Ranking of Criteria Affecting the Implementation Readiness of Internet of Things in industries Using TISM and Fuzzy TOPSIS Analysis

Authors

DOI:

https://doi.org/10.31181/jopi31202533

Keywords:

IoT readiness, Fuzzy TOPSIS, Decision Making, TISM, Industry

Abstract

The Internet of Things (IoT) technology has emerged as a vital driver across various fields, engaging businesses, platforms, and industries. IoT involves a holistic ecosystem and a value chain that necessitates the evaluation of impactful dimensions for successful implementation. This research employs the TISM method to identify driver and dependent criteria regarding IoT implementation readiness and uses the fuzzy TOPSIS method to rank these criteria. In the initial step, 15 criteria were identified through a review of previous studies. The TISM results reveal five levels reflecting the driver power and dependence of the criteria. Based on these results, “Implementation Knowledge and Expertise (C2)”, “Technical and Infrastructural Readiness (C9)” and “Financial and Investment Readiness (C12)” were placed at level 5, marking them as the most driver criteria. Additionally, “Implementation Roadmap (C8)” was identified as the most dependent criterion at level one. According to the fuzzy TOPSIS results, “Senior Management Support (C6)”, “IoT Usage Culture (C1)”, “Business Model Development Capability (C15)”, “Financial and Investment Readiness (C12)” and “Technical and Infrastructural Readiness (C9)” ranked first to fifth, respectively. The combined results provide valuable insights for decision-makers and stakeholders involved in IoT implementation. By determining driver and dependent levels and ranking the criteria, industries can effectively prepare for the successful implementation of IoT.

Downloads

Download data is not yet available.

References

Hejazi Dehaghani, S. A., Hajrahimi, B., & Dehaghani Hejazi, S. M. (2020). Providing a model for assessing the readiness of hospitals affiliated to Isfahan University of Medical Sciences in using the Internet of Things (IoT) Technology. Journal of Education and Health Promotion, 9, 201. https://doi.org/10.4103/jehp.jehp_429_19

Majeed, A. A., & Rupasinghe, T. D. (2017). Internet of Things (IoT) Embedded Future Supply Chains for Industry 4.0: An Assessment from an ERP-based Fashion Apparel and Footwear Industry. International Journal of Supply Chain Management, 6(1), Article 1. https://doi.org/10.59160/ijscm.v6i1.1395

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–119. https://doi.org/10.1016/j.compind.2018.06.004

Rizal, R., Selamat, S. R., Mas’ud, M. Z., & Rahmatulloh, A. (2024, September 1). AResNet Model Using Deep Learning Approach for Enhancing the Internet of Things (IoT) Forensic Readiness Framework. | EBSCOhost. https://doi.org/10.22266/ijies2024.1031.71

Fagbola, F. I., & Venter, H. S. (2022). Smart Digital Forensic Readiness Model for Shadow IoT Devices. Applied Sciences, 12(2), Article 2. https://doi.org/10.3390/app12020730

Abazi, B. (2016). An approach to the impact of transformation from the traditional use of ICT to the Internet of Things: How smart solutions can transform SMEs. IFAC-PapersOnLine, 49(29), 148–151. https://doi.org/10.1016/j.ifacol.2016.11.091

Neagu, G., Ianculescu, M., Alexandru, A., Florian, V., & Rădulescu, C. Z. (2019). Next generation IoT and its influence on decision-making. An illustrative case study. Procedia Computer Science, 162, 555–561. https://doi.org/10.1016/j.procs.2019.12.023

Kiel, D., Arnold, C., & Voigt, K.-I. (2017). The influence of the Industrial Internet of Things on business models of established manufacturing companies – A business level perspective. Technovation, 68, 4–19. https://doi.org/10.1016/j.technovation.2017.09.003

Sumrit, D. (2022). Evaluating readiness degree for Industrial Internet of Things adoption in manufacturing enterprises under interval-valued Pythagorean fuzzy approach. Production & Manufacturing Research. https://www.tandfonline.com/doi/abs/10.1080/21693277.2022.2064931

Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411. https://doi.org/10.1016/j.future.2017.11.022

Aazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674–4682. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2018.2855198

Choo, K.-K. R., Gritzalis, S., & Park, J. H. (2018). Cryptographic Solutions for Industrial Internet-of-Things: Research Challenges and Opportunities. IEEE Transactions on Industrial Informatics, 14(8), 3567–3569. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2018.2841049

Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., & Petracca, M. (2017). Industrial Internet of Things monitoring solution for advanced predictive maintenance applications. Journal of Industrial Information Integration, 7, 4–12. https://doi.org/10.1016/j.jii.2017.02.003

Latif, S., Idrees, Z., Ahmad, J., Zheng, L., & Zou, Z. (2021). A blockchain-based architecture for secure and trustworthy operations in the industrial Internet of Things. Journal of Industrial Information Integration, 21, 100190. https://doi.org/10.1016/j.jii.2020.100190

Ahmadirad, Z. (2024). The Banking and Investment in the Future: Unveiling Opportunities and Research Necessities for Long-Term Growth. International Journal of Applied Research in Management, Economics and Accounting, 1(2), Article 2. https://doi.org/10.63pahi053/ijmea.7

Malik, P. K., Sharma, R., Singh, R., Gehlot, A., Satapathy, S. C., Alnumay, W. S., Pelusi, D., Ghosh, U., & Nayak, J. (2021). Industrial Internet of Things and its Applications in Industry 4.0: State of The Art. Computer Communications, 166, 125–139. https://doi.org/10.1016/j.comcom.2020.11.016

Haseli, G., Sheikh, R., & Sana, S. S. (2020). Base-criterion on multi-criteria decision-making method and its applications. International Journal of Management Science and Engineering Management, 15(2), 79–88. https://doi.org/10.1080/17509653.2019.1633964

Kumar, R., Sindhwani, R., & Singh, P. L. (2021). IIoT implementation challenges: Analysis and mitigation by blockchain. Journal of Global Operations and Strategic Sourcing, 15(3), 363–379. https://doi.org/10.1108/JGOSS-08-2021-0056

Haseli, G., & Sheikh, R. (2022). Base criterion method (BCM). In Multiple criteria decision making: Techniques, Analysis and Applications (pp. 17–38). Springer. https://doi.org/10.1007/978-981-16-7414-3_2

Sodhro, A. H., Pirbhulal, S., Muzammal, M., & Zongwei, L. (2020). Towards Blockchain-Enabled Security Technique for Industrial Internet of Things Based Decentralized Applications. Journal of Grid Computing, 18(4), 615–628. https://doi.org/10.1007/s10723-020-09527-x

Haseli, G., Ranjbarzadeh, R., Hajiaghaei-Keshteli, M., Ghoushchi, S. J., Hasani, A., Deveci, M., & Ding, W. (2023). HECON: Weight assessment of the product loyalty criteria considering the customer decision’s halo effect using the convolutional neural networks. Information Sciences, 623, 184–205. https://doi.org/10.1016/j.ins.2022.12.027

Trappey, A. J. C., Trappey, C. V., Hareesh Govindarajan, U., Chuang, A. C., & Sun, J. J. (2017). A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0. Advanced Engineering Informatics, 33, 208–229. https://doi.org/10.1016/j.aei.2016.11.007

Haseli, G., Nazarian-Jashnabadi, J., Shirazi, B., Hajiaghaei-Keshteli, M., & Moslem, S. (2024). Sustainable strategies based on the social responsibility of the beverage industry companies for the circular supply chain. Engineering Applications of Artificial Intelligence, 133, 108253. https://doi.org/10.1016/j.engappai.2024.108253

Hasan, M. H., Khairuddin, A. A., & Akhir, E. A. P. (2019). A Case Study to Explore IoT Readiness in Outbound Logistics. International Journal of Supply Chain Management, 8(2), Article 2. https://doi.org/10.59160/ijscm.v8i2.3013

Nazarian-Jashnabadi, J., Bonab, S. R., Haseli, G., Tomaskova, H., & Hajiaghaei-Keshteli, M. (2023). A dynamic expert system to increase patient satisfaction with an integrated approach of system dynamics, ISM, and ANP methods. Expert Systems with Applications, 234, 121010. https://doi.org/10.1016/j.eswa.2023.121010

Sepahi, T., Shahbazi, M., & Shafiei Roudposhti, M. (2020). Drug distribution system in Iran: A multi method study of defects and solutions. Depiction of Health, 11(4), 324–343. https://doi.org/10.34172/doh.2020.41

Pappas, N., Caputo, A., Pellegrini, M. M., Marzi, G., & Michopoulou, E. (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

Dokhanian, S., Sodagartojgi, A., Tehranian, K., Ahmadirad, Z., Moghaddam, P. K., & Mohsenibeigzadeh, M. (2024). Exploring the impact of supply chain integration and agility on commodity supply chain performance. World Journal of Advanced Research and Reviews, 22(1), 441-450. https://doi.org/10.30574/wjarr.2024.22.1.1119

Mohammadabadi, S. M. S., Entezami, M., Moghaddam, A. K., Orangian, M., & Nejadshamsi, S. (2024). Generative Artificial Intelligence for Distributed Learning to Enhance Smart Grid Communication. International Journal of Intelligent Networks. https://doi.org/10.1016/j.ijin.2024.05.007

Ahmadirad, Z. (2024). Evaluating the influence of AI on market values in finance: distinguishing between authentic growth and speculative hype. International Journal of Advanced Research in Humanities and Law, 1(2), 50-57. https://doi.org/10.63053/ijrel.11

Ahmadirad, Z. (2024). The Beneficial Role of Silicon Valley's Technological Innovations and Venture Capital in Strengthening Global Financial Markets. International journal of Modern Achievement in Science, Engineering and Technology, 1(3), 9-17. https://doi.org/10.63053/ijset.40

Razavi, H., & Habibnia, A. (2024). The Rise of AI in Middle Eastern Fintech With the Case Studies From the UAE and Turkey. In Exploring Global FinTech Advancement and Applications (pp. 259-297). IGI Global. https://doi.org/10.4018/979-8-3693-1561-3.ch010

Razavi, H., Sarabadani, H., Karimisefat, A., & LEBRATY, J. F. (2019, February). Profitability prediction for ATM transactions using artificial neural networks: A data-driven analysis. In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) (pp. 661-665). IEEE. https://doi.org/10.1109/KBEI.2019.8735037

Sheykhfard, A., Haghighi, F., Saeidi, S., SafariTaherkhani, M., Fountas, G., & Das, S. (2024). Behavioral Modeling of Drivers near Speed Control Cameras: A Dual Perspective from Micro and Macro Data. Transportation Research Record, 03611981241287787. https://doi.org/10.1177/03611981241287787

Sadeghi, S., & Niu, C. (2024). Augmenting Human Decision-Making in K-12 Education: The Role of Artificial Intelligence in Assisting the Recruitment and Retention of Teachers of Color for Enhanced Diversity and Inclusivity. Leadership and Policy in Schools, 1-21. https://doi.org/10.1080/15700763.2024.2358303

Patil, S. K., & Kant, R. (2014). A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Systems with Applications, 41(2), 679–693. https://doi.org/10.1016/j.eswa.2013.07.093

Nazarian-Jashnabadi, J., Haseli, G., & Tomaskova, H. (2024). Digital transformation for the sustainable development of business intelligence goals. In Decision Support Systems for Sustainable Computing (pp. 169–186). https://doi.org/10.1016/B978-0-443-23597-9.00008-1

Qi, Q., Xu, Z., & Rani, P. (2023). Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations. Technological Forecasting and Social Change, 190, 122401. https://doi.org/10.1016/j.techfore.2023.122401

Li, Y., Su, D. A., & Mardani, A. (2023). Digital twins and blockchain technology in the industrial Internet of Things (IIoT) using an extended decision support system model: Industry 4.0 barriers perspective. Technological Forecasting and Social Change, 195, 122794. https://doi.org/10.1016/j.techfore.2023.122794

Hosseini Dehshiri, S. J., & Amiri, M. (2023). Evaluating the risks of the internet of things in renewable energy systems using a hybrid fuzzy decision approach. Energy, 285, 129493. https://doi.org/10.1016/j.energy.2023.129493

Heidary Dahooie, J., Mohammadian, A., Qorbani, A. R., & Daim, T. (2023). A portfolio selection of internet of things (IoTs) applications for the sustainable urban transportation: A novel hybrid multi criteria decision making approach. Technology in Society, 75, 102366. https://doi.org/10.1016/j.techsoc.2023.102366

Ali, S. M., Ashraf, M. A., Taqi, H. Md. M., Ahmed, S., Rob, S. M. A., Kabir, G., & Paul, S. K. (2023). Drivers for Internet of Things (IoT) adoption in supply chains: Implications for sustainability in the post-pandemic era. Computers & Industrial Engineering, 183, 109515. https://doi.org/10.1016/j.cie.2023.109515

Seker, S. (2022). IoT based sustainable smart waste management system evaluation using MCDM model under interval-valued q-rung orthopair fuzzy environment. Technology in Society, 71, 102100. https://doi.org/10.1016/j.techsoc.2022.102100

Yu, Z., Khan, S. A. R., Mathew, M., Umar, M., Hassan, M., & Sajid, M. J. (2022). Identifying and analyzing the barriers of Internet-of-Things in sustainable supply chain through newly proposed spherical fuzzy geometric mean. Computers & Industrial Engineering, 169, 108227. https://doi.org/10.1016/j.cie.2022.108227

Asadi, S., Nilashi, M., Iranmanesh, M., Hyun, S. S., & Rezvani, A. (2022). Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach. Technovation, 118, 102426. https://doi.org/10.1016/j.technovation.2021.102426

Sumrit, D. (2024). Enhancing readiness degree for Industrial Internet of Things adoption in manufacturing enterprises: An integrated Pythagorean fuzzy approach. Heliyon, 10(20), e39007. https://doi.org/10.1016/j.heliyon.2024.e39007

Nezhad, M. Z., Nazarian-Jashnabadi, J., Rezazadeh, J., Mehraeen, M., & Bagheri, R. (2023). Assessing dimensions influencing IoT implementation readiness in industries: A fuzzy DEMA℡ and fuzzy AHP analysis. Journal of Soft Computing and Decision Analytics, 1(1), 102–123. https://doi.org/10.31181/jscda11202312

Parra-Sánchez, D. T., Talero-Sarmiento, L. H., & Guerrero, C. D. (2021). Assessment of ICT policies for digital transformation in Colombia: Technology readiness for IoT adoption in SMEs in the trading sector. Digital Policy, Regulation and Governance, 23(4), 412–431. https://doi.org/10.1108/DPRG-09-2020-0120

Nurika, O., & Jung, L. T. (2024). Assessing Malaysia’s Internet of Things (IoT) Readiness Based on CREATE-IoT Key Performance Indicators. Journal of Advanced Research in Applied Sciences and Engineering Technology, 40(1), Article 1. https://doi.org/10.37934/araset.40.1.4554

Yahaya, N., Zakaria, N. H., & Tahir, H. M. (2018, July 25). An Investigation on the Factors that Influence Readiness of Internet of Things Adoption in Education Sector. https://repo.uum.edu.my/id/eprint/25251

Negm, E. (2022). Intention to use Internet of Things (IoT) in higher education online learning – the effect of technology readiness. Higher Education, Skills and Work-Based Learning, 13(1), 53–65. https://doi.org/10.1108/HESWBL-05-2022-0121

Radenković, M., Bogdanović, Z., Despotović-Zrakić, M., Labus, A., & Lazarević, S. (2020). Assessing consumer readiness for participation in IoT-based demand response business models. Technological Forecasting and Social Change, 150, 119715. https://doi.org/10.1016/j.techfore.2019.119715

Martínez, I., Zalba, B., Trillo-Lado, R., Blanco, T., Cambra, D., & Casas, R. (2021). Internet of Things (IoT) as Sustainable Development Goals (SDG) Enabling Technology towards Smart Readiness Indicators (SRI) for University Buildings. Sustainability, 13(14), Article 14. https://doi.org/10.3390/su13147647

Cui, Y., Liu, W., Rani, P., & Alrasheedi, M. (2021). Internet of Things (IoT) adoption barriers for the circular economy using Pythagorean fuzzy SWARA-CoCoSo decision-making approach in the manufacturing sector. Technological Forecasting and Social Change, 171, 120951. https://doi.org/10.1016/j.techfore.2021.120951

Kumar, D., Singh, R. K., Mishra, R., & Daim, T. U. (2023). Roadmap for integrating blockchain with Internet of Things (IoT) for sustainable and secured operations in logistics and supply chains: Decision making framework with case illustration. Technological Forecasting and Social Change, 196, 122837. https://doi.org/10.1016/j.techfore.2023.122837

Nazarian-Jashnabadi, J., Ronaghi, M., alimohammadlu, M., & Ebrahimi, A. (2023). The framework of factors affecting the maturity of business intelligence. Business Intelligence Management Studies, 12(46), 1-39. https://doi.org/10.22054/ims.2023.74305.2346

Amiri Sardari, Z., Abdoli Mohamadabadi, T., Nazarian-Jashnabadi, J., Tesoriere, G., & Campisi, T. (2024). Smart Experience and Green Health Tourism: The Moderating Role of Content Marketing. Sustainability, 16(11), 4546. https://doi.org/10.3390/su16114546

Jashnabadi, J. N., Pooya, A., & Bagheri, R. (2023). Provide a model for budget policy in university-community communication programs with a system dynamics approach (case study: Ferdowsi University of Mashhad). J. Ind. Manag. Perspect, 13(1), 9–39. https://doi.org/10.48308/jimp.13.1.9

Rezazadeh, J., Bagheri, R., Karimi, S., Nazarian-Jashnabadi, J., & Nezhad, M. Z. (2023). Examining the impact of product innovation and pricing capability on the international performance of exporting companies with the mediating role of competitive advantage for analysis and decision making. Journal of Operations Intelligence, 1(1), 30–43. https://doi.org/10.31181/jopi1120232

Kazemi, A., Kazemi, Z., Heshmat, H., Nazarian-Jashnabadi, J., & Tomášková, H. (2024). Ranking factors affecting sustainable competitive advantage from the business intelligence perspective: Using content analysis and F-TOPSIS. Journal of Soft Computing and Decision Analytics, 2(1), 39–53. http://dx.doi.org/10.31181/jscda21202430

Haseli, G., Torkayesh, A. E., Hajiaghaei-Keshteli, M., & Venghaus, S. (2023). Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts. Applied Soft Computing, 137, 110120. https://doi.org/10.1016/j.asoc.2023.110120

Sushil. (2012). Interpreting the Interpretive Structural Model. Global Journal of Flexible Systems Management, 13(2), 87–106. https://doi.org/10.1007/s40171-012-0008-3

Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making (Vol. 186). Springer. https://doi.org/10.1007/978-3-642-48318-9

Hooshangi, N., Mahdizadeh Gharakhanlou, N., & Ghaffari Razin, S. R. (2023). Evaluation of potential sites in Iran to localize solar farms using a GIS-based Fermatean Fuzzy TOPSIS. Journal of Cleaner Production, 384, 135481. https://doi.org/10.1016/j.jclepro.2022.135481

Hajiaghaei-Keshteli, M., Cenk, Z., Erdebilli, B., Selim Özdemir, Y., & Gholian-Jouybari, F. (2023). Pythagorean Fuzzy TOPSIS Method for Green Supplier Selection in the Food Industry. Expert Systems with Applications, 224, 120036. https://doi.org/10.1016/j.eswa.2023.120036

Hasani, A., Haseli, G., & Deveci, M. (2024). Analyzing operational risks of digital supply chain transformation using hybrid ISM-MICMAC method. OPSEARCH. https://doi.org/10.1007/s12597-024-00792-y

Toker, K., & Görener, A. (2023). Evaluation of circular economy business models for SMEs using spherical fuzzy TOPSIS: An application from a developing countries’ perspective. Environment, Development and Sustainability, 25(2), 1700–1741. https://doi.org/10.1007/s10668-022-02119-7

Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303–318. https://doi.org/10.1016/j.cie.2018.01.015

Published

2025-01-01

How to Cite

Seifi, N., Keshavarz, M., Kalhor, H., Shahrakipour, S., & Adibifar, A. (2025). Ranking of Criteria Affecting the Implementation Readiness of Internet of Things in industries Using TISM and Fuzzy TOPSIS Analysis. Journal of Operations Intelligence, 3(1), 46-66. https://doi.org/10.31181/jopi31202533