Design and Implementation of Organizational Architecture in Organizations in Charge of Combating Smuggling of Goods and Currency with the Aim of Improving the Management of Organizational Networks

Document Type : Research Paper


1 Department of Information Technology Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran


In the current situation, one of the concerns in the fight against smuggling of goods and currency is the improvement of the inter-organizational network. The purpose of this research is to design and implement organizational architecture to improve the management of organizational networks with SWOT approach, in this area using the artificial neural network toolbox and fuzzy logic in Matlab. This research is applied-modeling in terms of purpose. The statistical population includes expert professors and experts of organizations in charge of combating smuggling of goods and currency. After distributing 100 questionnaires, the sample size of this study is equal to 96 experts who were selected by a combination of two methods of non-probabilistic purposive sampling and snowball sampling. The results show that using the intelligent system, the status of "success of the organization's network management" can be examined numerically and more accurately: In terms of ideal importance, if; The "Network Management Based on EA Application Layer" status is good, ie
exactly 0.813, and "Network management based on EA data source layer" is good, ie exactly 0.824, and "Network Management Based on EA Central Component Layer" is good, ie exactly 0.819, and "Network Management Based on EA Data Preparation Layer" is good, ie exactly 0.812, and "Network management based on EA service quality layer" in good condition, ie exactly 0.815;Then; The status of "successful implementation of the organization's network management" is at the top level (fifth level), ie exactly 0.952.According to the membership functions of language variables by experts, the value of 4.76 within the 5-value range in the range defined for the "excellent" language variable, ie the success status of the organization's network management, has been calculated exactly 0.952.


Abraham, A. (2005), "Adaptation of Fuzzy Inference System Using Neural Learning", in Nedjah, Nadia; de Macedo Mourelle, Luiza, Fuzzy Systems Engineering: Theory and Practice, Studies in Fuzziness and Soft Computing, 181, Germany: Springer Verlag, pp. 53–83, doi:10.1007/11339366_3
Ahlemann, Frederik, et al. 2020. A resource-based perspective of value generation through enterprise architecture management. Information & Management In press, corrected proof Available online 23 January 2020Article 103266
Alahyari, M., & Pilevari, N. (2021). CO-Active Neuro-Fuzzy Inference System Application in Supply Chain Sustainability Assessment Based on Economic, Social, Environmental, and Governance Pillars. Journal of System Management, 6(3), 265-287.‏
Alwadain, Ayed, et al. 2016. Empirical insights into the development of a service-oriented enterprise architecture. Data & Knowledge EngineeringVolume 105September 2016Pages 39-52
Azar, Adel and Hojjat Faraji. 2010. Fuzzy Management Science. Kind Book Publishing Institute. Iran Center for Management and Productivity Studies (affiliated to Tarbiat Modares University). fourth edition.
Azzedin, F., & Ghaleb, M. (2019). Towards an architecture for handling big data in oil and gas industries : Service-oriented approach. International Journal of Advanced Computer Science and Applications (IJACSA), 10(2).
Bhattacharya, Prithvi. 2018. Aligning Enterprise Systems Capabilities with Business Strategy: An extension of the Strategic Alignment Model (SAM) using Enterprise Architecture. Procedia Computer Science Volume 1382018Pages 655-662.
Cap, Jan-Patrick, et al. 2019. Multi level network management – A method for managing inter-organizational innovation networks. Journal of Engineering and Technology ManagementVolume 51January–March 2019Pages 21-32
Castillo, Ricardo Pérez, et al. 2020. A decision-making support system for Enterprise Architecture Modelling. Decision Support SystemsIn press, corrected proof Available online 20 January 2020Article 113249
Dantu, Bharath & Eric Smith. 2011. Medical Process Modeling with a Hybrid System Dynamics Zachman Framework. Procedia Computer Science, Volume 6, 2011, Pages 76-81.
Dumitriu, Dan, et al. 2020. Enterprise Architecture Framework Design in IT Management. Procedia Manufacturing2020.
Griffo, Cristine, et al. 2019. Service contract modeling in Enterprise Architecture: An ontology-based approach. Information SystemsIn press, corrected proof Available online 18 October 2019Article 101454.
Hinkelmann, Knut, et al. 2016. A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology. Computers in Industry, Volume 79, June 2016, Pages 77-86.
Huang, Mingfeng, et al. 2020. An effective service-oriented networking management architecture for 5G-enabled internet of things. Computer Networks22 May 2020.
Izadi, M., Noorossana, R., Izadbakhsh, H., Saati, S., & Khalilzadeh, M. (2020). Z-Cognitive Map: An Integrated Cognitive Maps and Z-Numbers Approach under Cognitive Information. Journal of System Management, 6(2), 81-102.‏
Kandjani, Hadi, Lian Wen & Peter Bernus. 2012. Enterprise Architecture Cybernetics for Collaborative Networks: Reducing the Structural Complexity and Transaction Cost via Virtual Brokerage. IFAC Proceedings Volumes, Volume 45, Issue 6, 23–25 May 2012, Pages 1233-1239.
Lapalme, James, et al. 2016. Exploring the future of enterprise architecture: A Zachman perspective. Computers in Industry, Volume 79, June 2016, Pages 103-113.
Lin, Chin-Teng & C. S. George Lee. 1996. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems Hardcover –Publisher: Prentice Hall; Har/Dskt edition (May 1, 1996).
Manikannan, K & V Nagarajan. 2020. Optimized mobility management for RPL/6LoWPAN based IoT network architecture using the firefly algorithm. Microprocessors and MicrosystemsSeptember 2020.
Marchiori, Danilo & Mário Franco. 2020. Knowledge transfer in the context of inter-organizational networks: Foundations and intellectual structures. Journal of Innovation & KnowledgeVolume 5, Issue 2April–June 2020Pages 130-139
Mirsalari, Seyedeh Reyhaneh & Mina Ranjbarfard. 2020. A model for evaluation of enterprise architecture quality. Evaluation and Program Planning December 2020.
Mishra, Rabi Narayan & Kanungo Barada Mohanty. 2016. Real time implementation of an ANFIS-based induction motor drive via feedback linearization for performance enhancement. Engineering Science & Technology, an International Journal, In Press, Corrected Proof, Available online 28 September 2016.
Moayer, Sorousha & Parisa A. Bahri. 2009. Hybrid intelligent scenario generator for business strategic planning by using ANFIS. Expert Systems with Applications, Volume 36, Issue 4, May 2009, Pages 7729-7737
Namugenyi, Christine, et al. 2019. Design of a SWOT Analysis Model and its Evaluation in Diverse Digital Business Ecosystem Contexts. Procedia Computer Science 2019.
Närman, Per, et al. 2013. Using enterprise architecture analysis and interview data to estimate service response time. The Journal of Strategic Information Systems Volume 22, Issue 1March 2013Pages 70-85.
Newman, Mark. 2018. Networks-Second Edition. University of Michigan. DOI: 10.1093/oso/9780198805090.001.0001
Randolph, Robert V. et al. 2020. Better the devil you know: Inter-organizational information technology and network social capital in coopetition networks. Information & ManagementVolume 57, Issue 6 September 2020 Article 103344.
Sanjotvar Rajaskaran, Viji Alakshmi Pai, Mahmoud Keshavarzmehr (translator). 2012. Neural Networks, Fuzzy Logic, Genetic Algorithm: Composition and Application. Publisher: Nov Pardazan. ISBN: 978-964-975-162-7.
Stoller, James K. 2020. A Perspective on the Educational “SWOT” of the Coronavirus Pandemic. ChestIn press, uncorrected proof Available online 18 September 2020.
Takeuchi, Hironori & Shuichiro Yamamoto. 2019. AI Service System Development Using Enterprise Architecture Modeling. Procedia Computer Science Volume 1592019 Pages 923-932.
Wang, Qianjin, et al. 2020. Driving amount based stochastic configuration network for industrial process modeling. Neurocomputing Volume 39421 June 2020 Pages 61-69.
Xie, Jin & Ping Zhou. 2020. Robust stochastic configuration network multi-output modeling of molten iron quality in blast furnace ironmaking. Neurocomputing Volume 38728 April 2020 Pages 139-149.
Volume 7, Issue 1
February 2021
Pages 121-153
  • Receive Date: 10 December 2020
  • Revise Date: 30 March 2021
  • Accept Date: 19 April 2021
  • First Publish Date: 02 May 2021