Leveraging Artificial Intelligence for Efficient Quotation Management: A Performance Analysis of Custom GPT vs. Traditional Methods

Authors

  • Manisra Baramichai Logistics Engineering, University of the Thai Chamber of Commerce, Bangkok, Thailand
  • Pavanphat Prateepkaew Logistics Engineering, University of the Thai Chamber of Commerce, Bangkok, Thailand
  • Apiyada Kanjanaprasert Logistics Engineering, University of the Thai Chamber of Commerce, Bangkok, Thailand

Keywords:

Artificial Intelligence, Custom GPT, Document Automation, AI-Driven Processing

Abstract

This study explores the application of artificial intelligence (AI), specifically Custom GPT, in automating quotation document management. The research focuses on extracting product details, product codes, and other relevant information from PDF documents and converting them into structured Excel formats. A total of 18 quotation samples from two major customers were analyzed. The primary objectives are to compare the performance of AI-driven processing with manual data entry and to develop methods for reducing redundant tasks in document handling. Experimental results demonstrate that Custom GPT significantly reduces processing time. Performance analysis indicates that ChatGPT improves efficiency by 70–90% in automating product data entry compared to manual input by employees. The findings suggest that AI technology enhances operational efficiency, minimizes errors, and allows employees to focus on higher-value tasks such as market analysis and customer relationship management. This research also provides guidelines for integrating AI into document management processes, offering a foundation for future AI-driven business applications.

References

Crevier, D. (1993). AI: The tumultuous history of the search for artificial intelligence. Basic Books.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT Press.

Hgs. (2023). From GPT-1 to GPT-4: A look at the evolution of generative AI. Hgs Blog. [Onlline]. Retrieved 20 September 2023 from: https://hgs.cx/blog/from-gpt-1-to-gpt-4

Kline, R. R. (2011). Cybernetics, automata studies, and the Dartmouth conference on artificial intelligence. IEEE Annals of the History of Computing, 33(4), 53–59.

McCarthy, J., Minsky, M. L., Rochester, N., and Shannon, C. E. (2006). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. AI Magazine, 27(4), 12-14.

https://doi.org/10.1609/aimag.v27i4.1904

McCorduck, P. (2004). Machines who think. 2nd ed. A K Peters.

Roumeliotis, K. I. (2023). ChatGPT and Open-AI models. Future Internet, 15(6), 192. https://doi.org/10.3390/fi15060192

Russell, S. J., and Norvig, P. (2010). Artificial intelligence: A modern approach. 3rd ed. Prentice Hall.

Turing, A. M. (1950). Computing machinery and intelligence. Mind: A Quarterly Review, 59(236), 433–460.

https://doi.org/10.1093/mind/LIX.236.433

Wakefield, K. (2024, September 20). A guide to the types of machine learning algorithms and their applications. SAS. [Online]. Retrieved from: https://www.sas.com/en_gb/insights/articles/ analytics/machine-learning-algorithms.html

Yellow. (2024). ChatGPT for document processing. Yellow Systems Blog. [Online]. Retrieved from: https://yellow.systems/blog/chatgpt-for-document-processing

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Published

2025-06-24

How to Cite

Baramichai, M., Prateepkaew, P., & Kanjanaprasert, A. . (2025). Leveraging Artificial Intelligence for Efficient Quotation Management: A Performance Analysis of Custom GPT vs. Traditional Methods. Journal of Innovation and Management, 10(1), 147–157. retrieved from https://so03.tci-thaijo.org/index.php/journalcim/article/view/288450

Issue

Vol. 10 No. 1 (2025): January - June 2025

Section

Research Articles

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