Utilization Of Artificial Intelligence In Demand Forecasting: A Qualitative Study Of Business Actors
DOI:
https://doi.org/10.66784/joss.v1i2.44Keywords:
Artificial Intelligence, Demand forecasting, Digital Business, Managerial Decision-Making, Qualitative StudyAbstract
Market uncertainty in the era of volatility, uncertainty, complexity, and ambiguity (VUCA) demands that businesses possess high predictive acumen. This study explores the use of Artificial Intelligence (AI) in demand forecasting through a descriptive qualitative approach. The study focuses on a deeper understanding of how the integration of AI technology transforms managerial decision-making processes and the dynamics of its adaptation within the digital business ecosystem in Indonesia. In-depth interviews were conducted with ten informants holding managerial and operational analyst positions in the e-commerce and technology retail sectors in Indonesia. The results show that the application of AI, particularly based on Machine Learning and Deep Learning, is able to reduce subjective human bias and capture non-linear data patterns that conventional statistical methods fail to identify. This implementation significantly improves inventory estimation accuracy, minimizes holding costs, and prevents loss of sales momentum (stockouts). However, the transition to an AI-based forecasting system faces significant structural challenges, including data quality issues (data silos), limited local talent with multidisciplinary competencies, and cultural resistance within the organization. From the perspective of managerial decision-making theory, AI acts as a cognitive amplification tool, shifting the paradigm from pure intuition to data-driven decision-making. This study concludes that the success of AI implementation is determined not only by the sophistication of the algorithm, but also by the readiness of data governance and the alignment of inclusive organizational strategies
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References
(Apjii), A. P. J. I. I. (2024). Laporan Survei Penetrasi Internet Indonesia 2024. Apjii Resmi. Https://Apjii.Or.Id/Survei
Ali, H., Rivai Zainal, V., & Rafqi Ilhamalimy, R. (2022). Determination Of Purchase Decisions And Customer Satisfaction: Analysis Of Brand Image And Service Quality (Review Literature Of Marketing Management). Dinasti International Journal Of Digital Business Management, 3(1), 141–153. Https://Doi.Org/10.31933/Dijdbm.V3i1.1100
Anagnoste, S. (2018). Setting Up A Robotic Process Automation Center Of Excellence. Management Dynamics In The Knowledge Economy, 6(2), 307–322. Https://Doi.Org/10.25019/Mdke/6.2.07
Ananda, A. P., Sari, A. K., & Fatchurrohman, M. (2023). The Influence Of Price, Location And Word Of Mouth On Purchasing Decisions At Green Resto. Journal Of Artificial Intelligence And Digital Business, 2(1), 24–30. Https://Journal.Ilmudata.Co.Id/Index.Php/Riggs
Arief, Z., & Brabo, N. A. (2025). The Influence Of Word Of Mouth And Social Media Marketing On Students’ Decision To Choose A University With Brand Image As A Mediating Variable (Case Study At Darunnajah University). Journal Of Economics And Business (Jecombi), 6(03), 227–243. Https://Doi.Org/10.58471/Jecombi.V6i03.140
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The Impact Of Artificial Intelligence And Information Technology On Supply Chain Resilience And Performance. International Journal Of Logistics Research And Applications, 24(1), 1–21. Https://Doi.Org/10.1080/13675567.2021.1981273
Bishop, C. M., & Bishop, H. S. (2023). Deep Learning: Foundations And Concepts. Springer Nature. Https://Link.Springer.Com/Book/9783031454677
Bodie, Z., Kane, A., & Marcus, A. J. (2021). Essentials Of Investments (12th Ed.). Mcgraw-Hill Education. Https://Www.Mheducation.Com
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application Of Machine Learning Techniques For Supply Chain Demand Forecasting. European Journal Of Operational Research, 184(3), 1140–1154. Https://Doi.Org/10.1016/J.Ejor.2006.12.004
Chopra, S., & Meindl, P. (2016). Supply Chain Management: Strategy, Planning, And Operation (6th Ed.). Pearson. Https://Www.Pearson.Com
Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry And Research Design: Choosing Among Five Approaches (4th Edition (Ed.)). Sage Publication Inc.
Davenport, T. H., & Mittal, N. (2020). How To Set Up An Ai Center Of Excellence. Harvard Business Review, 98(4), 45–53. Https://Hbr.Org/2020/07/How-To-Set-Up-An-Ai-Center-Of-Excellence
Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence For The Real World. Harvard Business Review, 96(1), 108–116. Https://Hbr.Org/2018/01/Artificial-Intelligence-For-The-Real-World
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Mit Press. Https://Www.Deeplearningbook.Org
Google, Temasek, & Company, B. &. (2024). E-Conomy Sea 2024: Roaring Apace - Southeast Asia Digital Economy Report. Think With Google. Https://Www.Thinkwithgoogle.Com/Intl/En-Apac/Consumer-Insights/Consumer-Trends/Economy-Sea-2024/
Herawatie, D., Siswanto, N., & Widodo, E. (2024). Motorcycle Taxi In Shared Mobility And Informal Transportation: A Bibliometric Analysis. Journal Of Information Systems Engineering And Business Intelligence, 10(2), 250–269. Https://Doi.Org/10.20473/Jisebi.10.2.250-269
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles And Practice (2nd Ed.). Otexts. Https://Otexts.Com/Fpp2/
Irwan, I., Zusmelia, Z., Siska, F., Mazya, T. M., Elvawati, E., & Siahaan, K. W. A. (2022). Analysis Of Relationship Between Conversational Media Applications And Social Media With Social Capital In Disaster Mitigation At The Area Of Bogor Regency, Indonesia. International Journal Of Multidisciplinary: Applied Business And Education Research, 3(7), 1434–1442. Https://Doi.Org/10.11594/Ijmaber.03.07.22
Ivanov, D., Tang, C. S., & Dolgui, A. (2021). The 3d (Digital, Disrupted, And Distributed) Supply Chain: Managing The New Normal. International Journal Of Production Research, 59(12), 3501–3507. Https://Doi.Org/10.1080/00207543.2021.1919245
Jafari-Sadeghi, V., Garcia-Perez, A., Candelo, E., & Couturier, J. (2021). Exploring The Impact Of Digital Transformation On Technology Entrepreneurship And Technological Market Expansion: The Role Of Technology Readiness, Exploration And Exploitation. Journal Of Business Research, 124, 100–111.
Jararra, M., Al-Sartawi, A., & El Khoury, R. (2023). Augmented Intelligence And Managerial Decision-Making Efficiency: A Systematic Review And Conceptual Framework. Journal Of Enterprise Information Management, 36(5), 1205–1230. Https://Doi.Org/10.1108/Jeim-02-2022-0054
Jurafsky, D., & Martin, J. H. (2023). Speech And Language Processing (3rd Ed. Draft). Stanford University. Https://Web.Stanford.Edu/~Jurafsky/Slp3/
Kahneman, D. (2021). Thinking, Fast And Slow. Farrar, Straus And Giroux. Https://Us.Macmillan.Com
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer New York. Https://Doi.Org/10.1007/978-1-4614-6849-3
Lähteenmäki, I., Nätti, S., & Saraniemi, S. (2022). Digitalization-Enabled Evolution Of Customer Value Creation: An Executive View In Financial Services. Journal Of Business Research, 146. Https://Doi.Org/10.1016/J.Jbusres.2022.04.002
Miles, M. B., Huberman, A. M., & Saldaña, J. (2022). Qualitative Data Analysis: A Methods Sourcebook (3rd Ed.). Sage Publications.
Rialti, R., & Filieri, R. (2024). Leaders, Let’s Get Agile! Observing Agile Leadership In Successful Digital Transformation Projects Riccardo. Business Horizons, 2(2), 33–47. Https://Doi.Org/10.1016/J.Bushor.2024.04.003
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th Ed.). Pearson. Https://Www.Pearson.Com
Sakib, M. N., Ullah, M. S., & Rahman, M. M. (2025). Mapping The Evolution Of Digital Human Resource Management: A Systematic Review And Bibliometric Analysis. Future Business Journal, 11(1), 154. Https://Doi.Org/10.1186/S43093-025-00577-9
Simon, H. A. (2020). Models Of Bounded Rationality: Empirically Grounded Economic Reason (Vol. 3). Mit Press. Https://Mitpress.Mit.Edu
Soust-Verdaguer, B., Llatas, C., & García-Martínez, A. (2020). Critical Review Of Artificial Intelligence Applications In Demand Forecasting For Building Materials. Automation In Construction, 119, 103362. Https://Doi.Org/10.1016/J.Autcon.2020.103362
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