سامانه مدیریت نشریات علمی دانشگاه شهید بهشتی

جعفری, طه, زارعی, عظیم, آذر, عادل, مقدم, علیرضا. (1401). طراحی مدل تأثیر هوشمندی کسب وکار بر عملکرد زنجیره تأمین با تأکید بر یکپارچگی و چابکی. , 12(3), 279-315. doi: 10.52547/jimp.12.3.279
طه جعفری; عظیم زارعی; عادل آذر; علیرضا مقدم. "طراحی مدل تأثیر هوشمندی کسب وکار بر عملکرد زنجیره تأمین با تأکید بر یکپارچگی و چابکی". , 12, 3, 1401, 279-315. doi: 10.52547/jimp.12.3.279
جعفری, طه, زارعی, عظیم, آذر, عادل, مقدم, علیرضا. (1401). 'طراحی مدل تأثیر هوشمندی کسب وکار بر عملکرد زنجیره تأمین با تأکید بر یکپارچگی و چابکی', , 12(3), pp. 279-315. doi: 10.52547/jimp.12.3.279
جعفری, طه, زارعی, عظیم, آذر, عادل, مقدم, علیرضا. طراحی مدل تأثیر هوشمندی کسب وکار بر عملکرد زنجیره تأمین با تأکید بر یکپارچگی و چابکی. , 1401; 12(3): 279-315. doi: 10.52547/jimp.12.3.279

طراحی مدل تأثیر هوشمندی کسب وکار بر عملکرد زنجیره تأمین با تأکید بر یکپارچگی و چابکی

چشم‌انداز مدیریت صنعتی
دوره 12، شماره 3 - شماره پیاپی 47، 1401، صفحه 279-315    اصل مقاله (1.02 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.52547/jimp.12.3.279
نویسندگان
طه جعفری* 1؛ عظیم زارعی2؛ عادل آذر3؛ علیرضا مقدم4
1دانش‌آموخته دکتری، دانشگاه سمنان.
2دانشیار، دانشگاه سمنان.
3استاد، دانشگاه تربیت مدرس.
4استادیار، دانشگاه سمنان.
چکیده
امروزه مدیریت زنجیره تأمین باید راهکارهایی را اتخاذ کند تا بتواند پاسخگوی نیازهای متغیر مشتریان باشد. استفاده از هوشمندی کسب­وکار به­همراه تقویت یکپارچگی و قابلیت چابکی، موجب ایجاد مزیت رقابتی در زنجیره تأمین می­شود و درنتیجه زنجیره تأمین می­تواند موقعیت بهتری در کسب­‌وکار داشته باشد. هدف این پژوهش، بررسی چگونگی تأثیر هوشمندی کسب‌­وکار بر یکپارچگی، چابکی و عملکرد زنجیره تأمین است. درضمن اثر یکپارچگی و چابکی بر عملکرد زنجیره تأمین موردبررسی قرار می­‌گیرد. در این پژووهش ابتدا با مرور پیشینه موضوع، یک مدل مفهومی ارائه شده است؛ سپس روش پیمایش برای جمع‌آوری داده­‌ها به‌­کار رفته است. داده‌­ها از 369 شرکت از صنایع مختلف و شهرهای متفاوت در ایران طی آذر و دی‌ماه سال 1399 جمع‌آوری شد. واحد تحلیل پژوهش، شرکت­‌های تولیدی بودند. برای آزمون فرضیه‌­ها از مدل­سازی معادلات ساختاری استفاده شد. نتایج نشان می­‌دهد که هوشمندی کسب‌وکار، یکپارچگی و چابکی نقش مهمی در دستیابی به عملکرد بهتر زنجیره تأمین دارند؛ البته در این میان، هوشمندی کسب­‌وکار بیشترین تأثیر را بر عملکرد زنجیره تأمین دارد. هوشمندی کسب­‌وکار بر یکپارچگی و چابکی زنجیره تأمین نیز تأثیر مثبت و معناداری دارد؛ همچنین مشخص شد که یکپارچگی تأثیر مستقیمی بر چابکی زنجیره تأمین دارد.
کلیدواژه‌ها
هوشمندی کسب‌وکار؛ یکپارچگی؛ چابکی؛ عملکرد زنجیره تأمین
عنوان مقاله [English]
Designing a Model for the Impact of Business Intelligence on Supply Chain Performance with an Emphasis on Integration and Agility
نویسندگان [English]
Taha Jafari1؛ Azim Zarei2؛ Adel Azar3؛ Alireza Moghaddam4
1Ph.D, Semnan University.
2Associate Professor, Semnan University.
3Professor, Tarbiat Modares University.
4Assistant Professor, Semnan University.
چکیده [English]
Today, supply chain management must adopt solutions to meet the changing needs of customers. Using business intelligence, along with integraiton and agility, creates a competitive advantage in the supply chain, and as a result, the supply chain can achive a better position in the business. This paper aims to explore how business intelligence influences integration, agility, and supply chain performance. Meanwhile, the effect of supply chain integration and agility on supply chain performance will be examined. In this research, literature review was performed and a conceptual model was presented. Then the survey method was done. Data were collected through a survey of 369 companies from different industries and different cities in Iran during December 2020 and January 2021. The research analysis unit was manufacturing companies. Structural equation modeling was used to test hypotheses. The results show that business intelligence, integration, and agility play an important role in achieving better supply chain performance. In the meantime, BI has the greatest impact on supply chain performance. Additionally, business intelligence has a positive and significant effect on the integration and agility of the supply chain. It was also found that integration has a direct effect on supply chain agility.
کلیدواژه‌ها [English]
Business Intelligence, Integration, Agility, Supply Chain Performance
مراجع
  1. Al-Eisawi, D., Serrano, A. & Koulouri, T. (2020). The effect of organizational absorptive capacity on business intelligence systems efficiency and organizational efficiency. Industrial Management & Data Systems, 121(2), 519-544.
  2. AlKahtani, M., Rehman, A. U., Al-Zabidi, A., & Choudhary, A. (2018). Agile Supply Chain Assessment: An Empirical Study on Concepts, Research and Issues. Arabian Journal for Science and Engineering, 44, 2551–2565.
  3. Asgarnezhad Nouri, B., Saebnia, S., & Foladi, E. (2020). Effect of Lean and Agile Supply Chain Strategies on Supply Chain Responsiveness and Firm Performance: The Mediating role of Postponing Order and Strategic Partnership of Suppliers (Case Study: Automotive Industry). Journal of Industrial Management Perspective, 10(4), 65-89. (In Persian)
  4. Ashrafi, A., Zare Ravasan, A., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47, 1–15.
  5. Aslam, H., Blome, C., Roscoe, S., & Azhar, T. M. (2020). Determining the antecedents of dynamic supply chain capabilities. Supply Chain Management: An International Journal, 25(4), 427–442.
  6. Ataseven, C., and A. Nair. (2017). Assessment of Supply Chain Integration and Performance Relationships: A Meta-Analytic Investigation of the Literature. International Journal of Production Economics, 185, 252–265.
  7. Autry, C.W., Rose, W.J. and Bell, J.E. (2014). Reconsidering the supply chain integration performance relationship: in search of theoretical consistency and clarity. Journal of Business Logistics, 35(3), 275–6.
  8. Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research, 96, 228–237.
  9. Azar, A., Tizroo, A., Moghbel Baarz, A., & Anvari Rostami, AA. (2011). Modeling the Agility of Supply Chain Using Interpretive Structural Modeling Approach. Management Research in Iran, 14(4), 1-25. (In Persian)
  10. Barlette, Y., & Baillette, P. (2020). Big data analytics in turbulent contexts: towards organizational change for enhanced agility. Production Planning & Control, 33(2-3), 1–18.
  11. Bazargan, A. (2010). Introduction to qualitative and mixed research methods - common approaches in behavioral sciences, Didar, Tehran. (In Persian)
  12. Birkel, H. S., & Müller, J. M. (2020). Potentials of Industry 4.0 for Supply Chain Management within the Triple Bottom Line of Sustainability – A Systematic Literature Review. Journal of Cleaner Production, 289,
  13. Božič, K., & Dimovski, V. (2019). Business intelligence and analytics use, innovation ambidexterity, and firm performance: A dynamic capabilities perspective. The Journal of Strategic Information Systems, 28(4),
  14. Çağlar Kalkan, M.B. and Aydın, K. (2020). The role of 4PL provider as a mediation and supply chain agility. Modern Supply Chain Research and Applications, 2(2), 99-111.
  15. Cao, Z., Huo, B., Li, Y., & Zhao, X. (2015). The impact of organizational culture on supply chain integration: a contingency and configuration approach. Supply Chain Management: An International Journal, 20(1), 24–41.
  16. Carvalho, H., Azevedo, S. G., & Cruz-Machado, V. (2012). Agile and resilient approaches to supply chain management: influence on performance and competitiveness. Logistics Research, 4(1-2), 49–62.
  17. Chahal, H., Gupta, M., Bhan, N., & Cheng, T. C. E. (2020). Operations management research grounded in the resource-based view: A meta-analysis. International Journal of Production Economics, 230,
  18. Chand, P., Thakkar, J. J., & Ghosh, K. K. (2020). Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model. Resources Policy, 68,
  19. Chang, H. H., Hong Wong, K., & Sheng Chiu, W. (2019). The Effects of Business Systems Leveraging on Supply Chain Performance: Process Innovation and Uncertainty as Moderators. Information & Management, 56,
  20. Chang, W., Ellinger, A. E., Kim, K. (Kate), & Franke, G. R. (2016). Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors. European Management Journal, 34(3), 282–295.
  21. Danese, P., Molinaro, M., & Romano, P. (2020). Investigating fit in supply chain integration: A systematic literature review on context, practices, performance links. Journal of Purchasing and Supply Management, 26(5),
  22. DeGroote, S.E., & Marx, T.G. (2013). The impact of IT on supply chain agility and firm performance: An empirical investigation. International Journal of Information Management, 33, 909–916.
  23. Dehgani, R., & Jafari Navimipour, N. (2019). The impact of information technology and communication systems on the agility of supply chain management systems. Kybernetes, 48(10), 2217-2236.
  24. Delic, M., Eyers, D., & Mikulic, J. (2019). Additive manufacturing: empirical evidence for supply chain integration and performance from the automotive industry. Supply Chain Management, 24(5), 604-621.
  25. Delic, M., & Eyers, D. R. (2020). The effect of additive manufacturing adoption on supply chain flexibility and performance: an empirical analysis from the automotive industry. International Journal of Production Economics, 228, 107689.
  26. 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.
  27. Dubey, R, Singh, T, & Gupta, O. K. (2015). Impact of Agility, Adaptability and Alignment on Humanitarian Logistics Performance: Mediating Effect of Leadership. Global Business Review, 16(5), 812–831.
  28. Durach, C. F., & Wiengarten, F. (2019). Supply chain integration and national collectivism. International Journal of Production Economics, 224,
  29. Ebrahimpour Azbari, M., Moradi, M., & Marzban Moghaddam, N. (2016). The Impact of Supplier Integration Capability on Firm Performance with the Mediating Role of Organizational Agility. Journal of Industrial Management Perspective, 6(2), 169-192. (In Persian)
  30. Finfgeld-Connett, D., (2018). A Guide to Qualitative Meta-Synthesis. Taylor & Francis. New York, NY: Routledge.
  31. Fosso Wamba, S., & Akter, S. (2019). Understanding supply chain analytics capabilities and agility for data-rich environments. International Journal of Operations & Production Management, 39(6/7/8), 887–912
  32. Ganbold, O., Matsui, Y., & Rotaru, K. (2020). Effect of information technology-enabled supply chain integration on firm’s operational performance. Journal of Enterprise Information Management, ahead-of-print (ahead-of-print).
  33. Gey, D. G., Yusuf, Y., Menhat, M. S., Abubakar, T., & Ogbuke, N. J. (2019). Agile capabilities as necessary conditions for maximising sustainable supply chain performance: an empirical investigation. International Journal of Production Economics, 222 (1),
  34. Ghorani, S., Amiri, M., Olfat, L., & Kazazi, A. (2016). Developing a Supply Chain Agility Model and Analysis of its Effects on Supply Chain Performance. Journal of Industrial Management Perspective, 5(4), 9-39. (In Persian)
  35. Gligor, D. (2014). The role of demand management in achieving supply chain agility. Supply Chain Management: An International Journal, 19(5/6), 577–591.
  36. Gottge, Simon; Menzel, Torben; Forslund, Helena (2020). Industry 4.0 technologies in the purchasing process. Industrial Management & Data Systems, 120(4), 730–748.
  37. Hahn, G. J., & Packowski, J. (2015). A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems, 76, 45–52.
  38. Hair, J.F., Babin, B.J., Anderson, R.E., & Black, W.C. (2018). Multivariate Data Analysis. UK, Cengage.
  39. Huo, B. (2012). The Impact of Supply Chain Integration on Company Performance: An Organizational Capability Perspective. Supply Chain Management: An International Journal, 17(6), 596–610.
  40. Hwang, T., & Kim, S. T. (2018). Balancing in-house and outsourced logistics services: effects on supply chain agility and firm performance. Service Business, 13,531–556. 
  41. Irfan, M., Wang, M. and Akhtar, N. (2019). Enabling supply chain agility through process integration and supply flexibility: Evidence from the fashion industry. Asia Pacific Journal of Marketing and Logistics, 32(2), 519-547.
  42. Kalantari, Kh. (2015). Data processing and analysis in socio-economic research. Farhang Saba, Tehran. (In Persian)
  43. Khan, S.A., Chaabane, A. and Dweiri, F. (2019). A knowledge-based system for overall supply chain performance evaluation: a multi-criteria decision making approach. Supply Chain Management, 24(3), 377-396.
  44. Kumar, V., Jabarzadeh, Y., Jeihouni, P., & Garza-Reyes, J.A. (2020). Learning orientation and innovation performance: the mediating role of operations strategy and supply chain integration. Supply Chain Management, 25(4), 457-474.
  45. Lau, R. S. M., Xie, J., & Zhao, X. (2008). Effects of inventory policy on supply chain performance: A simulation study of critical decision parameters. Computers & Industrial Engineering, 55(3), 620–633.
  46. Lemahieu, W., Vanden Broucke, S., & Baesens, B. (2018). Data Warehousing and Business Intelligence. In Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data, Cambridge: Cambridge University Press, UK.
  47. Lee, C.W., Kwon, I.G. and Severance, D. (2007). Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer. Supply Chain Management: An International Journal, 12(6), 444-452.
  48. Lee, N. C.-A., Wang, E. T. G., & Grover, V. (2020). IOS drivers of manufacturer-supplier flexibility and manufacturer agility. The Journal of Strategic Information Systems, 29(1),
  49. Liu, L. (2010). Supply Chain Integration through Business Intelligence. 2010 International Conference on Management and Service Science,
  50. Liu, W., Yan, X., Si, C., Xie, D. and Wang, J. (2020). Effect of buyer-supplier supply chain strategic collaboration on operating performance: evidence from Chinese companies. Supply Chain Management, 25(6), 823-839.
  51. Maestrini, V., Luzzini, D., Maccarrone, P., & Caniato, F. (2017). Supply chain performance measurement systems: A systematic review and research agenda. International Journal of Production Economics, 183, 299–315.
  52. Maggioni, I., & Ricciardi, F. (2012). Business Intelligence for Supply Chain Management: Trends from Scholarly Literature and from the World of Practice. Information Systems: Crossroads for Organization, Management, Accounting and Engineering, 287–294.
  53. Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded Sustainable Supply Chain for Industry 4.0 requirements. Computers & Industrial Engineering, 127, 925–953.
  54. Mandal, S. (2018). An examination of the importance of big data analytics in supply chain agility development. Management Research Review, 41(10), 1201-1219.
  55. Manuel Maqueira, J., Moyano-Fuentes, J., & Bruque, S. (2018). Drivers and consequences of an innovative technology assimilation in the supply chain: cloud computing and supply chain integration. International Journal of Production Research, 57(7), 1–21.
  56. Mashayekhi Nezamabadi, E., & Alem Tabriz, A. (2017). The Impact of integration of Upstream and Downstream of Supply Chain on Quality Performance and Quality Program. Journal of Industrial Management Perspective, 6(4), 37-57. (In Persian)
  57. Nakayama, R S; De Mesquita Spínola, M; Silva, J R (2020). Towards I4.0: A comprehensive analysis of evolution from I3.0. Computers & Industrial Engineering, 144, 106453
  58. Oh, S., Ryu, Y. U., & Yang, H. (2018). Interaction effects between supply chain capabilities and information technology on firm performance. Information Technology and Management, 20,91–106.
  59. Olugu, E U; Wong, K Y. (2012). An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Systems with Applications, 39(1), 375–384.
  60. Parente, R C.; Geleilate, (2016). Developing new products in the automotive industry: exploring the interplay between process clockspeed and supply chain integration. Industrial and Corporate Change, 25(3), 507–521.
  61. Popovič, A., Puklavec, B. and Oliveira, T. (2018). Justifying business intelligence systems adoption in SMEs: Impact of systems use on firm performance. Industrial Management & Data Systems, 119(1), 210-228.
  62. Pramanik, D., Mondal, S.C. and Haldar, A. (2020). Resilient supplier selection to mitigate uncertainty: soft-computing approach. Journal of Modelling in Management, 15(4), 1339-1361.
  63. Roscoe, S., Eckstein, D., Blome, C., & Goellner, M. (2019). Determining how internal and external process connectivity affect supply chain agility: a life-cycle theory perspective. Production Planning & Control, 31(1), 1–14.
  64. Sadiq Jajja, M. S., Chatha, K. A., & Farooq, S. (2018). Impact of Supply Chain Risk on Agility Performance: Mediating Role of Supply Chain Integration. International Journal of Production Economics, 205 (2018), 118–138.
  65. Sangari, M. S., Razmi, J., & Zolfaghari, S. (2015). Developing a practical evaluation framework for identifying critical factors to achieve supply chain agility. Measurement, 62, 205–214. 
  66. Schniederjans, D. G. (2018). Business process innovation on quality and supply chains. Business Process Management Journal, 24(3), 635–651.
  67. Sharma, N., Sahay, B. S., Shankar, R., & Sarma, P. R. S. (2017). Supply chain agility: review, classification and synthesis. International Journal of Logistics Research and Applications, 20(6), 532–559.
  68. Shukor, A. A. A., Newaz, M. S., Rahman, M. K., & Taha, A. Z. (2020). Supply chain integration and its impact on supply chain agility and organizational flexibility in manufacturing firms. International Journal of Emerging Markets, ahead-of-print (ahead-of-print).
  69. Singh Patel, B., Samuel, C., & Sutar, G. (2020). Designing of an agility control system: a case of an Indian manufacturing organization. Journal of Modelling in Management, 15(4), 1591-1612.
  70. Sohrabi Yourtchi, B., Raeesi Vanaei, I., Roonaghi, M. H., Boghrati, F., & Kargar, M. H. (2016). Business intelligence (comprehensive approach). Ketab Mehraban, Tehran. (In Persian)
  71. Sufiyan, Mohd; Haleem, Abid; Khan, Shahbaz; Khan, Mohd Imran (2019). Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustainable Production and Consumption, 20, 40-57.
  72. SuSa Vugec, D., Bosilj Vuksic‡, V., Pejc‡ Bach, Mi., Jaklc, Jurij., Indihar temberger, Mojc (2020). Business intelligence and organizational performance. Business Process Management Journal, 26(6), 1709-1730.
  73. Taghva, M. R., Jafarian, A., & Shafiei Nikabadi, M. (2012). The Role of Information Security Management Systems in Supply Chain Performance Improvement, Iranian Research Institute For Science and Technology, 27(1), 463-482. (In Persian)
  74. Tarifa-Fernández, J., de-Burgos-Jimenez, J., & Cespedes-Lorente, J. (2019). Absorptive capacity as a confounder of the process of supply chain integration. Business Process Management Journal, 25(7), 1587-1611.
  75. Thai, V., & Jie, F. (2018). The impact of total quality management and supply chain integration on firm performance of container shipping companies in Singapore. Asia Pacific Journal of Marketing and Logistics, 30(3), 605–626.
  76. Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information & Management, 55(7), 822-839.
  77. Trkman, P., McCormack, K., de Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318–327.
  78. Tse, Y. K., Zhang, M., Akhtar, P., & MacBryde, J. (2016). Embracing supply chain agility: an investigation in the electronics industry. Supply Chain Management: An International Journal, 21(1), 140–156.
  79. Um, J. (2016). The impact of supply chain agility on business performance in a high level customization environment. Operations Management Research, 10(1-2), 10–19.
  80. Vanpoucke, E., Vereecke, A., & Muylle, S. (2017). Leveraging the impact of supply chain integration through information technology. International Journal of Operations & Production Management, 37(4), 510–530.
  81. Vickerya, S.K. Drogea, C. Setiab, P. & Sambamurthy V. (2010). Supply chain information technologies and organizational initiatives: complementary versus independent effects on agility and firm performance. International Journal of Production Research, 48(23), 7025–7042.
  82. Yiu, L.M.D., Yeung, A.C.L. and Jong, A.P.L. (2020). Business intelligence systems and operational capability: an empirical analysis of high-tech sectors. Industrial Management & Data Systems, 120(6), 1195-1215.
  83. Yu, Y., Huo, B. and Zhang, Z. (2020). Impact of information technology on supply chain integration and company performance: evidence from cross-border e-commerce companies in China. Journal of Enterprise Information Management, ahead-of-print.
  84. Yuen, K. F., Wang, X., Ma, F., Lee, G., & Li, X. (2019). Critical success factors of supply chain integration in container shipping: an application of resource-based view theory. Maritime Policy & Management, 46(6), 1–16.
  85. Yusuf, Y. Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N. M., & Cang, S. (2014). A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry. International Journal of Production Economics, 147, 531–543.
  86. Zhang, M., Lettice, F., Chan, H. K., & Nguyen, H. T. (2018). Supplier Integration and Firm Performance: The Moderating Effects of Internal Integration and Trust. Production Planning & Control, 29(10), 802–813.
  87. Zhu, Q., Krikke, H., & Caniëls, M. C. J. (2018). Supply chain integration: value creation through managing inter-organizational learning. International Journal of Operations & Production Management, 38(1), 211–229.
آمار
تعداد مشاهده مقاله: 5,557
تعداد دریافت فایل اصل مقاله: 3,460