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February 19, 20260 citationsOpen Access

Developing a Model for Using New Technologies Big Data and Business Intelligence to Reduce the Bullwhip Effect in Supply Chain

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SDSadegh DanandehDTDavood TalebiMMMohammad Mehdi Movahedi

Key Points

  • This research aims to develop a model that utilizes new technologies to reduce the bullwhip effect in supply chains, particularly within the petrochemical industry.
  • Conducted library studies and field surveys with 19 industry experts.
  • Extracted components and variables using purposive sampling and fuzzy Delphi method.
  • Distributed a questionnaire to 60 petrochemical industry managers and stakeholders for quantitative analysis.
  • Business intelligence and big data significantly reduce the bullwhip effect.
  • Identified 38 indicators impacting the bullwhip effect within 11 components and 3 aspects.
  • Statistical analysis confirmed the positive effects of selected technologies on supply chain performance.

Abstract

Purpose: Supply chain has a very complex nature and is getting more complex due to increasing globalization, market growth, and constantly-changing customer preferences. This increasing complexity can lead to a lack of visibility of assets, inefficient inventory administration, or logistical mismanagement. These complexities lead to the well-known phenomenon of the “bullwhip effect” (BE) in supply chain. Design/methodology/approach: This research is of applied type in terms of purpose and descriptive-survey in terms of methodology. First, the information resulted from library studies, field surveys, and comparative comparisons was collected and the components and variables of this field were extracted after interviewing 19 experts and specialists in the petrochemical industry who were selected using the purposive sampling method. Then, the resulting indicators were screened in three rounds after developing a questionnaire and using the fuzzy Delphi method, which ultimately resulted in 38 indicators, presented in the form of 11 components and 3 aspects. The statistical population of the research in quantitative part includes managers and stakeholders of the petrochemical industry, 60 of whom were selected as the sample group using the simple random sampling method and the Cochran sample size determination formula. Subsequently, the questionnaire resulting from the final Delphi part was distributed among this sample. Findings: Finally, the data analysis was carried out by structural equation modeling and PLS4 Software. The results of the study indicated that the business intelligence, big data, and each of their factors have a significant effect on the bullwhip effect reduction.

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Cite This Study

Danandeh et al. (2025) studied this question.

synapsesocial.com/papers/6996a80aecb39a600b3ee532https://doi.org/10.82395/ijfaes.2025.1205232
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