Barriers, Capabilities and Consequences of Supply Chain Intelligence in Iranian Industry

Zahedi, Amir Ehsan; Haghighat, Mahdiyeh

doi:10.22034/jipm.2025.2061431.2019

Barriers, Capabilities and Consequences of Supply Chain Intelligence in Iranian Industry

Document Type : Original Article

Authors

amir ehsan zahedi ¹

mahdiyeh haghighat ²

¹ Management department. Administration sciences and economy faculty. Arak university. Arak. iran

² Department of Industrial and Technological Management, Faculty of Management, Khwarazmi University, Tehran, Iran

10.22034/jipm.2025.2061431.2019

Abstract

Industry 4.0, using advanced technologies such as the internet of things, artificial intelligence, data analytics, robotics, sensors, and smart networks has created new possibilities for businesses to improve production and operational processes automatically and without the need for human intervention. Smart supply chain of Iranian manufacturing and service companies can be very transformative and challenging at the same time. Iranian companies have always sought to improve their supply chain processes with the increasing advancement of technology, but they face many problems due to some obstacles. The present study aims to find the obstacles, capabilities, and consequences of implementing smart supply chains in Iranian industries. The research has a developmental and applied orientation and is qualitative in terms of methodology, and was conducted using the Grounded Theory strategy. The data collection tool is semi-structured interviews, which were conducted by theoretical sampling of experts, and a total of 11 managers of leading industries in smart manufacturing were interviewed to achieve the criterion of "theoretical adequacy". For validation, two methods of participant review and non-participating experts review were used in the research, and after receiving corrective comments, the final model was presented. MAXQDA software was used to implement the Grounded Theory. 372 initial open codes formed 81 concepts that were categorized into 20 categories. Software, hardware, environmental, and organizational requirements are causal conditions for the smart factory (central category). Transparency, optimization, cost efficiency, supply chain agility, and integration are strategies that result from the central category. Uncertainty, relationship breadth, and data explosion, and specific influencing conditions and infrastructure, technology and technical barriers, security issues, financial and economic barriers, organizational and managerial barriers, cultural barriers and environmental barriers are general context conditions that affect strategies. Intra-chain consequences, environmental consequences and financial consequences are the outputs of applying strategies. To digitize the supply chain, organizations need strong and scalable IT infrastructures. These infrastructures include servers, networks, data storage systems and cloud systems that can process large volumes of data. High security is essential to protect sensitive information and prevent cyber threats. Expert human resources must work in an organizational culture that is receptive to innovation and digital change. Human resources must be continuously trained in different fields of new technologies and update their capabilities.

Keywords

Artificial Intelligence

Blockchain

Data Analysis

Industry 4.0

Internet of Things

Smart Supply Chain

Subjects

Economics, Industry, and Market

فهرست منابع

اصلانی لیائی، ولی‌الله، صادق عابدی، علی‌رضا ایرج پور، و رضا احتشام راثی. 1400. ارائه مدلی برای ارزیابی توانمندی‌های چندگانه زنجیره تأمین پایدار بر پایه هوش مصنوعی. چشم‌انداز مدیریت صنعتی 11 (3): 107-129. https://doi.org/10.52547/jimp.11.3.107

References:

Agrawal, P., and R. Narain. 2023. Analysis of enablers for the digitalization of supply chain using an interpretive structural modelling approach. International Journal of Productivity and Performance Management 72 (2): 410-439.‏ https://doi.org/10.1108/IJPPM-09-2020-0481

Ahmad Amouei, M., C. Valmohammadi, and K. Fathi. 2023. Developing and validating an instrument to measure the impact of digital supply chain activities on sustainable performance. Journal of Enterprise Information Management 36 (4): 925-951.‏ https://doi.org/10.1108/JEIM-12-2021-0520

Al Tera, A., A. Alzubi, and K. Iyiola. 2024. Supply chain digitalization and performance: A moderated mediation of supply chain visibility and supply chain survivability. Heliyon 10 (4). ‏ https://doi.org/10,1016 /j.heliyon.2024.e25584

Al-Okaily, M., and A. Al-Okaily. 2025. Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality. Journal of Modelling in Management 20 (2): 301-321.‏ https://doi.org/10.1108/JM2-08-2023-0183

Aslani Liaei, V., S. Abedi, A. Irajpour, and R. Ehtesham Rathi. 2021. Designing a Model for Evaluation of Sustainable Supply Chain Multi Capabilities Based on Artificial Intelligence. Journal of Industrial Management Perspective 11 (3): 107-129. [In Persian] https://doi.org/10.52547/jimp.11.3.107

Attaran, M. 2020. Digital Technology Enablers and their Implications for Supply Chain Management. Supply Chain Forum 21 (3): 158-172. https://doi.org/10.1080/16258312.2020.1751568

Bag, S., P. Dhamija, S. Luthra, and D. Huisingh. 2023. How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. The International Journal of Logistics Management 34 (4): 1141-1164.‏ https://doi.org/10.1108/IJLM-02-2021-0095

Bai, C., P. Dallasega, G. Orzes, and J. Sarkis. 2020. Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics 229: 107776.‏ https://doi.org/10.1016/j.ijpe.2020.107776

Bastos, T., T.C. Teixeira, and L. J. Nunes. 2024. Forest 4.0: technologies and digitalization to create the residual biomass supply chain of the future. Journal of Cleaner Production 467: 143041.‏ https://doi.org/10.1016/j.jclepro.2024.143041

Büchi, G., M. Cugno, and R. Castagnoli. 2020. Smart factory performance and Industry 4.0. Technological Forecasting and Social Change 150: 119790.‏ https://doi.org/10.1016/j.techfore.2019.119790.

Chen, Y., and G. Chen. 2022. Optimization of the intelligent asset management system based on WSN and RFID technology. Journal of Sensor 2022 (1): 3436530.‏ https://doi.org/10.1155/2022/3436530

Creswell, J. W. 2002. Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Upper Saddle River, NJ: Merrill Prentice Hall.

Dadhaneeya, H., P. K. Nema, and V. K. Arora. 2023. Internet of Things in food processing and its potential in Industry 4.0 era: A review. Trends in Food Science & Technology 139: 104109.‏ https://doi.org/10.1016/j.tifs.2023.07.006

Dixit, V. K., R. K. Malviya, V. Kumar, and R. Shankar.2024. An analysis of the strategies for overcoming digital supply chain implementation barriers. Decision Analytics Journal 10: 100389.‏ https://doi.org/10.1016/j.dajour.2023.100389

Dong, Y., S. F. Ahmad, M. Irshad, M. Al-Razgan, Y. A. Ali, and E. M. Awwad. 2023. The Digitalization Paradigm: Impacts on Agri-Food Supply Chain Profitability and Sustainability. Sustainability 15 (21): 15627.‏ https://doi.org/10..3390/su152115627

Fang, M., Y. Yu, K. Park, F. Liu, S. S. Xiao, and Y. Shi. 2024. Supply chain relationship dependencies and circular economy performance: The contingency role of digitalization capability. Journal of Purchasing and Supply Management 30(4): 100902.‏ https://doi.org/10.1016/j.pursup.2024.100902

Ghobakhloo, M., M. Iranmanesh, M. Vilkas, A. Grybauskas, and A. Amran. 2022. Drivers and barriers of Industry 4.0 technology adoption among manufacturing SMEs: a systematic review and transformation roadmap. Journal of Manufacturing Technology Management 33 (6): 1029-1058.‏ https://doi.org/10.1108/JMTM-12-2021-0505

Harju, A., J. Hallikas, M. Immonen, and K. Lintukangas. 2023. The impact of procurement digitalization on supply chain resilience: empirical evidence from Finland. Supply Chain Management: An International Journal 28 (7): 62-76.‏ https://doi.org/10.1108/SCM-08-2022-0312

Hayat, A., V. Shahare, A. K. Sharma, and N. Arora. 2023. Introduction to industry 4.0. In Blockchain and its Applications in Industry 4.0. Singapore: Springer Nature Singapore: 29-59. https://doi.org/10.1007/978-981-19-8730-4_2

Hennelly, P. A., J. S. Srai, G. Graham, and S. Fosso Wamba. 2020. Rethinking supply chains in the age of digitalization. Production Planning & Control 31 (2-3): 93-95. https://doi.org/10.1080/09537287.2019.1631469

Herold, D. M., M. Ćwiklicki, K. Pilch, and J. Mikl. 2021. The emergence and adoption of digitalization in the logistics and supply chain industry: an institutional perspective. Journal of Enterprise Information Management 34(6): 1917-1938.‏ https://doi.org/10.1108/JEIM-09-2020-0382

Hovanec, M., P. Korba, M. Vencel, and S. Al-Rabeei. 2023. Simulating a digital factory and improving production efficiency by using virtual reality technology. Applied Sciences 13 (8): 5118.‏ https://doi.org/‏10.3390/app13085118

Ishfaq, R., B. Davis‐Sramek, and B. Gibson. 2022. Digital supply chains in omnichannel retail: A conceptual framework. Journal of Business Logistics 43 (2): 169-188.‏ https://doi.org/10.1111/jbl.12277

Júnior, L. C. R., G. F. Frederico, and M. L. N. Costa. 2023. Maturity and resilience in supply chains: a systematic review of the literature. International Journal of Industrial Engineering and Operations Management 5 (1): 1-25.‏ https://doi.org/10.1108/IJIEOM-08-2022-0035

Kouhizadeh, M., Q. Zhu, and J. Sarkis. 2020. Blockchain and the circular economy: potential tensions and critical reflections from practice. Production Planning & Control 31 (11-12): 950-966.‏ https://doi.org/10.1080/09537287.2019.1695925

Kumar, A., S. Choudhary, J. A. Garza-Reyes, V. Kumar, S. A. Rehman Khan, and N. Mishra. 2021. Analysis of critical success factors for implementing Industry 4.0 integrated circular supply chain - moving towards sustainable operations. Production Planning & Control 34 (10): 984–998. https://doi.org/10.1080/09537287.2021.1980905

Lee, J. 2001. A grounded theory: integration and internalization in ERP adoption and use. ETD Collection for University of Nebraska-Lincoln AAI3016318.
https://digitalcommons.unl.edu/dissertations/AAI3016318

Li, C., P. Zheng, Y. Yin, B. Wang, and L. Wang. 2023. Deep reinforcement learning in smart manufacturing: A review and prospects. CIRP Journal of Manufacturing Science and Technology 40: 75-101.‏ https://doi.org/10.1016/j.cirpj.2022.11.003

Maiti, A., and R. G. Shilpa. 2020. Developing a framework to digitize supply chain between supplier and manufacturer. In 2020 5th International Conference on Computing, Communication and Security (ICCCS): 1-6.‏ https://doi.org/10.1109/ICCCS49678.2020.9277211

Mishra, R., R. Kr Singh, and A. Gunasekaran. 2024. Digitalization of supply chains in Industry 4.0 environment of manufacturing organizations: conceptualization, scale development & validation. Production Planning & Control 35(11): 1278-1297.‏ https://doi.org/10.1080/09537287.2023.2172622

Monshizadeh, F., M. R. Sadeghi Moghadam, T. Mansouri, and M. Kumar. 2023. Developing an industry 4.0 readiness model using fuzzy cognitive mapsapproach. International Journal of Production Economics 255: 108658. https://doi.org/10.1016/j.ijpe.2022.108658.

Nahr, J. G., H. Nozari, and M. E. Sadeghi. 2021. Green supply chain based on artificial intelligence of things (AIoT). International Journal of Innovation in Management, Economics and Social Sciences 1 (2): 56-63.‏ https://doi.org/10.52547/ijimes.1.2.56

Nguyen, T., M. Akbari, and K. Nguyen.2024. Unlocking the potential of Vietnamese supply chain with digitalization: a bibliometric analysis and systematic literature review. Operations and Supply Chain Management: An International Journal 17 (1):123-141.‏ http://doi.org/10.31387/oscm0560418

Nozari, H., J. Ghahremani Nahr, M. Fallah, and A. Szmelter-Jarosz. 2022. Assessment of cyber risks in an IoT-based supply chain using a fuzzydecision-making method. International Journal of Innovation inManagement Economics and Social Sciences 2 (1): 52-64. https://doi.org/10.52547/ijimes.

Öztürk, Ö. 2023. Analysis of Industry 4.0 Technologies’ Adoption Using Interpretive Structural Modelling: Empirical Findings from Manufacturing Sector in Turkey (Master's thesis, Middle East Technical University (Turkey)).‏

Paiola, M., and H. Gebauer. 2020. Internet of things technologies, digital servitization and business model innovation in BtoB manufacturing firms. Industrial Marketing Management 89: 245-264.‏ https://doi.org/10.1016/j.indmarman.2020.03.009

Pappas, N., A. Caputo, M. M. Pellegrini, G. Marzi, and E. Michopoulou. 2021. The complexity of decision-making processes and IoT adoption in accommodation SMEs. Journal of Business Research 131: 573-583.‏ https://doi.org/10.1016/j.jbusres.2021.01.010

Perano, M., A. Cammarano, V. Varriale, C. Del Regno, F. Michelino, and M. Caputo. 2023. Embracing supply chain digitalization and unphysicalization to enhance supply chain performance: a conceptual framework. International Journal of Physical Distribution & Logistics Management 53 (5/6): 628-659.‏ https://doi.org/10.1108/IJPDLM-06-2022-0201

Pham, T. N., M. T. Tran Hoang, Y. N. Nguyen Tran, and B. A. Nguyen Phan. 2024. Combining digitalization and sustainability: unveiling the relationship of digital maturity degree, sustainable supply chain management practices and performance. International Journal of Productivity and Performance Management 73 (8): 2508-2529.‏ https://doi.org/10.1108/IJPPM-06-2023-0311

Puto, A., M. M. Pellegrini, G. Marzi, and E. Michopoulou. 2021. The complexity of decision-making processes and IoT adoption in accommodation SMEs. Journal of Business Research 131: 573-583.‏ https://doi.org/10.1016/j.jbusres.2021.01.010

Qiao, S., N. N. Xiong, Y. Gao, Z. Fang, W. Yu, J. Zhang, and X. Jiang.2023. Self-supervised learning of depth and ego-motion for 3D perception in human computer interaction. ACM Transactions on Multimedia Computing, Communications and Applications 20 (2): 1-21.‏ https://doi.org/10.1145/3588571

Queiroz, M. M., S. Fosso Wamba, C. J. Chiappetta Jabbour, A. B. Lopes de Sousa Jabbour, and M. C. Machado. 2022. Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective. Annals of Operations Research 6: 1-27.‏ https://doi.org/10.1007/s10479-022-05006-6

Rad, F. F., P. Oghazi, M. Palmié, K. Chirumalla, N. Pashkevich, P. C.Patel, and S. Sattari. 2022. Industry 4.0 and supply chain performance: A systematic literature review of the benefits, challenges, and critical success factors of 11 core technologies. Industrial Marketing Management 105: 268-293.‏ https://doi.org/10.1016/j.indmarman.2022.06.009

Rahamneh, A., S. Alrawashdeh, A. Bawaneh, Z. Alatyat, A. Mohammad, and S. Al-Hawary. 2023. The effect of digital supply chain on lean manufacturing: A structural equation modelling approach. Uncertain Supply Chain Management 11 (1): 391-402.‏ https://doi.org/10.5267/j.uscm.2022.9.003

Rasool, F., M. Greco, and M. Grimaldi. 2022. Digital supply chain performance metrics: a literature review. Measuring Business Excellence 26 (1): 23-38.‏ https://doi.org/10.1108/MBE-11-2020-0147

Salamah, E., A. Alzubi, and A. Yinal. 2023. Unveiling the impact of digitalization on supply chain performance in the post-COVID-19 era: the mediating role of supply chain integration and efficiency. Sustainability 16 (1): 304. ‏ https://doi.org/10.3390/su16010304

Tiwari, S., P. Sharma, and A. K. Jha. 2024. Digitalization & Covid-19: An institutional-contingency theoretic analysis of supply chain digitalization. International Journal of Production Economics 267, 109063.‏ https://doi.org/10.1016/j.ijpe.2023.109063

Tubis, A. A., K. Grzybowska, and B. Król. 2023. Supply chain in the digital age: A Scientometric–Thematic literature review. Sustainability 15 (14): 11391.‏ https://doi.org/10.3390/su151411391

Vaz, A. L. A., F. A. Ferreira, L. F. Pereira, R. J. Correia, and A. Banaitis. 2022. Strategic visualization: the (real) usefulness of cognitive mapping in smart city conceptualization. Management Decision 60 (4): 916-939.‏ https://doi.org/10.1108/MD-11-2020-1512

Wang, S., and J. Li. 2024. How carbon emission trading mechanism and supply chain digitization affect manufacturing enterprises’ competitiveness? Evidence from China. Journal of Cleaner Production 452: 142164.‏ https://doi.org/10.1016/j.jclepro.2024.142164

Yang, M., M. Fu, and Z. Zhang. 2021. The adoption of digital technologies in supply chains: Drivers, process and impact. Technological Forecasting and Social Change 169: 120795.‏ https://doi.org/10.1016/j.techfore.2021.120795.

Zhao, N., J. Hong, and K. H. Lau. 2023. Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model. International Journal of Production Economics 259: 108817.‏ https://doi.org/10.1016/j.ijpe.2023.108817

Zúñiga, E. R., N. Yasue, T. Hirose, H. Nomoto, and T. Sawaragi. 2023. An integrated discrete-event simulation with functional resonance analysis and work domain analysis methods for industry 4.0 implementation. Decision Analytics Journal 9: 100323.‏ https://doi.org/10,.1016/j.dajour.2023..100323.