Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Development of a Machine Learning-Based Decision Support System for Smart Technology Selection in Small and Medium-Sized Enterprises Considering Implementation Risks

Document Type : Research Paper

Authors
1 Industrial Engineering Group, Alborz Campus, University of Tehran, Tehran, Iran
2 Faculty Member, Faculty of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
Abstract
Small and Medium-sized Enterprises (SMEs) are greatly hindered in selecting the most appropriate smart technologies as they strive to enhance productivity and competitiveness. This research suggests a machine learning-supported decision support system for smart technology choice in SMEs that systematically addresses implementation risks. The methodology adopts data mining approaches with reinforcement learning algorithms, identifying 24 applicable criteria in four categories technical, organizational, environmental, and risk using an expert Delphi panel. A prediction model was then developed using random forest algorithms and convolutional neural networks to analyze the prospects of successful implementation of smart technology. The model was validated with utmost rigor with data from 85 SMEs from various industries and was discovered to be 87.3% accurate. Results indicate that organizational culture, digital readiness, underlying implementation costs, and cybersecurity threats constitute the most important determinants shaping the successful implementation of smart technology in SMEs. The proposed decision support system possesses a dynamic interface through which SME managers can explore various scenarios and select the most suitable technology for their specific context. Through offering a new solution for managing uncertainty in decision-making, this research immensely adds to intelligent technology selection exercises for SMEs.
Keywords
Subjects

Aghaei, R., & Taheri, P. (2024). AI-powered blockchain technology in industry 4.0, a review. Digital Manufacturing, 1(1), 100015.
Aghajari, S., Akhtari, T., Shojaee, S., & Javanshir, F. (2024). Intelligent Decision Support Systems An Analysis of Machine Learning and Multicriteria Decision-Making Methods. Applied Sciences, 13(22), 12426.
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2024). Ethical artificial intelligence in business: A new framework for responsible AI adoption in SMEs. Business Horizons, 67(3), 317-329.
Al-Baldawi, Z., Kassam-Sawsan, A. H., & Al-Zubaidi, S. A. (2024). Investigation and Assessment the Level of SMES Leanness Using Fuzzy Dematel/Fuzzy AHP/Fuzzy Topsis Integrated Model. Management Systems in Production Engineering, 32(4), 537-547.
Ali, H., & Reuben, J. (2025). Cloud-Powered Innovation: Enhancing SME Competitiveness in the Digital Era. Journal of Digital Business, 4(2), 117-132.
Büyüközkan, G., & Güler, M. (2018). A hesitant fuzzy based TOPSIS approach for smart glass evaluation. In Advances in Fuzzy Logic and Technology 2017: Proceedings of: EUSFLAT-2017–The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11–15, 2017, Warsaw, Poland IWIFSGN’2017–The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13–15, 2017, Warsaw, Poland, Volume 1 10 (pp. 330-341). Springer International Publishing.‏
de Oliveira, A. C., & Ávila, P. (2024). A novel value-based multi-criteria decision making approach to evaluate new technology adoption in SMEs. PLOS ONE, 19(2), e0293842.
Donthu, N., Kumar, S., Mukherjee, D., & Pandey, N. (2023). How artificial intelligence capabilities impact firm performance? The mediating role of marketing agility. Journal of Business Research, 157, 113559.
Duman, G. M., Taskaynatan, M., Kongar, E., & Rosentrater, K. A. (2023). Combining interval type-2 fuzzy DEMATEL and interval type-2 fuzzy TODIM methods to evaluate sustainable manufacturing systems. Journal of Cleaner Production, 385, 135678.
Fang, C., Ullah, N., Batumalay, M., Al-Rahmi, W. M., & Alblehai, F. (2025). Blockchain technology and its impact on sustainable supply chain management in SMEs. PeerJ Computer Science, 11, e2466.
Ghobakhloo, M., Iranmanesh, M., Vilkas, M., Grybauskas, A., & Amran, A. (2023). Drivers and barriers of blockchain technology adoption in small and medium-sized enterprises. Technological Forecasting and Social Change, 189, 122339.
Gölgeci, I., Arslan, A., Khan, Z., & Kontkanen, M. (2023). Digital capabilities and SME performance: The moderating role of government support during COVID-19. Technological Forecasting and Social Change, 193, 122647.
Greco, F., Ishizaka, A., Tasiou, M., & Torrisi, G. (2023). Sigma-Mu efficiency: theory and a robust classifier tool for decision-making units. European Journal of Operational Research, 305(1), 307-316.
Guimarães, J. C. F., Severo, E. A., & Dorion, E. C. H. (2024). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 60, 507-520.
Gupta, S., Mehlawat, M. K., Maheshwari, P., & Kacprzyk, J. (2024). Generalized expected utility theory-based hesitant fuzzy VIKOR method: Application to ranking SMEs on digital maturity parameters. Information Sciences, 646, 119328.
Haseeb, M., Tasnia, M., Khan, N. H., & Hassan, S. (2023). Digital entrepreneurship and sustainability of small and medium enterprises during pandemic disruption. Journal of Innovation & Knowledge, 8(3), 100381.
Jaeger, P. T., & Burnett, G. (2024). Benefits and Risks of Machine Learning Decision Support Systems. Journal of Information Technology, 39(2), 218-231.
Kemp, R., & Ross, N. (2023). Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges. Management Review Quarterly, 74(1), 1-29.
Kumar, L., & Sharma, R. K. (2025). Adapting to Industry 4.0: evaluating SMEs preparedness through a comprehensive digital readiness assessment maturity model by validating stakeholders’ perceptions. Business Process Management Journal.
Lopez, C., Gavidia, J. V., & Lejia, L. (2024). Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. Computers in Industry, 156, 104029.
Mendel, J. M. (2024). Working with Type-2 Fuzzy Sets. In Explainable Uncertain Rule-Based Fuzzy Systems (pp. 281-339). Cham: Springer International Publishing.
Movahed, A. B., Aliahmadi, A., Parsanejad, M., & Nozari, H. (2023). A systematic review of collaboration in supply chain 4.0 with meta-synthesis method. Supply Chain Analytics, 4, 100052.
Movahed, A. B., Movahed, A. B., & Nozari, H. (2024). Opportunities and challenges of marketing 5.0. Smart and sustainable interactive marketing, 1-21.
Neirotti, P., Raguseo, E., & Gastaldi, L. (2024). How small businesses move toward digital transformation: A configurational approach to identify digital technology adoption patterns. Journal of Small Business Management, 62(5), 1297-1337.
Nozari, H., Abdi, H., & Szmelter-Jarosz, A. (2025). Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization. arXiv preprint arXiv:2503.02331.
Poli, G., & Attardi, R. (2023). Decision Support System for technology selection based on multi-criteria ranking: Application to NZEB refurbishment. Building and Environment, 214, 109102.
Rahman, M. S., Peeri, N. C., Shrestha, N., Zaki, R., Haque, U., & Hameed, S. A. (2023). Artificial intelligence and internet of things enabled technologies for COVID-19 pandemic: A systematic review. Health Policy and Technology, 12(2), 100740.
Raihan, A. (2024). A review of the digitalization of the small and medium enterprises (SMEs) toward sustainability. Global Sustainability Research, 3(2), 1-16.
Ramasamy, S., Sabharwal, M., & Williams, C. R. (2024). Challenges and opportunities in AI and digital transformation for SMEs: A cross-continental perspective. Journal of Technology Management and Innovation, 19(2), 53-67.
Saha, B., & Anwar, Z. (2023). A review of cybersecurity challenges in small business: The imperative for a future governance framework. Journal of Information Security, 15(1), 24-39.
Shahzad, F., Rehman, I. U., Nawaz, F., & Nawaz, N. (2023). Does entrepreneurial self-efficacy influence the adoption of artificial intelligence and robotics? An empirical study of SMEs. Journal of Innovation & Knowledge, 8(2), 100328.
Tavana, M., Abtahi, A., Di Caprio, D., & Poortarigh, M. (2024). An integrated decision support system for technology selection using fuzzy inference and hierarchical distance-based decision-making. Expert Systems with Applications, 238, 121629.
Tian, H., Dogra, A. V., Asmussen, N., & Hu, J. (2024). A risk assessment framework for technology implementation in manufacturing SMEs. International Journal of Production Research, 62(4), 1372-1392.
Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The processes of technological innovation. 
Vasey, B., Ursprung, S., Beddoe, B., Taylor, J., Marlow, N., Parston, G., & Darzi, A. (2023). Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. NPJ Digital Medicine, 6(1), 146.
Verma, S., Kumar, V., & Tomar, M. (2023). The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises. Scientific Reports, 13, 1837.
VK, R. K., Saunila, M., Rantala, T., & Ukko, J. (2025). The interplay between smart technologies, business sustainability, and environmental sustainability: An empirical analysis of SMEs. Corporate Social Responsibility and Environmental Management, 32(1), 835-848.
Wang, S., & Zhang, H. (2025). Digital Transformation and Innovation Performance in Small-and Medium-Sized Enterprises: A Systems Perspective on the Interplay of Digital Adoption, Digital Drive, and Digital Culture. Systems, 13(1), 43.
Volume 17, Issue 2 - Serial Number 2
Spring 2025
Pages 103-131

  • Receive Date 14 January 2024
  • Revise Date 08 March 2024
  • Accept Date 02 May 2024