The Potential Impact of New Technologies of Big Data and Business Intelligence to Reduce the Bullwhip Effect in Supply Chain

Document Type : Original Article

Authors

1 Ph.D. Student, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

3 Assistant Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Abstract

Introduction: Modern supply chains, influenced by globalization and changing customer preferences, are facing increasing complexity, which leads to problems such as lack of transparency of assets, inefficient inventory management, and intensification of the “bullwhip effect”. The petrochemical industry, with its characteristics such as high production volume, low relative value of products, high costs, and long supply chains, is particularly vulnerable to this effect. The key solution to reduce this effect is to increase transparency and integration of the supply chain; however, the fragmentation and massive volume of data have made this goal challenging. In this context, “big data” and “business intelligence” technologies have emerged as strategic solutions that have the ability to process huge volumes of data and provide actionable and real-time insights. However, the simultaneous impact of these two technologies, especially in developing countries such as Iran and in specific industries such as petrochemicals, has not been studied enough. This study aims to fill this research gap and provide empirical evidence from the Iranian petrochemical industry.
Methods: This study is applied in terms of purpose and descriptive-survey in terms of data collection method, which was conducted with a mixed design (qualitative-quantitative). In the qualitative phase, key effective factors were first identified through library study and field surveys. To finalize and achieve expert consensus on these factors, the "Fuzzy Delphi" method was used with a community of 19 senior industry managers and university professors. In the quantitative phase, a statistical sample of 384 managers and stakeholders of the Iranian petrochemical industry was selected using a non-probability judgmental sampling method, and data were collected through a questionnaire based on the Likert scale. Advanced statistical methods including exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were used to analyze the data and test the hypotheses using M plus version 8.3 software. The reliability and validity of the research tool were confirmed by indicators such as Cronbach's alpha, composite reliability, average variance extracted, and HTMT criterion.
Results and Discussion: The research findings showed that the presented conceptual model has a good fit (CMIN/DF=1.122, CFI=0.958, TLI=0.956, SRMR=0.043, RMSEA=0.018). The results of structural equation modeling confirmed all 13 research hypotheses and showed that all eleven identified factors (organizational commitment, commercial value, order volume, information sharing capability, visibility, IoT applications, increased connectivity through cloud computing, agility capability, innovation capability, customer relationship management, and customer service management) have a positive and significant impact on big data and business intelligence capabilities. Also, big data and business intelligence significantly (with path coefficients of 0.288 and 0.186, respectively) reduce the bullwhip effect. Importantly, the impact paths of these two distinct but complementary technologies were identified: big data reduces the bullwhip effect mainly through operational-technical factors (such as visibility and order volume optimization), and business intelligence mainly through strategic-managerial factors (such as innovation capability and IoT data integration). The coefficient of determination (R²) for the variables of big data, business intelligence and bullwhip effect was 0.992, 0.994 and 0.855 respectively, indicating a very high ability of the model to explain the variance of the dependent variables.
Conclusion: The present study proves that big data and business intelligence technologies are practical solutions to reduce the bullwhip effect in the petrochemical industry supply chain. Investing in these technologies, even with indigenous solutions, is a strategic necessity to reduce costs and increase competitiveness. Successful implementation requires management commitment, investment and training. A two-phase implementation framework is proposed: first, establishing a data infrastructure and transparency, and then developing analytics and intelligence. This study paves the way for future research in other industries and countries, as well as using more advanced modeling methods.

Keywords

Main Subjects

References

  1. Adebanjo, D., Teh, P. L., & Ahmed, P. K. (2018). The impact of supply chain relationships and integration on innovative capabilities and manufacturing performance: the perspective of rapidly developing countries. International journal of production research, 56(4), 1708-1721.
  2. 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.
  3. 3. Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: a literature review. International journal of production research, 57(15-16), 4719-4742.
  4. 4. Birkel, H. S., & Hartmann, E. (2020). Internet of Things–the future of managing supply chain risks. Supply Chain Management: An International Journal, 25(5), 535-548.
  5. 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), 34-56.
  6. Bray, R. L., Yao, Y., Duan, Y., & Huo, J. (2019). Ration gaming and the bullwhip effect. Operations Research, 67(2), 453-467.
  7. Bush, R. P., Ortinau, D. J., Bush, A. J., & Hair Jr, J. F. (1990). Developing A Behavior Based Scale to Assess Retail Salesper. Journal of Retailing, 66(1), 119.
  8. Cao, Q., Schniederjans, D. G., & Schniederjans, M. (2017). Establishing the use of cloud computing in supply chain management. Operations Management Research, 10(1), 47-63.
  9. Chan, F., Lee, G. K., Lee, E. J., Kubota, C., & Allen, C. A. (2007). Structural equation modeling in rehabilitation counseling research. Rehabilitation Counseling Bulletin, 51(1), 44-57.
  10. 10. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
  11. 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.
  12. Colicchia, C., Creazza, A., Noè, C., & Strozzi, F. (2019). Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA). Supply chain management: an international journal, 24(1), 5-21.
  13. 13. Cukier, K., & Mayer-Schönberger, V. (2021). Big data: A revolution that will transform how we live, work, and think.
  14. Curcio, D., & Longo, F. (2009). Inventory and internal logistics management as critical factors affecting the supply chain performances. International Journal of Simulation and Process Modelling, 5(4), 278-288.
  15. Dai, J., Li, S., & Peng, S. (2017). Analysis on causes and countermeasures of bullwhip effect. In MATEC web of conferences, 100(1), 05018-05048.
  16. De Jong, B., Gillespie, N., Williamson, I., & Gill, C. (2021). Trust consensus within culturally diverse teams: A multistudy investigation. Journal of Management, 47(8), 2135-2168.
  17. De Vass, T., Shee, H., & Miah, S. J. (2018). The effect of “Internet of Things” on supply chain integration and performance: An organisational capability perspective. Australasian Journal of Information Systems, 22.
  18. 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.
  19. Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of business research, 69(2), 897-904.
  20. Francis, V. (2008). Supply chain visibility: lost in translation?. Supply chain management: An international journal, 13(3), 180-184.
  21. Giannakis, M., Spanaki, K., & Dubey, R. (2019). A cloud-based supply chain management system: effects on supply chain responsiveness. Journal of Enterprise Information Management, 32(4), 585-607.

22.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.

  1. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317.
  2. Harman, H. H. (1976). Modern factor analysis. University of Chicago press.
  3. H Davenport, T. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & leadership, 42(4), 45-50.
  4. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., ... & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods, 17(2), 182-209.
  5. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing, 277-319.
  6. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135.
  7. 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.
  8. Hoshmandi Maher, M. , Amiri, M. and Olfat, L. (2013). An Integrated Approach for Supplier Selection in a Supply Chain: IT Capabilities Approach. Journal of Industrial Management Perspective, 2(4), 91-115. (in persian)
  9. 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.
  10. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2), 195-204.
  11. Inamdar, Z., Raut, R., Narwane, V. S., Gardas, B., Narkhede, B., & Sagnak, M. (2021). A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018. Journal of Enterprise Information Management, 34(1), 101-139.
  12. International Data Corporation (IDC). (2024). Worldwide Quarterly Mobile Phone Tracker [Data set]. https://www.idc.com/getdoc.jsp?containerId=IDC_P33195.
  13. 35. 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.
  14. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). Linear model selection and regularization. In An introduction to statistical learning: with applications in R, 225-288
  15. 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.
  16. 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.
  17. Khan, M. H., Ahmed, S., & Hussain, D. (2019). Analysis of bullwhip effect: a behavioral approach. In Supply Chain Forum: An International Journal, 20(4), 310-331.
  18. Koot, M., Mes, M. R., & Iacob, M. E. (2021). A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics. Computers & industrial engineering, 154, 107076.
  19. 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.
  20. Lee, H. L., Padmanabhan, V., & Whang, S. (1997a). Information distortion in a supply chain: The bullwhip effect. Management science, 43(4), 546-558.
  21. Lee, H. L., Padmanabhan, V., & Whang, S. (1997b). Information distortion in a supply chain: the bullwhip effect. Management science, 43(12), 1875-1886.
  22. 44. 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.
  23. 45. Liang, T. P., & Liu, Y. H. (2018). Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2-10.
  24. Liao, Y., & Li, Y. (2019). Complementarity effect of supply chain competencies on innovation capability. Business Process Management Journal, 25(6), 1251-1272.
  25. 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.
  26. 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.
  27. Mohaghar, A., Hashemi, P. S. H., & Talaei, H. (2017). Dynamic Modeling of a New Product Supply Chain using System Dynamics Approach, Journal of Industrial Management Perspective, 6(24), 9-36 (in persian)
  28. 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.
  29. Narayanan, V. (2014). Using big-data analytics to manage data deluge and unlock real-time business insights. The Journal of Equipment Lease Financing, 32(2), 1.
  30. Naoui, F. (2014). Customer service in supply chain management: a case study. Journal of Enterprise Information Management, 27(6), 786-801.
  31. Novais, L., Maqueira, J. M., & Ortiz-Bas, Á. (2019). A systematic literature review of cloud computing use in supply chain integration. Computers & Industrial Engineering, 129, 296-314.
  32. Nyamukoroso, M. (2022). How big data characteristics can help the manufacturing industry mitigate the bullwhip effect in their supply chain (Doctoral dissertation).
  33. Ojha, D., Sahin, F., Shockley, J., & Sridharan, S. V. (2019). Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type. International Journal of Production Economics, 208, 529-543.
  34. 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.
  35. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
  36. Ponte, B., Dominguez, R., Cannella, S., & Framinan, J. M. (2022). The implications of batching in the bullwhip effect and customer service of closed-loop supply chains. International Journal of Production Economics, 244, 108379.
  37. Pradhan, S. K., & Routroy, S. (2018). Improving supply chain performance by Supplier Development program through enhanced visibility. Materials Today: Proceedings, 5(2), 3629-3638.
  38. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3), 879-891.
  39. Quinn, K. (2003). Establishing a Culture of Measurement–A Practical Guide to Business Intelligence. Information Builders.
  40. 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.
  41. Raweewan, M., & Ferrell Jr, W. G. (2018). Information sharing in supply chain collaboration. Computers & Industrial Engineering, 126, 269-281.
  42. 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.
  43. Shekarian, E. (2020). A review of factors affecting closed-loop supply chain models. Journal of Cleaner Production, 253, 119823.
  44. Somapa, S., Cools, M., & Dullaert, W. (2018). Characterizing supply chain visibility–a literature review. The International Journal of Logistics Management, 29(1), 308-339.
  45. Steger, M. F. (2006). An illustration of issues in factor extraction and identification of dimensionality in psychological assessment data. Journal of personality Assessment, 86(3), 263-272.
  46. Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233.
  47. 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.
  48. 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.
  49. Wang, C., & Hu, Q. (2020). Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance. Technovation, 94, 102010.
  50. Wang, X., & Disney, S. M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operational Research, 250(3), 691-701.
  51. Wei, H. L., & Wang, E. T. (2010). The strategic value of supply chain visibility: increasing the ability to reconfigure. European Journal of Information Systems, 19(2), 238-249.
  52. Yin, X., & Tang, W. (2025). The bullwhip effect, market competition and standard deviation ratio in two parallel supply chains. Computers & Chemical Engineering, 192, 108916.
  53. Yousefi, R. and Menhaj, M. B. (2011). The Impact of the Bulk Demand Forecasting System on the Whip Effect in the Supply Chain: A Comparative Approach. Journal of Industrial Management Perspective, 1(3), 29-41. (in persian)
  54. Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of cleaner production, 142, 626-641.
  55. Zhu, T., Balakrishnan, J., & Da Silveira, G. J. (2020). Bullwhip effect in the oil and gas supply chain: A multiple-case study. International journal of production economics, 224, 107548.
Journal of Industrial Management Perspective
Volume 15, Issue 4 - Serial Number 60
December 2025
Pages 143-172

Files

Share

How to cite

Statistics