect: A bibliometric and meta-analytic view of the constructs. International Journal of Production Research, 59(1), 1–19.

    Article  Google Scholar 

  • Murgado-Armenteros, E. M., Gutiérrez-Salcedo, M., Torres-Ruiz, F. J., & Cobo, M. J. (2014). Analysing the conceptual evolution of qualitative marketing research through science mapping analysis. Scientometrics, 102(1), 519–557.

    Article  Google Scholar 

  • Nachiappan, S. P., & Jawahar, N. (2007). A genetic algorithm for optimal operating parameters of VMI system in a two-echelon supply chain. European Journal of Operational Research, 182(3), 1433–1452.

    Article  MATH  Google Scholar 

  • Pasandideh, S. H. R., Niaki, S. T. A., & Nia, A. R. (2011). A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model. Expert Systems with Applications, 38(3), 2708–2716.

    Article  Google Scholar 

  • Paule-Vianez, J., Gómez-Martínez, R., & Prado-Román, C. (2020). A bibliometric analysis of behavioral finance with mapping analysis tools. European Research on Management and Business Economics., 26(2), 71–77.

    Article  Google Scholar 

  • 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 of Production Economics, 230, 107798.

    Article  Google Scholar 

  • Ponte, B., Wang, X., de la Fuente, D., & Disney, S. M. (2017). Exploring nonlinear supply chains: The dynamics of capacity constraints. International Journal of Production Research, 55(14), 4053–4067.

    Article  Google Scholar 

  • Priore, P., Ponte, B., Rosillo, R., & de la Fuente, D. (2019). Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. International Journal of Production Research, 57(11), 3663–3677.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shiau, W. L., Dwivedi, Y. K., & Tsai, C. H. (2015). Supply chain management: Exploring the intellectual structure. Scientometrics, 105(1), 215–230.

    Article  Google Scholar 

  • Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799–813.

    Article  Google Scholar 

  • Sourirajan, K., Ramachandran, B., & An, L. (2008). Application of control theoretic principles to manage inventory replenishment in a supply chain. International Journal of Production Research, 46(21), 6163–6188.

    Article  MATH  Google Scholar 

  • Springer, M., & Kim, I. (2010). Managing the order pipeline to reduce supply chain volatility. European Journal of Operational Research, 203(2), 380–392.

    Article  MATH  Google Scholar 

  • Sterman, J. D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35(3), 321–339.

    Article  Google Scholar 

  • Turrisi, M., Bruccoleri, M., & Cannella, S. (2013). Impact of reverse logistics on supply chain performance. International Journal of Physical Distribution & Logistics Management, 43(7), 564–585.

    Article  Google Scholar 

  • Waller, M. A., Williams, B. D., & Eroglu, C. (2008). Hidden effects of variable order review intervals in inventory control. International Journal of Physical Distribution & Logistics Management, 38(3–4), 244–258.

    Article  Google Scholar 

  • Wan, X., & Evers, P. T. (2011). Supply chain networks with multiple retailers: A test of the emerging theory on inventories, stockouts, and bullwhips. Journal of Business Logistics, 32(1), 27–39.

    Article  Google Scholar 

  • Wang, X., & Disney, S. M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operational Research, 250(3), 691–701.

    Article  MathSciNet  MATH  Google Scholar 

  • Xu, S., Hao, L., An, X., Pang, H., & Li, T. (2020). Review on emerging research topics with key-route main path analysis. Scientometrics, 122(1), 607–624.

    Article  Google Scholar 

  • Yu, D., & He, X. (2020). A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268, 115048.

    Article  Google Scholar 

  • Yu, D., & Pan, T. (2020). Tracing knowledge diffusion of topsis: A historical perspective from citation network. Expert Systems with Applications, 168(2), 114238.

    Google Scholar 

  • Yu, D., & Sheng, L. (2020). Knowledge diffusion paths of blockchain domain: The main path analysis. Scientometrics, 125(1), 471–497.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Acknowledgements

This manuscript was supported by the Ministry of Education of Humanities and Social Science Project (No. 19YJC630208), the Qinglan Project of Jiangsu Province (2019) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. SJCX21_0883).

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  1. Business School, Nanjing Audit University, Nanjing, 211815, Jiangsu, China

    Dejian Yu & Zhaoping Yan

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  1. Dejian Yu
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Yu, D., Yan, Z. Knowledge diffusion of supply chain bullwhip effect: main path analysis and science mapping analysis. Scientometrics 126, 8491–8515 (2021). https://doi.org/10.1007/s11192-021-04105-8

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