Assessment of the Effects of IT Infrastructure Automation and Technical Web Optimization on Operational Performance and User Experience in Mining and Steel Supply Chain Industries: A Case Study of Bahar Trade Company

Authors

    Amin Jamali * Department of Computer Engineering, Isf.C., Islamic Azad University, Isfahan, Iran aminjamali79@aminjamali.site
    Mohammad Reza Badami Department of Industrial Engineering, Na.C., Islamic Azad University, Najafabad, Iran
https://doi.org/10.61838/msesj.477

Keywords:

IT infrastructure automation; database optimization; Next.js; technical SEO; operational performance; user experience; B2B conversion; mining and steel supply chain

Abstract

This study aimed to assess the effects of IT infrastructure automation, database optimization, Next.js-based web re-architecture, and technical search-engine optimization on operational performance and user experience at Bahar Trade Company. A quantitative, applied, developmental case-study design with a pre-intervention and post-intervention comparison was employed. The study included two consecutive 90-day periods, comprising 90 daily observations before implementation and 90 matched observations after implementation. The intervention consisted of automated Bash-based server monitoring and database backup procedures, PostgreSQL query optimization and indexing, deployment of a headless Payload CMS architecture, development of a Next.js server-side-rendered frontend, and implementation of technical SEO measures. Daily data were collected on SQL query response time, system uptime, Core Web Vitals, organic search traffic, and B2B conversion rate. Data were analyzed in SPSS using descriptive statistics, the Kolmogorov–Smirnov test, and paired-samples (t)-tests at a significance level of (\alpha=0.05). The paired-samples (t)-test showed that heavy SQL query response time decreased significantly after the intervention, (t(89)=24.81), (p<0.001), supporting the first hypothesis. Largest Contentful Paint also improved significantly following the implementation of the Next.js architecture and technical optimization measures, (t(89)=18.45), (p<0.001). In addition, the B2B conversion rate increased significantly in the post-intervention period, (t(89)=-12.32), (p<0.001). The negative sign reflected the order of subtraction between the paired conditions rather than a decline in performance. These inferential results supported the second hypothesis and confirmed statistically significant improvements in database responsiveness, web-loading performance, and commercial conversion. The integrated implementation of infrastructure automation, database engineering, server-side-rendered web architecture, and technical SEO significantly enhanced both internal operational efficiency and external digital performance, indicating that coordinated technical transformation can strengthen reliability, user experience, and commercial effectiveness in mining and steel supply chain companies.

References

[1] A. Bharadwaj, O. A. El Sawy, P. A. Pavlou, and N. Venkatraman, "Digital business strategy: Toward a next generation of insights," MIS Quarterly, vol. 37, no. 2, pp. 471-482, 2013, doi: 10.25300/MISQ/2013/37.2.03.

[2] T. H. Davenport and A. Spanyi, "Digital transformation is not just about technology," Harvard Business Review, vol. 97, no. 2, pp. 2-6, 2019. [Online]. Available: https://hbr.org/2019/03/digital-transformation-is-not-just-about-technology.

[3] M. Kunytska, I. Piskun, V. Kotenko, and A. Kryvoruchko, "Digital modelling technologies in the mining industry: Effectiveness and prospects of digitalisation of open-pit mining enterprises," Bulletin of Cherkasy State Technological University, vol. 29, no. 1, pp. 52-61, 2024, doi: 10.62660/bcstu/1.2024.52.

[4] R. Riabtsev, "Mechanisms of digital transformation of the management system in the mining and metallurgical complex: From automation to intelligent models," Social Development: Economic and Legal Issues, no. 15, p. 11, 2026, doi: 10.70651/3083-6018/2026.3.11.

[5] I. Lee, "Big Data Analytics in Supply Chain Management," Data, vol. 7, no. 1, p. 17, 2022, doi: 10.3390/data6010017.

[6] A. M. Khedr, "Enhancing supply chain management with deep learning and machine learning techniques: A review," Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 4, p. 100379, 2024, doi: 10.1016/j.joitmc.2024.100379.

[7] K. Douaioui, "Machine Learning and Deep Learning Models for Demand Forecasting in Supply Chain Management: A Critical Review," Applied System Innovation, vol. 7, no. 5, p. 93, 2024, doi: 10.3390/asi7050093.

[8] M. Heidari and H. Amiri, "Inspecting the Predictive Power of Artificial Intelligence Models in Predicting the Stock Price Trend in Tehran Stock Exchange," Financial Research Journal, vol. 24, no. 4, pp. 602-623, 2022, doi: 10.22059/frj.2022.320064.1007149.

[9] N. Gholami and N. Shams Gharne, "Presenting an Optimized CNN-LSTM Model for Stock Price Forecasting in the Tehran Stock Exchange," Financial Management Perspective, vol. 14, no. 45, pp. 123-147, 2024, doi: 10.48308/jfmp.2024.104892.

[10] P. Peykani, M. Sargolzaei, N. Sanadgol, A. Takaloo, and H. Kamyabfar, "The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market," Journal of Financial Studies, vol. 11, no. 4, pp. 45-68, 2023.

[11] M. H. Poostforoush, A. Monajemi, S. Daei-Karimzadeh, and S. Samadi, "Automation of Algorithmic Trading Strategies in Artificial Financial Markets by Combining Machine Learning Techniques and Agent-based Modeling," Iranian Journal of Finance, vol. 9, no. 3, pp. 95-134, 2025, doi: 10.30699/ijf.2025.458379.1472.

[12] R. Mohammadi and K. Ebrahimi, "Automation of database support and monitoring processes in industrial organizations using DevOps approach," Journal of Information Technology Management, vol. 13, no. 4, pp. 112-135, 2021, doi: 10.22059/jitm.2021.321458.

[13] T. A. Limoncelli, C. Hogan, and C. Chalup, The Practice of System and Network Administration, 3rd ed. Addison-Wesley Professional, 2014.

[14] R. Jabbari, N. B. Ali, K. Petersen, and B. Tanikella, "What is DevOps? A systematic mapping study," presented at the Proceedings of the 10th International Symposium on Empirical Software Engineering and Measurement (ESEM), 2016. [Online]. Available: https://doi.org/10.1145/2962695.2962707.

[15] G. Kim, J. Humble, P. Debois, and J. Willis, The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations. IT Revolution Press, 2016.

[16] D. Chaffey and F. Ellis-Chadwick, Digital Marketing: Strategy, Implementation and Practice, 7th ed. Pearson UK, 2019.

[17] E. Schurman, P. Walton, and B. Walton, "The impact of web performance on user engagement and business metrics: An empirical analysis," Journal of Web Engineering, vol. 20, no. 5, pp. 1421-1448, 2021, doi: 10.13052/jwe1540-9589.2053.

[18] M. Biilmann and C. Bach, Modern Web Development on the Jamstack: Tools and Best Practices for Fast, Robust, and Secure Websites. O'Reilly Media, 2020.

[19] R. Ollila, N. Mäkitalo, and T. Mikkonen, "Modern web frameworks: A comparison of rendering performance," Journal of Web Engineering, vol. 21, no. 3, pp. 789-813, 2022, doi: 10.13052/jwe1540-9589.21311.

[20] P. Gieda and M. Miłosz, "Comparative analysis of Next.js and Astro frameworks," Journal of Computer Sciences, vol. 38, pp. 1-5, 2026, doi: 10.7726/jcs.2026.38.1.

[21] R. Berman and Z. Katona, "The role of search engine optimization in search marketing," Marketing Science, vol. 32, no. 4, pp. 644-651, 2013, doi: 10.1287/mksc.2013.0783.

[22] K. Kowalczyk and T. Szandala, "Enhancing SEO in single-page web applications in contrast with multi-page applications," IEEE Access, vol. 12, pp. 11597-11614, 2024, doi: 10.1109/ACCESS.2024.3355740.

[23] S. M. Hoseini and M. Zarei, "Evaluating the effects of modern front-end architectures and technical SEO on conversion rates in B2B platforms," Journal of Digital Resource Management, vol. 10, no. 3, pp. 45-62, 2022, doi: 10.22054/rmd.2022.65412.

[24] H. M. Alzoubi, "Optimizing supply chain excellence: Unravelling the synergies between IT proficiencies, smart supply chain practices, and organizational culture," Uncertain Supply Chain Management, vol. 12, no. 3, pp. 1855-1866, 2024, doi: 10.5267/j.uscm.2024.2.017.

[25] Y. Cui, "Supply Chain Transparency and Blockchain Design," Management Science, vol. 70, no. 8, pp. 5120-5138, 2024, doi: 10.1287/mnsc.2023.4851.

[26] T. A. Mukha, "Process mining-driven digital transformation of enterprise logistics for circular and sustainable supply-chain performance," Intellectualization of Logistics and Supply Chain Management: Electronic Scientific and Practical Journal, no. 32, pp. 88-99, 2025, doi: 10.46783/smart-scm/2025-32-6.

[27] R. K. Yin, Case Study Research and Applications: Design and Methods, 6th ed. SAGE Publications, 2018.

[28] M. Karami and H. Bouraghi, "Analysis of the Impact of Digital Technologies on Supply Chain Optimization in Various Industries," Management Strategies and Engineering Sciences, vol. 5, no. 4, pp. 160-166, 2023. [Online]. Available: https://msesj.com/index.php/mses/article/view/60.

[29] X. Xu, X. Chen, J. Hou, T. C. E. Cheng, Y. Yu, and L. Zhou, "Should live streaming be adopted for agricultural supply chain considering platform's quality improvement and blockchain support?," Transportation Research Part E: Logistics and Transportation Review, vol. 195, p. 103950, 2025, doi: 10.1016/j.tre.2024.103950.

Downloads

Published

2027-11-01

Submitted

2026-02-10

Revised

2026-07-22

Accepted

2026-07-29

Issue

Section

Articles

How to Cite

Jamali, A., & Badami, M. R. . (2027). Assessment of the Effects of IT Infrastructure Automation and Technical Web Optimization on Operational Performance and User Experience in Mining and Steel Supply Chain Industries: A Case Study of Bahar Trade Company. Management Strategies and Engineering Sciences, 1-17. https://doi.org/10.61838/msesj.477

Similar Articles

1-10 of 222

You may also start an advanced similarity search for this article.