From Variability to Visibility: Mitigating the Bullwhip Effect in Modern Supply Chains

Authors

  • Nisrina Salwa International Islamic University Malaysia
  • Agung Zulfikri Telkom University

DOI:

https://doi.org/10.61194/sijl.v2i2.619

Keywords:

Bullwhip Effect, Inventory Management, Supply Chain Resilience, Demand Forecasting, Information Sharing, Distribution Strategy, Supply Chain Technology

Abstract

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

2024-05-31

How to Cite

Salwa, N., & Zulfikri , A. (2024). From Variability to Visibility: Mitigating the Bullwhip Effect in Modern Supply Chains. Sinergi International Journal of Logistics, 2(2), 118–132. https://doi.org/10.61194/sijl.v2i2.619

Issue

Section

Articles