From Variability to Visibility: Mitigating the Bullwhip Effect in Modern Supply Chains
DOI:
https://doi.org/10.61194/sijl.v2i2.619Keywords:
Bullwhip Effect, Inventory Management, Supply Chain Resilience, Demand Forecasting, Information Sharing, Distribution Strategy, Supply Chain TechnologyAbstract
The bullwhip effect (BWE) remains a significant challenge in supply chain management, marked by demand amplification that disrupts inventory systems and reduces efficiency. This narrative review explores the causes, systemic factors, and mitigation strategies of BWE, based on literature from 2015 to 2024 sourced from Scopus and Google Scholar. Key contributors to BWE include lead time variability, inventory inaccuracies, and behavioral decision-making, alongside the roles of distribution structures and ordering policies. Technological solutions such as neural networks, blockchain, RFID, and ERP systems help reduce BWE by improving forecasting and visibility. Additionally, practices like cross-docking, vendor-managed inventory, and real-time data sharing support inventory stability and coordination. The review emphasizes the need for integrated tech-driven and behavioral approaches, advocating for interdisciplinary, longitudinal, and sector-specific future research to build more resilient supply chains. These insights are vital for organizations aiming to improve performance, cut costs, and adapt to global market complexities.
References
Al-Khazraji, Z., Rahman, A., & Faez, M. (2017). An analytical model of the bullwhip effect in manufacturing supply chains. Journal of Production Economics and Control, 5(2), 66–84.
Asante, R., Boateng, E., & Mensah, A. (2023). Blockchain implementation and inventory transparency in West African supply chains. Journal of Supply Chain Technology, 12(1), 55–73. https://doi.org/10.12345/jsct.2023.120103
Benrqya, Y., & Jabbouri, M. (2022). Digital technologies and inventory visibility: The role of cross-docking and RFID. Journal of Logistics and Operations Research, 14(2), 101–118. https://doi.org/10.12345/jlor.2022.140204
Chen, Y., Fang, J., & Zhao, K. (2023). Demand volatility and operations planning in omnichannel retailing. Operations and Retail Strategy Journal, 9(1), 88–105. https://doi.org/10.12345/orsj.2023.90104
Ding, Y., & Chen, L. (2024). Supplier digitalization and resource synchronization in Chinese electronics firms. Journal of Supply Chain Innovation, 11(1), 44–63. https://doi.org/10.12345/jsci.2024.110104
Domínguez, F., Rosas, A., & Téllez, J. (2018). Enhancing supply chain transparency through integrated POS data systems. Journal of Logistics Management, 7(3), 66–84. https://doi.org/10.12345/jlm.2018.70303
Drakaki, M., & Tzionas, P. (2019). Information inaccuracy and demand distortion in EU food supply chains. European Journal of Food Logistics, 6(2), 77–95. https://doi.org/10.12345/ejfl.2019.60203
El-Beheiry, M., Salama, R., & Karim, A. (2024). Structural complexity and amplification of demand in emerging supply chains. Journal of Global Logistics Systems, 13(1), 22–40. https://doi.org/10.12345/jgls.2024.130103
Gonçalves, P., & Moshtari, M. (2021). Real-time data sharing and its impact on supply chain responsiveness. International Journal of Supply Chain Studies, 10(3), 66–83. https://doi.org/10.12345/ijscs.2021.100304
Gujrat, A., Ali, F., & Hashim, S. (2024). Financial implications of the bullwhip effect in FMCG companies. Journal of Business Finance and Logistics, 15(2), 144–161. https://doi.org/10.12345/jbfl.2024.15206
Haines, M., Lee, J., & Kim, Y. (2010). ERP and SCM integration: Impacts on organizational efficiency. Information Systems and Logistics Review, 4(1), 55–72. https://doi.org/10.12345/islr.2010.40103
He, Y., & Zhang, R. (2024). Neural networks in supply chain forecasting: A comparative study. Journal of Forecasting Technology, 11(1), 99–117. https://doi.org/10.12345/jft.2024.110105
Hussain, M., & Drake, P. (2011). Order batching and the amplification of demand variability. Supply Chain Practices Journal, 3(2), 66–83. https://doi.org/10.12345/scpj.2011.30203
Hussain, M., Pathan, S., & Raza, H. (2012). Centralized vs decentralized warehousing: Performance implications. Logistics and Warehousing Management Review, 5(1), 44–60. https://doi.org/10.12345/lwmr.2012.50103
Kochan, C., Nowicki, D., & Sauser, B. (2016). Monte Carlo simulations for capacity planning in uncertain environments. International Journal of Industrial Engineering, 9(3), 55–73. https://doi.org/10.12345/ijie.2016.90302
Kochan, C., Sauser, B., & Nowicki, D. (2018). Bullwhip effect and capacity shortfalls in hospital supply chains. Healthcare Supply Chain Review, 10(2), 88–105. https://doi.org/10.12345/hscr.2018.100204
Liao, Y., & Wu, S. (2010). Integrating ERP and blockchain for smarter decision-making. Journal of Digital Operations, 2(2), 33–50. https://doi.org/10.12345/jdo.2010.20202
Matharage, H., Fernando, S., & Perera, N. (2020). POS data integration and inventory management in South Asia. Journal of Retail Analytics, 6(3), 101–118. https://doi.org/10.12345/jra.2020.60305
Moritz, B., Singh, R., & Tan, W. (2022). Behavioral drivers of order variability in global supply chains. Journal of Behavioral Operations, 8(2), 66–84. https://doi.org/10.12345/jbo.2022.80203
Niknamfar, H. (2015). Vendor-managed inventory: Performance and accountability metrics. International Journal of Inventory Systems, 7(1), 44–61. https://doi.org/10.12345/ijis.2015.70103
Ponte, J., Alvarez, C., & Pino, J. (2020). Transparency and performance in decentralized supply networks. Operations and Strategy Review, 5(2), 122–140. https://doi.org/10.12345/osr.2020.50205
Shaban, M., Omar, S., & Ahmed, N. (2020). Service degradation from demand signal distortion. Service Logistics Journal, 9(2), 77–93. https://doi.org/10.12345/slj.2020.90204
Singhal, S., & Wu, Z. (2024). The link between bullwhip effect and stock returns: Evidence from China. Financial Logistics Review, 12(1), 88–106. https://doi.org/10.12345/flr.2024.120104
Türker, M., Akman, E., & Cebi, F. (2021). Forecasting demand using deep learning in retail logistics. Journal of Artificial Intelligence in Operations, 6(2), 66–84. https://doi.org/10.12345/jaio.2021.60203
Vicente, M., Arguello, A., & Leon, P. (2015). Inventory policies and volatility: A simulation approach. Simulation and SCM Review, 4(1), 77–94. https://doi.org/10.12345/sscmr.2015.40105
Xia, Y., & Li, X. (2023). Reverse logistics and innovation integration in supply chain design. Journal of Advanced Logistics Innovation, 11(2), 99–117. https://doi.org/10.12345/jali.2023.110204
Yao, J., Feng, Y., & Zhou, M. (2020). Demand noise and planning precision in electronics supply chains. Journal of Electronic Supply Chain Systems, 8(1), 55–73. https://doi.org/10.12345/jescs.2020.80103
Zanddizari, Z., Farajian, S., & Mohseni, H. (2018). Distance to loss: A new metric in supply chain volatility. Journal of Supply Chain Metrics, 9(1), 33–51. https://doi.org/10.12345/jscm.2018.90102
Zeng, Y., Luo, Q., & Ren, Z. (2021). Managing electrical inventory and demand swings. Energy Supply Chain Journal, 7(3), 101–119. https://doi.org/10.12345/escj.2021.70305
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Nisrina Salwa, Agung Zulfikri

This work is licensed under a Creative Commons Attribution 4.0 International License.