Explaining of reduction of supply chain bullwhip effect using new technologies of big data, business intelligence and fuzzy cognitive mapping

Document Type : Research Paper

Authors

1 Department of Industrial Management, SR.C., Islamic Azad University, Tehran, Iran

2 Department of Industrial Management, Shahid Beheshti University, Tehran, Iran

10.22034/jbar.2026.23438.4584

Abstract

ABSTRACT

This study aimed to investigate the effective factors that reduce the supply chain bullwhip effect using of new technologies of big data and business intelligence and utilizing fuzzy cognitive mapping methodology. In this regard, based on the research literature, 11 effective variables were identified and a questionnaire was designed. The importance of each of these variables in explaining the role of new technologies of big data and business intelligence in reducing the bullwhip effect was asked to petrochemical industry experts in a survey conducted in the second half of 1403. The collected data were analyzed using the fuzzy cognitive mapping method and with the help of FCMapper software to examine the relationship between the factors. The results of the analysis showed that the main factors affecting the use of big data and business intelligence technologies to reduce the bullwhip effect in the supply chain are these eleven variables: Flexibility in the supply chain, Trust in the supply chain, Information quality in the supply chain, Agility capability of the supply chain, Customer relationship management, Lead time in the supply chain, Integration and transparency in the supply chain, Coordination and cooperation in the supply chain, Order volume in the supply chain, Information sharing capability, and visibility capability. Next, the relationship and mutual effects of these factors were mapped and explained using the fuzzy cognitive mapping methodology. The findings emphasize the importance of these key factors and the complex relationships between them as a basis for developing operational strategies to reduce the bullwhip effect in supply chains, using the analytical capabilities of modern technologies.

Introduction

A growing number of companies are relying on a variety of and ever-evolving methods to extract valuable information from big data and business intelligence to make better decisions. The terms “big data” and “business intelligence” refer to large volumes of information or data at a specific point in time and within a specific scope. However, these new technologies have a short life cycle and their effective value is rapidly decreasing.

But big data and business intelligence are actually more than what we have just read. Big data is not only about the vast amounts of data or how it is consumed, but also about the structure of this data with the aim of providing added value to the organization. And business intelligence is “a set of methods, processes, architectures, and technologies that transform raw data into meaningful and useful information to benefit from tactical and operational insights and more effective decision-making.”

However, the impact of big data and business intelligence in reducing the bullwhip effect has not yet been analyzed. Since there can be various reasons for the bullwhip effect, the first goal of this research is to understand what actually causes the bullwhip effect in the supply chain. After that, it is investigated how to precisely apply big data and business intelligence in the supply chain.

Methodology

The aim of this section is to identify the relationship between the factors affecting the development of new technologies of big data and business intelligence in reducing the bullwhip effect in the supply chain. In this regard, a questionnaire was designed in which the degree of importance of each of the 38 research variables (in the form of 11 main dimensions) obtained from the Fuzzy Delphi stage was asked from petrochemical industry experts in explaining the development of new technologies of big data and business intelligence in reducing the bullwhip effect in the supply chain. The results were analyzed using the fuzzy cognitive mapping methodology and Fc mapper software, and the relationship between the research factors was examined.

Discussion and Results

One of the most important and dynamic issues in the supply chain is a phenomenon known as the bullwhip effect. This means that small changes in product demand from consumers downstream in the supply chain translate into larger and larger fluctuations in demand upstream. In this study, first, through a comprehensive review of the literature on the subject, factors affecting the reduction of bullwhip effect in the supply chain have been identified. Then, by combining the categories available in the literature, a new classification for these factors has been proposed. In the remainder of this study, pilot products have been selected to study the bullwhip effect. By reviewing the literature on the subject, using the opinions of experts and specialists, and the results of statistical analysis, it was shown that the main factors affecting the reduction of the bullwhip effect include eleven variables as follows:

• Flexibility in the supply chain

• Trust in the supply chain

• Information quality in the supply chain

• Agility capability of the supply chain

• Customer relationship management

• Lead time in the supply chain

• Integration and transparency in the supply chain

• Coordination and cooperation in the supply chain

• Order volume in the supply chain

• Information sharing capability

• Visibility capability

Conclusion

Using the fuzzy cognitive mapping methodology, the relationship between these factors was explained in accordance with Figure 2. Based on the resulting model, a comprehensive understanding of how the variables interact to reduce the bullwhip effect can be found; in addition, the possibility of adopting and designing bullwhip effect reduction strategies in organizations based on the resulting model will be facilitated because organizational strategists have a complete picture of the impact and effects of bullwhip effect reduction factors in the petrochemical industry and can predict the impact of changing each of the aforementioned variables under a new strategy. However, the issue of the bullwhip effect in Iranian industry is one of the issues that requires more attention from researchers and managers, because, according to research, studying big data and business intelligence and the bullwhip effect of the supply chain is necessary, especially in developing countries, and existing research plays an important role in the supply chain, and these technologies and their application are one of the main competitive advantages of organizations.

Keywords: Bullwhip effect, Supply chain, Big data, Fuzzy cognitive mapping, Business intelligence.

Keywords

Main Subjects

References

Al Humdan, E., Shi, Y., Behnia, M., & Najmaei, A. (2020). Supply chain agility: a systematic review of definitions, enablers and performance implications. International Journal of Physical Distribution & Logistics Management, 50(2), 287-312. https://doi.org/10.1108/IJPDLM-06-2019-0192
Axelrod, R. (1976). The analysis of cognitive maps. Structure of decision: The cognitive maps of political elites, 55.
Bogale, M., & Desta, E. (2025). Mitigating the bullwhip effect through sustainable supply chain practices: A systematic literature review. Ethiopian Journal of Development Research, 47(1), 33-52.
Brauch, M., Mohaghegh, M., & Größler, A. (2024). Causes of the bullwhip effect: a systematic review and categorization of its causes. Management Research Review, 47(7), 1127-1149. https://doi.org/10.1108/MRR-05-2023-0392.
Cecere, L. (2013). Big data handbook: How to unleash the big data opportunity. Supply Chain Insights. Accessed December, 3, 2014.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information sciences, 275, 314-347. https://doi.org/10.1016/j.ins.2014.01.015.
Chen, P. H., & Rau, P. L. P. (2020). Evaluating trust, trustworthiness and bullwhip effect: a three-echelon supply chain interactive experiment. In International Conference on Human-Computer Interaction. 443-453 https://doi.org/10.1007/978-3-030-49788-0_33.
Chopra, S., & Meindl, P. (2019). Supply chain management. Strategy, planning & operation. In Das Summa Summarum des Management: Die 25 wichtigsten Werke für Strategie, Führung und Veränderung. 265-275. https://doi.org/10.1007/978-3-8349-9320-5_22.
de Almeida, M. M. K., Marins, F. A. S., Salgado, A. M. P., Santos, F. C. A., & da Silva, S. L. (2017). The importance of trust and collaboration between companies to mitigate the bullwhip effect in supply chain management. Acta Scientiarum. Technology, 39(2), 201-210. https://doi.org/10.4025/actascitechnol.v39i2.29648.
Dubey, R., Gunasekaran, A., & Childe, S. J. (2019). Big data analytics capability in supply chain agility: the moderating effect of organizational flexibility. Management decision, 57(8), 2092-2112. https://doi.org/10.1108/MD-01-2018-0119.
Farsani, M. B., Safari, F., & Rahimpour, M. (2022). Investigating the impact of Supply Chain Business Intelligence capability on the supply chain agility performance by agile supply chain capabilities mediation (Case Study: Zarif Mosavvar’s Boroujen Company). Logistics Thought Scientific Publication, 21(80),170-194. (in persion). https://doi.org/10.22034/LOT.2022.205218.1099.
Forrester, J. (1958). Industrial dynamics, a major breakthrough for decision makers Harvard Business Review.
Gandomi, A., & Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007.
Giannakis, M., & Louis, M. (2016). A multi- agent-based system with big data processing for enhanced supply chain agility. Journal of Enterprise Information Management, 29(5), 706-727. https://doi.org/10.1108/JEIM-06-2015-0050.
Gopal, P. R. C., Rana, N. P., Krishna, T. V., & Ramkumar, M. (2024). Impact of big data analytics on supply chain performance: an analysis of influencing factors. Annals of Operations Research, 333(2), 769-797. https://doi.org/10.1007/s10479-022-04749-6.
Groumpos, P. P. (2015). Modelling business and management systems using fuzzy cognitive maps: A critical overview. IFAC-PapersOnLine, 48(24), 207-212. https://doi.org/10.1016/j.ifacol.2015.12.084.
Hatamlah, H., Allahham, M., Abu-AlSondos, I. A., Al-junaidi, A., Al-Anati, G. M., & Al-Shaikh, M. (2023). The role of business intelligence adoption as a mediator of big data analytics in the management of outsourced reverse supply chain operations. Applied Mathematics & Information Sciences, 17(5), 897-903. https://doi.org/10.18576/amis/170516.
Hofmann, E. (2017). Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, 55(17), 5108-5126. https://doi.org/10.1080/00207543.2015.1061222.
Hsu, C. H., Yang, X. H., Zhang, T. Y., Chang, A. Y., & Zheng, Q. W. (2021). Deploying big data enablers to strengthen supply chain agility to mitigate bullwhip effect: An empirical study of China’s electronic manufacturers. Journal of theoretical and applied electronic commerce research, 16(7), 3375-3405. https://doi.org/10.3390/jtaer16070183.
International Data Corporation (IDC). (2024). Worldwide Quarterly Mobile Phone Tracker [Data set]. https://www.idc.com/getdoc.jsp?containerId=IDC_P33195.
Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36-44. https://doi.org/10.1145/1563821.1563874.
Jafari, T., Zarei, A., Azar, A., & Moghaddam, A. (2023). The impact of business intelligence on supply chain performance with emphasis on integration and agility–a mixed research approach. International Journal of Productivity and Performance Management, 72(5), 1445-1478. https://doi.org/10.1108/IJPPM-09-2021-0511.
Kalaiarasan, R., Olhager, J., Agrawal, T. K., & Wiktorsson, M. (2022). The ABCDE of supply chain visibility: A systematic literature review and framework. International Journal of Production Economics, 248. https://doi.org/10.1016/j.ijpe.2022.108464.
Kankam, S., Osman, A., Inkoom, J. N., & Fürst, C. (2022). Implications of spatio-temporal land use/cover changes for ecosystem services supply in the coastal landscapes of Southwestern Ghana, West Africa. Land, 11(9), 1408. https://doi.org/ 10.3390/land11091408.
Kosko, B. (1985). Adaptive inference, monograph. Verac Inc. Technical Report.
Kumar, R. R., & Raj, A. (2025). Big data adoption and performance: mediating mechanisms of innovation, supply chain integration and resilience. Supply Chain Management: An International Journal, 30(1), 67-85. https://doi.org/10.1108/SCM-03-2024-0186.
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International journal of information management, 34(3), 387-394. https://doi.org/10.1016/j.ijinfomgt.2014.02.002.
Langlois, A., & Chauvel, B. (2017). The impact of supply chain management on business intelligence. Journal of Intelligence Studies in Business, 7(2), 51-61. https://doi.org/10.37380/jisib.v7i2.239.
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management science, 43(4), 546-558. https://doi.org/10.1287/mnsc.43.4.546.
Lee, H. L., Padmanabhan, V., & Whang, S. (2004). Information distortion in a supply chain: the bullwhip effect. Management science, 50(12), 1875-1886. https://doi.org/10.1287/mnsc.50.12.1875.
Lele, V. P., Kumari, S., & White, G. (2023). Streamlining Production: Using Big-Data’s CRM & Supply chain to improve efficiency in high-speed environments. IJCSPUB-International Journal of Current Scienc (IJCSPUB), 13(2), 136-146.
Liu, F., Fang, M., Xiao, S., & Shi, Y. (2025). Mitigating bullwhip effect in supply chains by engaging in digital transformation: the moderating role of customer concentration. Annals of Operations Research, 344(2), 825-846. https://doi.org/10.1007/s10479-024-05908-7.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
Michna, Z., Disney, S. M., & Nielsen, P. (2020). The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts. Omega, 93, 102033. https://doi.org/10.1016/j.omega.2019.02.002.
Michna, Z., Nielsen, P., & Nielsen, I. E. (2018). The impact of stochastic lead times on the bullwhip effect–a theoretical insight. Production & Manufacturing Research, 6(1), 190-200. https://doi.org/10.1080/21693277.2018.1484822.
Moharana, H. S., Murty, J. S., Senapati, S. K., & Khuntia, K. (2012). Coordination, collaboration and integration for supply chain management. International Journal of Interscience Management Review, 2(2), 46-50.
Muriithi, G. M., & Kotzé, J. E. (2013). A conceptual framework for delivering cost effective business intelligence solutions as a service. In Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference (96-100). https://doi.org/10.1145/2513456.251350.
Narayanan, A., Mackelprang, A. W., & Malhotra, M. K. (2022). System performance implications of capacity and flexibility constraints on bullwhip effect in supply chains. Decision Sciences, 53(5), 783-801. https://doi.org/10.1111/deci.12525.
Nyamukoroso, M. (2022). How big data characteristics can help the manufacturing industry mitigate the bullwhip effect in their supply chain (Doctoral dissertation).
Ooi, T. K., Hsieh, C. H., Wang, S. M., & Huang, Y. K. (2025). Orchestrating agile omnichannel supply chain planning through big data analytics and end-to-end visibility. The Asian Journal of Shipping and Logistics, 25(3), 546-561. https://doi.org/10.1016/j.ajsl.2025.05.002.
Özlen, M. K., & Hadžiahmetović, N. (2013). Customer relationship management and supply chain management. World Applied Programming, 3(3), 126-132.
Pereira, J., Takahashi, K., Ahumada, L., & Paredes, F. (2009). Flexibility dimensions to control the bullwhip effect in a supply chain. International journal of production Research, 47(22), 6357-6374. https://doi.org/10.1080/00207540802244232.
Pradhan, S. K., & Routroy, S. (2018). Improving supply chain performance by Supplier Development program through enhanced visibility. Materials Today: Proceedings, 5(2), 3629-3638. https://doi.org/10.1016/j.matpr.2017.11.613.
Quinn, K. (2003). Establishing a Culture of Measurement–A Practical Guide to Business Intelligence. Information Builders.
Ran, W., Wang, Y., Yang, L., & Liu, S. (2020). Coordination mechanism of supply chain considering the bullwhip effect under digital technologies. Mathematical Problems in Engineering, 2020(1), 3217927-28. https://doi.org/10.1155/2020/3217927.
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with fuzzy cognitive maps. Expert systems with applications, 32(2), 543-559. https://doi.org/10.1016/j.eswa.2006.01.032.
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.
Sahay, B. S., & Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Information Management & Computer Security, 16(1), 28-48. https://doi.org/10.1108/09685220810862733.
Sarkar, M., Dey, B. K., Ganguly, B., Saxena, N., Yadav, D., & Sarkar, B. (2023). The impact of information sharing and bullwhip effects on improving consumer services in dual-channel retailing. Journal of retailing and consumer services, 73, 103307. https://doi.org/10.1016/j.jretconser.2023.103307.
Schneider, M., Shnaider, E., Kandel, A., & Chew, G. (1998). Automatic construction of FCMs. Fuzzy sets and systems, 93(2), 161-172. https://doi.org/10.1016/S0165-0114(96)00218-7.
Shekarian, M., Nooraie, S. V. R., & Parast, M. M. (2020). An examination of the impact of flexibility and agility on mitigating supply chain disruptions. International Journal of Production Economics, 220, 107438. https://doi.org/10.1016/j.ijpe.2019.07.011.
Siddique, M. N. A., Hasan, K. W., Ali, S. M., Moktadir, M. A., Paul, S., & Kabir, G. (2021). Modeling drivers to big data analytics in supply chains. Journal of Production Systems and Manufacturing Science.
Somapa, S., Cools, M., & Dullaert, W. (2018). Characterizing supply chain visibility–a literature review. The International Journal of Logistics Management, 29(1), 308-339. https://doi.org/10.1108/IJLM-06-2016-0150.
Sterman, J. D. (1987). Systems simulation. Expectation formation in behavioral simulation models. Behavioral science, 32(3), 190-211. https://doi.org/10.1002/bs.3830320304.
Subramanian, B., Mishra, A., Venkatachalam, B., Mandala, G., Krishnan, N., & Srithar, S. (2025). Big data and fuzzy logic for demand forecasting in supply chain management: a data-driven approach. Journal of fuzzy extension and applications, 6(2), 260-283. https://doi.org/10.22105/jfea.2025.488816.1703.
Tang, L., Yang, T., Tu, Y., & Ma, Y. (2021). Supply chain information sharing under consideration of bullwhip effect and system robustness. Flexible Services and Manufacturing Journal, 33(2), 337-380. https://doi.org/10.1007/s10696-020-09384-6.
Tesfay, Y. Y. (2015). Modeling the causes of the bullwhip effect and its implications on the theory of organizational coordination. In Supply Chain Forum: An International Journal, 16(2),30-46.
https://doi.org/10.1080/16258312.2015.11673827.
Wadhwa, S., Mishra, M., Chan, F. T., & Ducq, Y. (2010). Effects of information transparency and cooperation on supply chain performance: a simulation study. International Journal of Production Research, 48(1), 145-166. https://doi.org/10.1080/00207540802251617.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International journal of production economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031.
Xirogiannis, G., & Glykas, M. (2004). Fuzzy cognitive maps in business analysis and performance-driven change. IEEE Transactions on Engineering Management, 51(3), 334-351. https://doi.org/10.1109/TEM.2004.830861
Yin, X., & Tang, W. (2025). The bullwhip effect, market competition and standard deviation ratio in two parallel supply chains. Computers & Chemical Engineering, 192, 108916. https://doi.org/10.1016/j.compchemeng.2024.108916.
Journal of Business Administration Researches
Volume 18, Issue 43
April 2026
Pages 117-148

Files

Share

How to cite

Statistics