نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 گروه مدیریت صنعتی، دانشگاه شهید بهشتی، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The Potential Impact of New Technologies of Big Data and Business Intelligence to Reduce the Bullwhip Effect in Supply Chain
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.
کلیدواژهها [English]
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