Advancing Intelligent Supply Chain Management in the Industry 4.0 Era: A Meta-Synthesis Analysis

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 Assistant Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.

2 Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.

3 Ph.D. Candidate, College of Kish, University of Tehran, Tehran, Iran.

10.22059/imj.2024.381349.1008176

چکیده

Objective: In the Fourth Industrial Revolution, advanced technologies are revolutionizing supply chains by enhancing data collection, processing, and analysis across material, financial, and information flows. This shift enables businesses to adopt intelligent supply chain processes with unprecedented efficiency. The integration of intelligent strategies and process-oriented approaches, supported by tools like Intelligent Business Process Management Systems (iBPMS), holds transformative potential for supply chain management, paving the way for Intelligent Supply Chain Management (iSCM) models. This study aims to identify the key dimensions and sub-dimensions of intelligent supply chain processes within the context of Industry 4.0 technologies.
Methods: The research employs a meta-synthesis methodology, systematically reviewing peer-reviewed literature and international publications from 2016 to 2025. Following strict meta-synthesis protocols, the study involved keyword screening, thematic evaluation, and iterative refinement, resulting in a curated selection of 62 high-impact journal articles and 4 seminal books. These sources underwent rigorous validation to ensure scholarly relevance before analysis. 
Results: The findings identified 117 open codes related to intelligent supply chain processes, which were consolidated into 18 core codes and further classified into five key dimensions: (1) Intelligent Supply Chain Management (covering SCM and intelligent procurement); (2) Process Intelligent Automation (including automation approaches, intelligent processes, and equipment); (3) Process Management (focused on process-oriented approaches, systems, and modeling); (4) Technological Infrastructure (encompassing emerging technologies, ICT infrastructure, software maturity, and robotics); and (5) Macro & Structural Dimensions (addressing managerial, industrial, e-business, market, and organizational factors).
Conclusion: The study concludes that Industry 4.0 technologies—such as IoT, AI, blockchain, robotics, and big data analytics—facilitate advanced data-driven supply chain management. When integrated with iBPMS, these innovations enhance efficiency, agility, and end-to-end visibility, establishing a foundation for next-generation intelligent supply chains

کلیدواژه‌ها

عنوان مقاله [English]

Advancing Intelligent Supply Chain Management in the Industry 4.0 Era: A Meta-Synthesis Analysis

نویسندگان [English]

  • Rohollah Ghasemi 1
  • Ali Mohaghar 2
  • Mohammad Mehdi Dehghanian 3
1 Assistant Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.
2 Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.
3 Ph.D. Candidate, College of Kish, University of Tehran, Tehran, Iran.
چکیده [English]

Objective: In the Fourth Industrial Revolution, advanced technologies are revolutionizing supply chains by enhancing data collection, processing, and analysis across material, financial, and information flows. This shift enables businesses to adopt intelligent supply chain processes with unprecedented efficiency. The integration of intelligent strategies and process-oriented approaches, supported by tools like Intelligent Business Process Management Systems (iBPMS), holds transformative potential for supply chain management, paving the way for Intelligent Supply Chain Management (iSCM) models. This study aims to identify the key dimensions and sub-dimensions of intelligent supply chain processes within the context of Industry 4.0 technologies.
Methods: The research employs a meta-synthesis methodology, systematically reviewing peer-reviewed literature and international publications from 2016 to 2025. Following strict meta-synthesis protocols, the study involved keyword screening, thematic evaluation, and iterative refinement, resulting in a curated selection of 62 high-impact journal articles and 4 seminal books. These sources underwent rigorous validation to ensure scholarly relevance before analysis. 
Results: The findings identified 117 open codes related to intelligent supply chain processes, which were consolidated into 18 core codes and further classified into five key dimensions: (1) Intelligent Supply Chain Management (covering SCM and intelligent procurement); (2) Process Intelligent Automation (including automation approaches, intelligent processes, and equipment); (3) Process Management (focused on process-oriented approaches, systems, and modeling); (4) Technological Infrastructure (encompassing emerging technologies, ICT infrastructure, software maturity, and robotics); and (5) Macro & Structural Dimensions (addressing managerial, industrial, e-business, market, and organizational factors).
Conclusion: The study concludes that Industry 4.0 technologies—such as IoT, AI, blockchain, robotics, and big data analytics—facilitate advanced data-driven supply chain management. When integrated with iBPMS, these innovations enhance efficiency, agility, and end-to-end visibility, establishing a foundation for next-generation intelligent supply chains

کلیدواژه‌ها [English]

  • Process Management Systems
  • Intelligent Supply Chain Management
  • Industry 4.0
  • Meta-Synthesis

مراجع

Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems, 86(9), 614-628. Doi: pii/S0167739X1830400X
Abouzid, I., Bekali, Y. K., & Saidi, R. (2022). Modelling IoT Behaviour in Supply Chain Business Processes with BPMN: A Systematic Literature Review. Journal of ICT Standardization, 439-468. Doi: 13657/14703/53341
Adler, C., & Lalonde, C. (2020). Identity, agency, and institutional work in higher education: a qualitative meta-synthesis. Qualitative Research in Organizations and Management, 15(2), 121–144. Doi: 10.1108/qrom-11-2018-1696
Adu-Amankwa, K., Corney, J., Rentizelas, A., & Wodehouse, A. (2022). Intellectual property management challenges of additive manufacturing in replacement part supply chains. IFAC-Papers Online, 55(10), 1527–1532. Retrieved from: https://strathprints.strath.ac.uk/83128/1/
Ahmed, I., Zhang, Y., Jeon, G., Lin, W., Khosravi, M. R., & Qi, L. (2022). A blockchain‐and artificial intelligence‐enabled smart IoT framework for a sustainable city. International Journal of Intelligent Systems, 37(9), 6493–6507. Doi: 10.1002/int.22852
Akanmu, A., & Anumba, C. J. (2015). Cyber-physical systems integration of building information models and the physical construction. Engineering, Construction and Architectural Management, 22(5), 516–535. Doi: 7bbc87a3-a8c5-4594-ab91-b3d3ad247ac9
Asim, Z., Sorooshian, S., Al Shamsi, I. R., Muniyanayaka, D., & Al Azzani, A. (2025). Supply Chain 4.0 A Source of Sustainable Initiative across Food Supply Chain: Trends and Barriers. In Human Perspectives of Industry 4.0 Organizations (pp. 17-37). CRC Press. Doi: 10.1201/9781032616810-2
Basu, R., Das, M. C., Kumar, A., & Sarkar, B. (2025). Identifying and Prioritizing Quality 4.0 Practices in Sustainable Manufacturing Using Rough Number-Based AHP-MABAC. In Handbook of Intelligent and Sustainable Manufacturing (pp. 135-165). CRC Press. Doi: 10.1201/9781003405870-8 
Bazargan, A., Ghasemi, R., Eftekhar Ardebili, M., & Zarei, M. (2017). The relationship between ‘higher education and training’and ‘business sophistication’. Iranian Economic Review, 21(2), 319–341. Doi:  10.22059/ier.2017.62106  
Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 333(2), 627–652. Doi: 10.1007/s10479-021-03956-x
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. Doi: 10.1080/00207543.2017.1402140
Bouanba, N., Barakat, O., & Bendou, A. (2022). Artificial Intelligence & Agile Innovation: Case of Moroccan Logistics Companies. Procedia Computer Science, 203, 444–449. Doi: pii/S1877050922006640
Cheng, C., & Bai, J. (2022). Coping with Multiple Chronic Conditions in the Family Context: A Meta-Synthesis. Western Journal of Nursing Research, 44(10), 972–984. Doi: abs/10.1177/ 01939459211041171
Crowe, M., Gillon, D., Jordan, J., & Mccall, C. (2017). Older peoples’ strategies for coping with chronic non-malignant pain: A qualitative meta-synthesis. International Journal of Nursing Studies, 68, 40–50. Doi: pii/S0020748916302462
Cui, L., Gao, M., Dai, J., & Mou, J. (2022). Improving supply chain collaboration through operational excellence approaches: an IoT perspective. Industrial Management & Data Systems, 122(3), 565-591. Doi: 10.1108/imds-01-2020-0016
Daneshjoovash, S. K., Jafari, P., & Khamseh, A. (2020). Effective commercialization of high-technology entrepreneurial ideas: a meta-synthetic exploration of the literature. Journal of Small Business & Entrepreneurship. Retrieved from: https://members.bestbusinesscoach.ca/wp-content/uploads/2022/11/ Effective-commercialization-of-high-technology-entrepreneurial-ideas-a-meta-synthetic-exploration-of-the-literature.pdf
deVass, T., Shee, H., & Miah,S.J. (2021). IoT in supply chain management: Opportunities and challenges for businesses in early industry4.0context. Operations and Supply Chain Management: An International Journal,14(2),148-161. Retrieved from: https://www.journal.oscm-forum.org/publication/article/ iot-in-supply-chain-management-opportunities-and-challenges-for-businesses-in-early-industry-4.0-con
Domingos, D., & Martins, F. (2017). Using BPMN to model Internet of Things behavior within a business process. International Journal of Information Systems and Project Management, 5(4), 39–51. Retrieved from: https://revistas.uminho.pt/index.php/ijispm/article/view/3838
Dwivedi, A., Agrawal, D., Jha, A., & Mathiyazhagan, K. (2023). Studying the interactions among Industry 5.0 and circular supply chain: Towards attaining sustainable development. Computers & Industrial Engineering, 176, 108927. Doi: abs/pii/S0360835222009159
Eyilmez, Y. (2024). The Effectiveness of IBPM (Intelligent Business Process Management) on CRM Compared to BPM. Doi: 8cde4334-26aa-4326-a564-f9a5a66cb05a
Farahani, P., Meier, C., & Wilke, J. (2017). Digital supply chain management agenda for the automotive supplier industry. In Shaping the digital enterprise (pp. 157–172). Springer, Cham. Doi: 10.1007/978-3-319-40967-2_8
Farshidi, S., Kwantes, I. B., & Jansen, S. (2024). Business process modeling language selection for research modelers. Software and Systems Modeling, 23(1), 137–162. Doi:10.1007/s10270-023-01110-8
 Feldmann, K., Franke, J., & Schüßler, F. (2010). Development of micro assembly processes for further miniaturization in electronics production. CIRP Annals - Manufacturing Technology, 59(1), 1–4. Doi: pii/S0007850610000065
Finfgeld-Connett, D. (2018). A guide to qualitative meta-synthesis (Vol. 10). New York, NY, USA: Routledge. Doi: h10.4324/9781351212793
Friede, G. (2019). Why don’t we see more action? A Metasynthesis of the investor impediments to integrate environmental, social, and governance factors. Business Strategy and the Environment, 1–23. Doi: abs/10.1002/bse.2346
García-Reyes, H., Avilés-González, J., & Avilés-Sacoto, S. V. (2022). A Model to Become a Supply Chain 4.0 Based on a Digital Maturity Perspective. Procedia Computer Science, 200, 1058-1067. Doi: pii/S1877050922003143
Ghadge, A., Karantoni, G., Chaudhuri, A., & Srinivasan, A. (2018). Impact of additive manufacturing on aircraft supply chain performance: A system dynamics approach. Journal of Manufacturing Technology Management, 29(5), 846–865. Doi: 10.1108/jmtm-07-2017-0143
Ghasemi, R., Alidoosti, A., Hosnavi, R., & Norouzian Reykandeh, J. (2018). Identifying and prioritizing humanitarian supply chain practices to supply food before an earthquake. Industrial management journal, 10(1), 1–16. (In Persian). Doi: 10.22059/imj.2018.234645.1007246
Ghasemi, R., Hashemi–Petroudi, S. H., Mahbanooei, B., & Mousavi–Kiasari, Z. (2013). Relationship between Infrastructure and Technological Readiness based on Global Competitiveness Report: A Guidance for Developing Countries, 1st International. In the 7th National Conference on Electronic Commerce & Economy (pp. 19–21). Doi: publication/236892715
Ghasemi, R., Mahbanooei, B., & Beigi, R. G. (2018). The Relationship between Labor Market Efficiency and Innovation. In Proceedings of the 11th International Seminar on Industrial Engineering & Management (ISIEM), (Nov. 27-29, 2018, Makassar, Indonesia) (pp. 142–149). Doi: 2019/08/ 11ISIEM2018.126
Ghasemi, R., Mohaghar, A., Safari, H., & Akbari Jokar, M. R. (2016). Prioritizing the applications of internet of things technology in the healthcare sector in Iran: A driver for sustainable development. Journal of Information Technology Management, 8(1), 155–176. Doi: 10.22059/ jitm.2016.55760
Govindan, K., Kannan, D., Jørgensen, T. B., & Nielsen, T. S. (2022). Supply chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence. Transportation Research Part E: Logistics and Transportation Review, 164, 102725. Doi: pii/ S1366554522001168
Grambow, G., Hieber, D., Oberhauser, R., & Pogolski, C. (2021). A context and augmented reality BPMN and BPMS extension for industrial internet of things processes. In International Conference on Business Process Management (pp. 379-390). Cham: Springer International Publishing. Retrieved from: publication/file/10386/ 
Haghighi, S. M., Torabi, S. A., & Ghasemi, R. (2016). An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). Journal of Cleaner Production, 137, 579–597. Doi: pii/S0959652616310150
Hall, H., Leach, M., Brosnan, C., & Collins, M. (2017). Nurses’ attitudes towards complementary therapies: A systematic review and meta-synthesis. International Journal of Nursing Studies, 69, 47–56. Doi: pii/S0020748917300214
Hao, X. (2023). Examining Collaborative Business Process Modeling Techniques. Journal of Enterprise and Business Intelligence, 3(2), 075–084. Retrieved from: https://anapub.co.ke/journals/jebi/ jebi_abstract/2023/jebi_volume_03_issue_02/jebi_volume3_issue2_2.html
Heguy, X., Tazi, S., Zacharewicz, G., & Ducq, Y. (2024). Tracking Interoperability and Data Quality: A Methodology with BPMN 2.0 Extensions and Performance Evaluation. Modelling, 5(3), 797-818. Doi: 2673-3951/5/3/42
Hernandez, M. C., Alvarez, A. N. R., & Anguiano, F. I. S. (2023). Project management and supply chain 4.0 improvement: the case of infant formulas in the face of the challenge of COVID-19. Procedia Computer Science, 217, 278-285. Doi: pii/S1877050922023018
Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34. Doi: pii/S0166361517301902
Hoorshad, A., Safari, H., & Ghasemi, R. (2023). Developing Smart Supply Chain Management Model in Fast-moving Consumer Goods Industry (FMCG). Journal of Industrial Management Perspective, 13(4), 108–148. Doi: 10.48308/jimp.13.4.108
Horton, L. (2020). Making qualitative data more visible in policy: A critical appraisal of meta-synthesis. Qualitative Research, 20(5), 534–548. Doi: abs/10.1177/1468794119881953
Jafarnejad, A., Ghasemi, R., & Abdullahi, B. (2011). Relationship between “Financial Market Development” and “Technological Readiness” based on Global Competitiveness Report: A Guidance for Developing Countries, 1st International. In the 5th National Conference on Management of Technology (pp. 23–25). Doi: publication/315597059  
Jafarnejad, A., Ghasemi, R., & Ghasemi, M. R. (2010). Developing and designing a new technology for drilling in a small area. In 4th National Conference on Management of Technology, Tehran, Iran, 8th & 9th Nov. Doi: publication/315619822
Jafarnejad, A., Ghasemi, R., Abdollahi, B., & Esmailzadeh, A. (2013). Relationship between macroeconomic environment and technological readiness: A secondary analysis of countries' global competitiveness. International Journal of Management Perspective. 1(2), 1–13. Doi: publication/ 315573973 
Jafarnejad, A., Rahayu, G. H. N. N., Ghasemi, R., & Bahrami, F. (2014). Relationship between knowledge management process capabilities and supply chain relations quality. In 6th International Conference on Operations and Supply Chain Management, (pp. 1072–1085). Doi: publication/315551645_ 
Jain, R. K., & Samanta, S. K. (2025). Application of IoT and Artificial Intelligence in Smart Manufacturing: Towards Industry 4.0. In Handbook of Intelligent and Sustainable Manufacturing, (pp. 235–259). CRC Press. Doi: 10.1201/9781003405870-14
Jamalian, A., Ghadikolaei, A. S., Zarei, M., & Ghasemi, R. (2018). Sustainable supplier selection by way of managing knowledge: a case of the automotive industry. International Journal of Intelligent Enterprise, 5(1-2), 125–140. Doi: abs/10.1504/IJIE.2018.091186
Karanam, S. D., Kamath, R. S., Kulkarni, R. V. R., & Pai, B. H. S. K. (2021). Big data integration solutions in organizations: A domain-specific analysis. Data Integrity and Quality, 1–31. Retrieved from: https://www.intechopen.com/chapters/75007
Karimi, T., Azar, A., Mohebban, B., & Ghasemi, R. (2022). Developing an Internet of Things-based intelligent transportation technology roadmap in the food cold supply chain. Industrial Management Journal, 14(2), 195–219. (In Persian). Doi: 10.22059/imj.2021.319427.1007825
Kehayov, M., Holder, L., & Koch, V. (2022). Application of artificial intelligence technology in the manufacturing process and purchasing and supply management. Procedia Computer Science, 200, 1209-1217. Doi: pii/S1877050922003301
Khan, M. D., Schaefer, D., & Milisavljevic-Syed, J. (2022). Supply Chain Management 4.0: Looking Backward, Looking Forward. Procedia CIRP, 107, 9-14. Doi: pii/S2212827122002189
Kim, B., & Jeong, J. (2022). Blockchain-based Real-time Information Management in Remicon Manufacturing Process. Procedia Computer Science, 203, 135-140. Doi: pii/S187705092200624X
Kocsi, B., Matonya, M. M., Pusztai, L. P., & Budai, I. (2020). Real-time decision-support system for high-mix low-volume production scheduling in Industry 4.0. Processes, 8(8), 912. Doi: 2227-9717/8/8/912
Korpysa, J., & Halicki, M. (2022). Project supply chain management and fintech startups–relationship. Procedia Computer Science, 207, 4419-4427. Doi: pii/S1877050922014028
Kumi, S., Lomotey, R. K., & Deters, R. (2022). A Blockchain-based platform for data management and sharing. Procedia Computer Science, 203, 95-102. Doi: pii/S1877050922006196
Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & information systems engineering, 6, 239–242. Doi: 10.1007/s12599-014-0334-4
Lazazzara, A., Tims, M., & De Gennaro, D. (2020). The process of reinventing a job: A meta–synthesis of qualitative job crafting research. Journal of Vocational Behavior, 116, 103267. Doi: abs/pii/S0001879119300016
Liao, S. H., & Tasi, Y. S. (2019). Big data analysis on the business process and management for the store layout and bundling sales. Business Process Management Journal, 25(7), 1783-1801. Doi: 10.1108/ bpmj-01-2018-0027
Lin, H., Lin, J., &Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), 100276. Technology and Entrepreneurship, 2(1), 100032. Doi: d36763d90b2d4fff83feff1579996997
Maiyar, L. M. (2022). Link between Industry 4.0 and green supply chain management: Evidence from the automotive industry. Doi: pii/S036083522200362X
Mehregan, M. R., Ghasemi, R., Amirnequiee, S., & Zarei, M. (2016, December). Developing DEMATEL-CCA Hybrid Algorithm Approach to Analyze the Causal Relations on Global Competitiveness Pillars. In the 4th International Conference on Strategic Management, Faculty of Management, University of Tehran. Retrieved from: https://www.sid.ir/fileserver/se/539e20160405.pdf
Meyer, S., Ruppen, A., & Hilty, L. (2015). The things of the Internet of Things in BPMN. In Advanced Information Systems Engineering Workshops: CAiSE 2015 International Workshops, Stockholm, Sweden, June 8-9, 2015, Proceedings 27 (pp. 285–297). Springer International Publishing. Retrieved from: https://www.zora.uzh.ch/id/eprint/119761/
Mezgebe, T. T., Gebreslassie, M. G., Sibhato, H., & Bahta, S. T. (2023). Intelligent Manufacturing Eco-system: A Post COVID-19 Recovery and Growth opportunity for manufacturing industry in Sub-Saharan Countries. Scientific African, 01547. Doi: ncbi.nlm.nih.gov/36643766/
Mladenova, T. (2020, October). Open-source ERP systems: an overview. In 2020 International Conference on Automatics and Informatics (ICAI) (pp. 1–6). IEEE. Doi: publication/348316044_ 
Mohaghar, A., & Ghasemi, R. (2011). A conceptual model for cooperate strategy and supply chain performance by structural equation modeling a case study in the Iranian automotive industry. European journal of social sciences, 22(4), 519-530. Doi: publication/287383785_ 
Mohaghar, A., Ghasemi, R., & Askarian, A. (2024a). An Agent Based Simulation in Semi-Prepared Food Supply Chain for Export during Coronavirus Pandemic (A Case study in Amadeh-Laziz Company). Journal of Industrial Management Perspective, 14(4), 9–36. Doi: 10.48308/jimp.14.4.9
Mohaghar, A., Ghasemi, R., & Imani, M. H. (2022: a). Developing a Resilient Business Model for Complex Techno-social Organizations by Meta-Synthesis Method. Industrial Management Journal, 14(4), 507-538. Doi: 10.22059/imj.2022.349851.1007993 (In Persian)
Mohaghar, A., Ghasemi, R., Abdullahi, B., Esfandi, N., & Jamalian, A. (2011). Canonical correlation analysis between supply chain relationship quality and cooperative strategy: a case study in the Iranian automotive industry. European Journal of Social Sciences, 26(1), 132–145. Retrieved from: http://article.sapub.org/10.5923.j.logistics.20160501.02.html
Mohaghar, A., Ghasemi, R., Toosi, H., & Sheykhizadeh, M. (2022b). Evaluating the city of knowledge’s project management office functions using BWM and importance‐performance analysis. Journal of Decisions and Operations Research, 7(4), 530–549. Retrieved from: https://www.journal-dmor.ir/article_140216.html?lang=en (In Persian).
Mohaghar, A., Heydarzadeh Moghaddam, H., & Ghasemi, R. (2023a). Developing a Model to Optimize Maximum Coverage of Roadside Units Placement in Vehicular Ad–hoc Network for Intelligent Transportation System. Journal of Industrial Management Perspective, 13(2), 211–240. Doi: 10.48308/jimp.13.2.211
Mohaghar, A., Jafarnezhad, A., Yazdi, M.M. & Sadeghi Moghadam, M. (2012). Presenting an Informational Coordinative and Comprehensive Model in Automobile Supply Network by Using Meta-Synthesis. Information Technology Management, 5(4): 161–194. (In Persian) Doi: 10.22059/jitm.2013.36059  
Mohaghar, A., Mahbanooei, B., & Ghasemi, R. (2023b), Internet of Things governance: Cognitive Map based on experiences of selected countries, 19th International Conference on Management, 19, (pp. 1–13), Tarbiat Modares University, Tehran, Iran, Retrieved from: https://imc.isu.ac.ir/fa/ (Accessed at Jan.12.2025). (In Persian)
Mohaghar, A., Sadeghi Moghadam, M. R., Ghourchi Beigi, R., & Ghasemi, R. (2021). IoT-based services in the banking industry using a business continuity management approach. Journal of Information Technology Management, 13(4), 16–38. Doi: 10.22059/jitm.2021.314908.2666
Mohaghar, A., Safari, H., Ghasemi, R., Abdullahi, B., & Maleki, M. H. (2011). Canonical correlation analysis between supply chain relationship quality and supply chain performance: A case study in the Iranian automotive industry. International Bulletin of Business Administration, 10(10), 122–134. Doi: publication/312488805_ 
Mohaghar, A., Safari, H., Ghasemi, R., Zarei, A. (2024b). Model of Financial Supply Chain Flexibility for automotive Leasing Industry in Iran, Industrial Management Journal, 16(3), 398–425. Doi: 10.22059/imj.2024.373340.1008130
Mohammadzadeh, A. K., Ghafoori, S., Mohammadian, A., Mohammadkazemi, R., Mahbanooei, B., & Ghasemi, R. (2018). A Fuzzy Analytic Network Process (FANP) approach for prioritizing internet of things challenges in Iran. Technology in Society, 53, 124-134. Doi: pii/S0160791X1730194X
Morgner, R., & Burger, M. (2024). Siemens: Acting Resiliently Through Hybrid Process Intelligence in the Supply Chain Metaverse. In Process Intelligence in Action: Taking Process Mining to the Next Level (pp. 175-191). Cham: Springer Nature Switzerland. Doi: 10.1007/978-3-031-61343-2_19
Nagarajan, S. M., Deverajan, G. G., Chatterjee, P., Alnumay, W., & Muthukumaran, V. (2022). Integration of IoT based routing process for food supply chain management in sustainable smart cities. Sustainable Cities and Society, 76, 103448. Doi:  abs/2022SusCS..7603448N
Najem, R., Amr, M. F., Bahnasse, A., & Talea, M. (2022). Artificial intelligence for digital finance,axes and techniques. Procedia Computer Science, 203, 633-638. Doi: 608006f1-d92d-369b-9b01-8cb848bfd736
Nasrollahi, M., Ghadikolaei, A. S., Ghasemi, R., Sheykhizadeh, M., & Abdi, M. (2022). Identification and prioritization of connected vehicle technologies for sustainable development in Iran. Technology in Society, 68, 101829. Doi: abs/pii/S0160791X21003043
Paterson, B. L., Dubouloz, C. J., Chevrier, J., Ashe, B., King, J., & Moldoveanu, M. (2009). Conducting qualitative metasynthesis research: Insights from a metasynthesis project. International Journal of Qualitative Methods, 8(3), 22-33. Retrieved from: https://journals.library.ualberta.ca/ijqm/index.php/ IJQM/article/view/5100
Pavlicek, J., Pavlickova, P., Pokorná, A., & Brnka, M. (2023, June). Business Process Models and Eye Tracking System for BPMN Evaluation-Usability Study. In International Workshop on Model-Driven Organizational and Business Agility (pp. 53-64). Cham: Springer Nature Switzerland. Retrieved from: https://ouci.dntb.gov.ua/en/works/lDPwZjgl/
Pourezzat, A. A., Mahbanooei, B., Ghasemi, R., Rafiei, R (2022), Governance Performance Evaluation System, University of Tehran Press, Tehran: Iran. Doi: publication/361391504_ 
Pozzo, D. N., Correa, K.R., Madrid, A.I.C., Campo, C.J.C., Donado, M.E.G., & Biegelmeyer, U.H. (2022). Logistics 4.0: a review of current trends using bibliometric analysis. Procedia Computer Science, 203, 531-536. Doi: 983e95c0-3383-498a-a1e9-5d24789e0bf2
Rastegar, A. A., Mahbanooei, B., & Ghasemi, R. (2012, May). Canonical correlation analysis between technological readiness and labor market efficiency: A secondary analysis of countries global competitiveness in 2011–2012. In 13th International Conference on Econometrics, Operations Research and Statistics (ICEOS-2012) (pp. 24-26). Doi: publication/315572228_ 
Razavi, S. M., Abdi, M., Amirnequiee, S., & Ghasemi, R. (2016). The impact of supply chain relationship quality and cooperative strategy on strategic purchasing. Journal of Logistics Management, 5(1), 6-15. Doi: 10.5923.j.logistics.20160501.02.html
Razavi, S., Mostafa, G., Rohollah, A. B., & Kashani, M. (2011). Relationship between technological readiness and innovation: A secondary analysis of countries global competitiveness. European Journal of Scientific Research, 59(3), 318-328. Retrieved from: https://www.researchgate.net/ figure/Correlation-coefficient-between-Technological-readiness-and-Innovation_tbl3_315600630
Rezaeefard, M., Pilevari, N., Razi, F. F., & Radfar, R. (2022). Reducing the bullwhip effect in supply chain with factors affecting the customer demand forecasting. International Journal of Services Operations and Informatics, 12(2), 144-183. Retrieved from: https://iors.ir/journal/article-1-801-en.html
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135. Doi: 10.1080/00207543.2018.1533261
Sadeghi Moghadam, M. R., Norouzian Reykandeh, J., & Ghasemi, R. (2017). Explanation of the importance-performance dimensions and components of humanitarian supply chain in post-disaster. Organizational resources management researches, 7(3), 157-176. Doi:  20.1001.1.22286977.1396.7.3.8.7  
Saghafi, F., Mohaghar, A. & Kashiha, M. (2020). Presenting technological catch-up framework based on grounded theory and meta-synthesis. Management Research in Iran, 24(1), 107- 129. Retrieved from: https://mri.modares.ac.ir/article_519.html?lang=en 
Sheykhzadeh, M., Ghasemi, R., Vandchali, H. R., Sepehri, A., & Torabi, S. A. (2024). A hybrid decision-making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: A case study of a pharmaceutical industry. Environment, Development and Sustainability, 1-28. Doi:10.1007/s10668-023-04135-7
Susithra, S., & Vasantha, S. (2024). The Adoption of Green Supply Chain Practices using Artificial Intelligence (AI) for Smart Global Value Chain. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-8). IEEE. Retrieved from: https://ir.vistas.ac.in/id/eprint/9197/
Szelągowski, M., & Berniak-Woźny, J. (2024). BPM challenges, limitations and future development directions–a systematic literature review. Business Process Management Journal, 30(2), 505-557. Doi: 10.1108/bpmj-06-2023-0419
Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does industry 4.0 mean to supply chain?, Procedia Manufacturing, 13, 1175–1182. Doi: pii/S2351978917308302
Valderas, P., Torres, V., & Serral, E. (2022). Modelling and executing IoT-enhanced business processes through BPMN and microservices. Journal of Systems and Software, 184, 111139. Doi: 10.1016/j.jss. 2021.111139
Wang, J., Omar, A., Al Mheiri, A., Hassan, H., Nassif, R., & Mukluf, Y. (2017). Cloud Computing, Intelligent Business Process Management and Artificial Intelligence. International journal of data analysis and information systems, 9, 1-12. Retrieved from:  https://www.serialsjournals.com/ volumearticle&issue_id=142&product_id=395
Xue, J., Zhang, W., Rasool, Z., & Zhou, J. (2022). A review of supply chain coordination management based on bibliometric data. Alexandria Engineering Journal, 61(12), 10837-10850. Doi: pii/S1110016822002757
Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2022). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering, 108859.Doi: pii/S0360835222008476
Yousefi, D., Yousefi, J., & Ghasemi, R. (2024). Key Success Factors to Implement IoT in the Food Supply Chain. Journal of Information Technology Management. 16(3), 61-91. Doi: 10.22059/jitm.2024.372404.3618
Zadtootaghaj, P., Mohammadian, A., Mahbanooei, B., & Ghasemi, R. (2019). Internet of Things: A Survey for the Individuals' E-Health Applications. Journal of Information Technology Management, 11(1), 102-129. Doi: 10.22059/jitm.2019.288695.2398
Zamani, M., Ghorchibeigi, R., & Ghasemi, R. (2018). Identifying the requirements and applications of Internet of things (IoT) in the banking industry based on international experience. In 7th National Conference on Electronic Banking and Payment Systems, Tehran, Iran. Doi: publication/346910535 
Zarei, M., Jamalian, A., & Ghasemi, R. (2017). Industrial guidelines for stimulating entrepreneurship with the internet of things. In The Internet of Things in the Modern Business Environment (pp. 147-166). IGI Global. Retrieved from: https://www.igi-global.com/chapter/industrial-guidelines-for-stimulating-entrepreneurship-with-the-internet-of-things/180739
Zarei, M., Mohammadian, A., & Ghasemi, R. (2016). Internet of things in industries: A survey for sustainable development. International Journal of Innovation and Sustainable Development, 10(4), 419-442. Doi: abs/10.1504/IJISD.2016.079586
Zavantis, D., Outay, F., El-Hansali, Y., Yasar, A., Shakshuki, E., & Malik, H. (2022). Autonomous Driving and Connected Mobility Modeling: Smart Dynamic Traffic Monitoring and Enforcement System for Connected and Autonomous Mobility. Procedia Computer Science, 203, 213-221. Doi: pii/S1877050922006330
Zhang, G., Yang, Y., & Yang, G. (2022). Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America. Annals of Operations Research, 1-43. Doi: 10.1007/s10479-022-04689-1
Zhang, H., & Xiao, J. (2020). Quality assessment framework for open government data Metasynthesis of qualitative research, 2009-2019. The Electronic Library, 38(2), 209–222. Doi: 10.1108/el-06-2019-0145.
Zheng, K., Zheng, L.J., Gauthier, J., Zhou, L., Xu, Y., Behl, A., & Zhang, J.Z. (2022). Block chain technology for enterprise credit information sharing in supply chain finance. Journal of Innovation & Knowledge, 7(4), 100256. Doi: pii/S2444569X22000919
Zhong, R.Y., Newman, S.T., Huang, G.Q., & Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Computers and Industrial Engineering, 101 ,572-591. Doi: 10.1016/j.cie.2016.07.013
Zhou, L.i., Chong, A. Y. L., & Ngai, E.W.T. (2015). Supply chain management in the era of the internet of things. International Journal of Production Economics, 159, 1–3. Retrieved from: https://www. sciencedirect.com/journal/international-journal-of-production-economics/vol/159/suppl/C
 
مدیریت صنعتی
دوره 17، شماره 2
1404
صفحه 148-117

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