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

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.