تدوین مدل کیفی یادگیری ماشین و هوش مصنوعی در هزینه یابی پروژه مبتنی بر فعالیت
کلمات کلیدی:
یادگیری ماشین, هوش مصنوعی, هزینهیابی مبتنی بر فعالیت, مدیریت هزینه پروژهچکیده
در دنیای رقابتی امروز، مدیریت دقیق هزینهها در پروژهها حیاتی است و سیستم هزینهیابی مبتنی بر فعالیت با هدف تخصیص دقیق هزینهها به فعالیتها طراحی شده، اما پیچیدگی اجرایی و نیاز به دادههای فراوان بهرهبرداری از آن را دشوار کرده است. در عصر تحول دیجیتال، بهرهگیری از فناوریهای نوین همچون یادگیری ماشین و هوش مصنوعی در حوزههای مالی و حسابداری به ضرورتی انکارناپذیر بدل شده است. این پژوهش با رویکرد کیفی و روش اسنادی در طی سال 1404 با کمک نرم افزار MAXQDA به تدوین مدلی مفهومی برای تلفیق قابلیتهای یادگیری ماشین و هوش مصنوعی در چارچوب هزینهیابی پروژه مبتنی بر فعالیت پرداخته است. ابتدا با استخراج ۲۰ مؤلفه اصلی یادگیری ماشین، ۱۸ مؤلفه کلیدی هوش مصنوعی و ۱۵ مؤلفه محوری در هزینهیابی مبتنی بر فعالیت، یک طبقهبندی مفهومی و ترکیبی ارائه گردید. سپس مؤلفهها در قالب محورهای مفهومی تجمیع و تحلیل شدند تا روابط سیستمی و کاربردی آنها در مدیریت هزینه پروژهها مشخص شود. یافتههای تحقیق نشان میدهد که مدلهای یادگیری ماشین قابلیت پیشبینی دقیق هزینهها، شناسایی محرکهای هزینه و تحلیل رفتار هزینهای را فراهم میکنند؛ در حالیکه هوش مصنوعی از طریق سیستمهای خبره، منطق فازی و عاملهای هوشمند میتواند تصمیمگیری در شرایط عدم قطعیت را تسهیل نماید. در نهایت، ترکیب این فناوریها در ساختار هزینهیابی مبتنی بر فعالیت منجر به افزایش دقت تخصیص هزینه، شفافیت اطلاعات مالی و بهینهسازی استفاده از منابع در پروژهها میشود. بر اساس نتایج تحقیق، پیشنهادهای مدیریتی جهت پیادهسازی عملی این مدل ارائه شده است.
دانلودها
مراجع
Alam, A. (2023). Reflections on the process of adapting hospital performance measures for an LMIC tertiary care setup. BMJ Leader. https://doi.org/10.1136/leader-2021-000568
Alkhajah, M. (2023). Financial Accounting in the Digital Era: Literature. https://easychair-www.easychair.org/publications/preprint_download/rTTC
Babbie, E., Wagner Iii, W. E., & Zaino, J. (2022). Adventures in social research: Data analysis using IBM SPSS statistics. Sage Publications. https://books.google.com/books?hl=en&lr=&id=amdjEAAAQBAJ&oi=fnd&pg=PT22&dq=spss+statistics&ots=jUUXUcqeQq&sig=wl8y3twWEJO7tssXArPmrEyL_BY
Balicka, H. (2023). Digital technologies in the accounting information system supporting decision-making processes. Scientific Papers of Silesian University of Technology-Organization and Management Series(169). https://doi.org/10.29119/1641-3466.2023.169.4
Borges, P., Alves, M. d. C., & Silva, R. (2024). The Activity-Based Costing System Applied in Higher Education Institutions: A Systematic Review and Mapping of the Literature. Businesses, 4, 18-38. https://doi.org/10.3390/businesses4010002
Bose, S., Dey, S. K., & Bhattacharjee, S. (2023). Big data, data analytics and artificial intelligence in accounting: An overview. In Handbook of Big Data Research Methods (pp. 32). https://doi.org/10.4337/9781800888555.00007
George, D., & Mallery, P. (2021). IBM SPSS statistics 27 step by step: A simple guide and reference. Routledge. https://doi.org/10.4324/9781003205333-1
Hammann, D. (2024). Big data and machine learning in cost estimation: An automotive case study. International Journal of Production Economics, 269, 109137. https://doi.org/10.1016/j.ijpe.2023.109137
Judijanto, L. (2024). Integration of Artificial Intelligence in Activity-Based Project Costing: Enhancing Accuracy and Efficiency in Project Cost Management. International Journal of Communication Networks and Information Security (Ijcnis), 16(4), 66-79. https://ijcnis.org/index.php/ijcnis/article/view/6860
Khan, M. S. U. (2024). Exploring Theoretical Foundations of Activity-Based Costing. International Journal of Research and Innovation in Social Science, 8(3s), 2953-2965. https://doi.org/10.47772/IJRISS.2024.803212S
Khodami Pour, A., & Javanmard, H. (2023). A Review of the Various Dimensions of Implementing Activity-Based Costing in Developed and Developing Countries. First National Conference on Emerging Research in Accounting, Finance, Management, and Economics with an Innovation Ecosystem Development Approach, Tehran. https://civilica.com/doc/1922708
Knox, B. D. (2023). Machine Learning Activity-Based Costing: Can Activity-Based Costing's First-Stage Allocation Be Replaced with a Neural Network? Journal of Emerging Technologies in Accounting, 20(2), 95-117. https://doi.org/10.2308/JETA-2021-046
Kushnir, N., Los, Z., Shpak, V., Malanchuk, L., & Tsaruk, D. (2023). Implementation of the financial monitoring system into the Ukrainian enterprises activity based on the sustainable economic development and green financing concept. IOP Conference Series: Earth and Environmental Science, https://doi.org/10.1088/1755-1315/1126/1/012004
Kwak, Y. H., Park, K. M., & Lee, J. (2021). Machine Learning in Project Cost Estimation: A Systematic Review. Engineering, Construction and Architectural Management, 28(5), 1077-1095.
Landers, R. (2023). Computing intraclass correlations (ICC) as estimates of interrater reliability in SPSS. https://www.authorea.com/
Leemans, S. J., Partington, A., Karnon, J., & Wynn, M. T. (2023). Process mining for healthcare decision analytics with micro-costing estimations. Artificial Intelligence in Medicine, 135, 102473. https://doi.org/10.1016/j.artmed.2022.102473
Loang, O. K. (2023). Risk-Averse Behaviour in Emerging Markets: The Role of Economic Indicators, Bank Characteristics and Developed Markets. Jurnal Ekonomi Malaysia, 57(1), 1-16. https://doi.org/10.17576/JEM-2023-5701-04
Loang, O. K. (2023a). The Road to Sustainable Investing: Corporate Governance, Sustainable Development Goals, and the Financial Market. Institutions and Economies, 33-57. https://doi.org/10.22452/IJIE.vol15no3.2
Loang, O. K. (2023b). SUSTAINABLE DEVELOPMENT GOALS, HERDING, AND RISK-AVERSE BEHAVIOR IN MUSLIM COUNTRIES. Journal of Islamic Monetary Economics and Finance, 9(2), 313-336. https://doi.org/10.21098/jimf.v9i2.1611
Loang, O. K., & Ahmad, Z. (2021). Does volatility mediate the impact of analyst recommendations on herding in Malaysian stock market? Economics and Business Review, 7(4), 54-71. https://doi.org/10.18559/ebr.2021.4.4
Loang, O. K., & Ahmad, Z. (2022a). Does volatility cause herding in Malaysian stock market? Evidence from quantile regression analysis. Millennial Asia. https://doi.org/10.1177/09763996221101217
Loang, O. K., & Ahmad, Z. (2022b). Herding And Market Overreaction: Evidence from Shariah-Compliant Stocks in Malaysia. Global Business & Management Research, 14.
Loang, O. K., & Ahmad, Z. (2023). Economic and political factors on herding in Islamic GCC stock markets during COVID-19 pandemic. International Journal of Islamic and Middle Eastern Finance and Management, 16(4), 819-834. https://doi.org/10.1108/IMEFM-01-2022-0019
Loang, O. K., Ahmad, Z., & Naveenan, R. V. (2023). Non-Performing Loans, Macroeconomic and Bank-specific Variables in Southeast Asia during COVID-19 Pandemic. The Singapore Economic Review, 68(03), 941-961. https://doi.org/10.1142/S0217590822500679
Lotfi Haravi, M. M., Hoshmand, M., & Asadi, M. (2023). Application of Quantum Machine Learning Algorithms in Financial Sciences. Science and Technology Policy Letter, 13(1), 62-75.
Mandolini, M., Manuguerra, L., Sartini, M., Lo Presti, G. M., & Pescatori, F. (2024). A cost modelling methodology based on machine learning for engineered-to-order products. Engineering Applications of Artificial Intelligence, 136, 108957. https://doi.org/10.1016/j.engappai.2024.108957
Mehrali, Z., Hosseini, S. A., & Nasiri, H. (2022). Examination and Relationship Between Activity-Based Costing and Accounting Objectives. Seventh International Conference on New Perspectives in Management, Accounting, and Entrepreneurship, Tehran. https://civilica.com/doc/1514049
Milana, C., & Ashta, A. (2021). Artificial intelligence techniques in finance and financial markets: a survey of the literature. Strategic Change, 30(3), 189-209. https://doi.org/10.1002/jsc.2403
Nosrati, A. (2024). Multi-objective Optimization of Machine Learning Algorithms for Reducing Computational Costs and Improving Performance in Adaptive Artificial Intelligence. Nineteenth International Conference on Innovation and Research in Engineering Sciences, https://civilica.com/doc/2140176
Piccialli, F., Di Cola, V. S., Giampaolo, F., & Cuomo, S. (2021). The role of artificial intelligence in fighting the COVID-19 pandemic. Information Systems Frontiers, 23(6), 1467-1497. https://doi.org/10.1007/s10796-021-10131-x
Raoufi, A., & Bashi Azghadi, S. N. (2023). Application of Machine Learning Techniques in Estimating and Predicting Costs of Construction Projects: A Review of Research from 2017 to 2022. Sixteenth International Symposium on Advances in Sciences and Technology, Mashhad. https://civilica.com/doc/2019667
Saeed, A. M. M., Widyaningsih, A., & Khaled, A. S. D. (2023). Activity-Based Costing (ABC) in the Manufacturing Industry: A Literature Review. Journal of Developing Economies, 8, 261-270. https://doi.org/10.20473/jde.v8i2.40426
Salas-Hidalgo, L. M. (2022). Two-phase cost and time-driven activity-based cost in a manufacturing plant. https://doi.org/10.18687/LEIRD2022.1.1.64
Sidey-Gibbons, C., Pfob, A., Asaad, M., Boukovalas, S., Lin, Y. L., Selber, J. C., Butler, C. E., & Offodile, A. C. (2021). Development of machine learning algorithms for the prediction of financial toxicity in localised breast cancer following surgical treatment. Jco Clinical Cancer Informatics, 5, 338-347. https://doi.org/10.1200/CCI.20.00088
Tashmanov, G., & Tursunaliyev, I. (2023). The Importance Of Cost Management In Joint Stock Companies. Models And Methods For Increasing The Efficiency Of Innovative Research. 2(19), 148-150. https://interonconf.org/index.php/ger/article/download/1268/1176
Traore, K., & Loang, O. K. (2023). The Factors That Influence E-Banking Service Quality In Guinea's Commercial Banks. International Journal of Accounting, 8(46), 153-167.
XiXi, D., & Loang, O. K. (2023). The Sustainable Development Of Regional Rural Finance In China. International Journal of Accounting, 8(46), 59-74.
Yousef Zadeh, S., Bitari, A., & Naghdi Taheri, M. (2023). The Impact of Independence, Support from Senior Managers, and Employee Competence on Effective Internal Audit. Second National Conference on Novel Applied Research in Accounting, Damghan. https://civilica.com/doc/1964526
Zhang, L., & Li, H. (2021). Artificial Intelligence in Cost Estimation for Construction Projects. Automation in Construction, 125, 103573.
دانلود
چاپ شده
ارسال
بازنگری
پذیرش
شماره
نوع مقاله
مجوز
حق نشر 2025 Reza Asadi, Artin Beytari, Mohammadreza Ghorbaniyan (Author)

این پروژه تحت مجوز بین المللی Creative Commons Attribution-NonCommercial 4.0 می باشد.