Exploring AI’s Role in Business Analytics for Operational Efficiency: A Survey Across Manufacturing Sectors

Abstract

Artificial intelligence (AI) has become a critical transformer in modern manufacturing that provides unique opportunities to improve business analytics and operational efficiency. In this study, we examine patterns, benefits and challenges of Artificial Intelligence (AI) adoption in US manufacturing sectors based on a survey of 300 industry professionals. Specific AI applications such as process automation, supply chain optimization and predictive analytics are first studied, through analysis of the operational outcomes of efficiency improvement, cost reduction, revenue growth and workforce optimization. The results indicate that AI adoption is growing commonplace organizations are using these tools to cope with challenging operational hurdles and move ahead against competitors. Despite its disadvantages, logistic regression helps overcome these challenges, including in our analysis of data from a case study comparing hand and automatic coding. We saw strong correlations between applications like process automation and supply chain optimization and operational efficiency, what we found were very important applications we should be using to streamline our workflows and reduce inefficiencies. The implementation costs are high, there is a lack in skill personnel and lastly, the data quality is an issue, this still stands out as a barrier impeding the adoption of AI, majorly in SMEs. Robust insights into the relationships between the AI adoption factors and operational outcomes were obtained through statistical analysis involving chi-square tests, correlation analysis; logistic regression and ANOVA. The organizations that invested over 50% of their budgets in AI saw a much higher revenue growth than the organizations that invested minimally in the technology – pointing to the value of targeted AI investments for their bottom line. On the bright side, there are many benefits to this but there are still limitations like a lack of scalable data infrastructure and resistance to change. The findings from this research will contribute to the growing body of knowledge of the transformative capability of AI in manufacturing and offer actionable recommendations for overcoming adoption barriers and making optimal use of investments. US based manufacturers can leverage these AI capabilities to achieve sustainable growth and continue their competitive advantage in the rapidly changing industrial environment.

References

Abdulrahman, Y, Arnautović, E, Parezanović, V, & Svetinovic, D. (2023). AI and blockchain synergy in aerospace engineering: an impact survey on operational efficiency and technological challenges. IEEE Access.

Adesina, A. A, Iyelolu, T. V, & Paul, P. O. (2024). Optimizing business processes with advanced analytics: techniques for efficiency and productivity improvement. World Journal of Advanced Research and Reviews, 22(3), 1917-1926.

Ahmad, S. (2024). The Impact of Decision making by Charismatic leadership in conflicted and tangled circumstances: Impact of Decision making by Charismatic leadership in conflicted and tangled circumstances. KASBIT Business Journal, 17(1).

Ahmed, A., Rahman, S., Islam, M., Chowdhury, F., & Badhan, I. A. (2023). Challenges and Opportunities in Implementing Machine Learning For Healthcare Supply Chain Optimization: A Data-Driven Examination. International journal of business and management sciences, 3(07), 6-31.

Ahmed, T., Mosaddeque, A., Hossain, A., Twaha, U., Rowshon, M., & Babu, B. (2022). The Dynamics of AI and Automation in Financial Forecasting, Human Resources Planning, and Resources Optimization for Designing an Effective National Healthcare Policy. Journal of Business Insight and Innovation, 1(2), 78-88.

Ali, M, Khan, T. I, Khattak, M. N, & ŞENER, İ. (2024). Synergizing AI and business: Maximizing innovation, creativity, decision precision and operational efficiency in high-tech enterprises. Journal of Open Innovation: Technology, Market and Complexity, 10(3), 100352.

Ara, A, Maraj, M. A. A, Rahman, M. A, & Bari, M. H. (2024). The Impact of Machine Learning on Prescriptive Analytics for Optimized Business Decision-Making. International Journal of Management Information Systems and Data Science, 1(1), 7-18.

Asif, M. (2022). Integration of Information Technology in Financial Services and its Adoption by the Financial Sector in Pakistan. Inverge Journal of Social Sciences, 1(2), 23-35.

Asif, M., Khan, A., & Pasha, M. A. (2019). Psychological capital of employees’ engagement: moderating impact of conflict management in the financial sector of Pakistan. Global Social Sciences Review, IV, 160-172.

Badhan, I. A, Hasnain, M. N, & Rahman, M. H. (2022). Enhancing Operational Efficiency: A Comprehensive Analysis of Machine Learning Integration in Industrial Automation. Journal of Business Insight and Innovation, 1(2), 61-77.

Badhan, I. A., Hasnain, M. N., & Rahman, M. H. (2022). Enhancing Operational Efficiency: A Comprehensive Analysis of Machine Learning Integration in Industrial Automation. Journal of Business Insight and Innovation, 1(2), 61-77.

Badhan, I. A., Hasnain, M. N., & Rahman, M. H. (2023). Advancing Operational Efficiency: An In-Depth Study Of Machine Learning Applications In Industrial Automation. Policy Research Journal, 1(2), 21-41.

Balcıoğlu, Y. S, Çelik, A. A, & Altındağ, E. (2024). Artificial intelligence integration in Sustainable Business Practices: A text mining analysis of USA firms. Sustainability, 16(15), 6334.

Eboigbe, E. O, Farayola, O. A, Olatoye, F. O, Nnabugwu, O. C, & Daraojimba, C. (2023). Business intelligence transformation through AI and data analytics. Engineering Science & Technology Journal, 4(5), 285-307.

Ekellem, E. A. F. (2023). Operational Renaissance: Harnessing AI for Enhanced Business Efficacy. Authorea Preprints.

Helo, P, & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: An exploratory case study. Production Planning & Control, 33(16), 1573-1590.

Hossain, M. A, Agnihotri, R, Rushan, M. R. I, Rahman, M. S, & Sumi, S. F. (2022). Marketing analytics capability, artificial intelligence adoption and firms' competitive advantage: Evidence from the manufacturing industry. Industrial Marketing Management, 106, 240-255.

Kumar, S, & Aithal, P. S. (2023). Tech-business analytics in primary industry sector. International Journal of Case Studies in Business, IT and Education (IJCSBE), 7(2), 381-413.

Kumar, S, & Aithal, P. S. (2023). Tech-Business Analytics in Secondary Industry Sector. International Journal of Applied Engineering and Management Letters (IJAEML), 7(4), 1-94.

Latif, A., Bashir, R., Hasan, S. T., & Hussain, H. (2023). Comparative Analysis of Psycho-Social Stress Among Parents of Children with Mental and Physical Disabilities. Journal of Asian Development Studies, 12(3), 1570-1580.

Latif, A., Bhatti, R. S., & Butt, A. J. (2021). Association Between Immunization And Occurrence Of Disease: A Secondary Data Analysis. Bulletin of Business and Economics (BBE), 10(3), 253-258.

Latif, A., Hasan, S. T., Abdullah, M., & Ahmad, H. M. (2023). Exploring the Nexus: Educational, Health, and Economic Incentives in Power Looms and their Impacts on Job Satisfaction. Bulletin of Business and Economics (BBE), 12(3), 635-639.

LATIF, A., ZAKA, M. S., & RASOOL, M. U. (2023). Married Women's Career Trajectories and Their Impact on Life Satisfaction: A Literature Review. Jahan-e-Tahqeeq, 6(1), 09-33.

Mikalef, P, Krogstie, J, Pappas, I. O, & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169.

Mohapatra, H, & Mishra, S. R. (2024). Unlocking insights: exploring data analytics and AI tool performance across industries. In Data analytics and machine learning: navigating the big data landscape (pp. 265-288). Singapore: Springer Nature Singapore.

Mosaddeque, A., Rowshon, M., Ahmed, T., Twaha, U., & Babu, B. (2022). The Role of AI and Machine Learning in Fortifying Cybersecurity Systems in the US Healthcare Industry. Inverge Journal of Social Sciences, 1(2), 70-81.

Munawar, S., BUTT, A. J., & LATIF, A. (2023). Pros and Cons of 18th Amendment: A study of Center-Province relations 2010-2022. Jahan-e-Tahqeeq, 6(3), 292-298.

Nzeako, G, Akinsanya, M. O, Popoola, O. A, Chukwurah, E. G, & Okeke, C. D. (2024). The role of AI-Driven predictive analytics in optimizing IT industry supply chains. International Journal of Management & Entrepreneurship Research, 6(5), 1489-1497.

Oyekunle, D, & Boohene, D. (2024). Digital transformation potential: The role of artificial intelligence in business. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev, 9(3), 1.

Rana, N. P, Chatterjee, S, Dwivedi, Y. K, & Akter, S. (2022). Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness. European Journal of Information Systems, 31(3), 364-387.

Sayem, M. A., Taslima, N., Sidhu, G. S., Chowdhury, F., Sumi, S. M., Anwar, A. S., & Rowshon, M. (2023). AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices. Int. J. Recent Innov. Trends Comput. Commun, 11(10), 1109-1122.

Settibathini, V. S, Kothuru, S. K, Vadlamudi, A. K, Thammreddi, L, & Rangineni, S. (2023). Strategic analysis review of data analytics with the help of artificial intelligence. International Journal of Advances in Engineering Research, 26, 1-10.

Talpur, F, Tabbassum, K, Irfan, M, Luhana, K. K, Koondhar, M. Y, Memon, A. B, ... & Ali, A. (2024). Exploring Nexus among Big Data Analytics, Artificial Intelligence and Operational Performance. Kurdish Studies, 12(2), 6066-6078.

Taslima, N., Islam, M., Rahman, S., Islam, S., & Islam, M. M. (2022). Information system integrated border security program: A quantitative assessment of AI-driven surveillance solutions in US immigration control. Journal of Business Insight and Innovation, 1(2), 47-60.

Udeh, C. A orieno, O. H, Daraojimba, O. D, Ndubuisi, N. L, & Oriekhoe, O. I. (2024). Big data analytics: a review of its transformative role in modern business intelligence. Computer Science & IT Research Journal, 5(1), 219-236.

Wamba-Taguimdje, S. L, Wamba, S. F, Kamdjoug, J. R. K, & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business process management journal, 26(7), 1893-1924.

Wang, Y, Skeete, J. P, & Owusu, G. (2022). Understanding the implications of artificial intelligence on field service operations: A case study of BT. Production Planning & Control, 33(16), 1591-1607.

Waqar, M, Bhatti, I, & Khan, A. H. (2024). AI-powered automation: Revolutionizing industrial processes and enhancing operational efficiency. Revista de Inteligencia Artificial en Medicina, 15(1), 1151-1175.

Yu, Y, Xu, J, Zhang, J. Z, Liu, Y. D, Kamal, M. M, & Cao, Y. (2024). Unleashing the power of AI in manufacturing: Enhancing resilience and performance through cognitive insights, process automation and cognitive engagement. International Journal of Production Economics, 270, 109175.

Zong, Z, & Guan, Y. (2024). AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation and Efficiency. Journal of the Knowledge Economy, 1-40.