VERİ ANALİTİĞİ VE KARAR VERME: MATEMATİKSEL MODELLERİN İŞLETMELERDE KULLANIMI: DATA ANALYTICS AND DECISION-MAKING: APPLICATIONS OF MATHEMATICAL MODELS IN BUSINESS OPERATIONS

Ebru GÖKSU KIRCA

doi:10.5281/zenodo.16968627

Authors

Ebru GÖKSU KIRCA Bağımsız Araştırmacı https://orcid.org/0009-0002-4378-6560

DOI:

https://doi.org/10.5281/zenodo.16968627

Keywords:

Data analytics, mathematical models, decision-making, business management, optimization

Abstract

In the modern business environment, where global competition is intensifying, organizations are compelled to make rapid, precise, and data-driven decisions to sustain their competitive advantage. Within this context, data analytics and mathematical models are regarded not merely as technical tools but as integral components of strategic management processes. This study provides a comprehensive examination of how data analytics techniques and mathematical modeling approaches are integrated into both strategic and operational decision-making frameworks in business settings. Particular emphasis is placed on the practical applications of optimization, regression analysis, decision trees, machine learning, and simulation methods. Furthermore, the study highlights the integration of artificial intelligence and big data technologies into decision support systems, exploring their potential to enhance operational efficiency and secure sustainable competitive advantages. The findings indicate that the development of data-driven decision mechanisms significantly reduces risks, optimizes resource allocation, and fosters long-term organizational success. Additionally, the critical roles of data quality, analytical infrastructure, and human expertise in ensuring the effectiveness of these processes are underscored. In conclusion, the research emphasizes that businesses must adopt a holistic approach encompassing technical, strategic, and human resource dimensions to fully leverage data analytics and mathematical modeling in their decision-making practices.

References

Bazerman, M. H., & Moore, D. A. (2013). Judgment in Managerial Decision Making (8th ed.). Wiley.

Bhimani, A. (2020). Big Data, Analytics and the Future of Accountancy. Routledge.

Bihani, P., & Patil, A. (2014). A Comparative Study of Data Analytics Tools. International Journal of Advanced Research in Computer Science and Software Engineering, 4(6), 216-220.

Blum, W., & Niss, M. (1991). Applied mathematical problem solving, modelling, applications, and links to other subjects-State, trends and issues in mathematics instruction. Educational Studies in Mathematics, 22(1), 37-68. https://doi.org/10.1007/BF00302716

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503

Chen, M., Mao, S., Zhang, Y., & Leung, V. C. (2014). Big Data: Related Technologies, Challenges and Future Prospects. SpringerBriefs in Computer Science. https://doi.org/10.1007/978-3-319-04919-8

Chong, A. Y. L., & Shi, Y. (2021). Big data analytics for supply chain management: A review and research agenda. International Journal of Production Economics, 235, 108103. https://doi.org/10.1016/j.ijpe.2021.108103

Davenport, T. H. (2010). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.

Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Press.

Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116.

Eykhoff, P. (1974). System Identification: Parameter and State Estimation. Wiley-Interscience.

Fitz-enz, J., & Mattox, J. R. (2014). Predictive Analytics for Human Resources. Wiley.

Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1-15. file:///C:/Users/User/Downloads/c8d3cba5-8f10-4a00-894c-3a3b886ad844.pdf, 10.06.2025

Gandomi, A., & Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Giordano, F. R., Weir, M. D., & Fox, W. P. (2014). A First Course in Mathematical Modeling (5th ed.). Brooks Cole.

Gonzalez-Feliu, J., & Morana, J. (2020). Sustainable Urban Logistics: Concepts, Methods and Information Systems. Springer.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Hillier, F. S., & Lieberman, G. J. (2021). Introduction to Operations Research (11th ed.). McGraw-Hill Education.

Hull, J. C. (2018). Options, Futures, and Other Derivatives (10th ed.). Pearson Education.

Kantardzic, M. (2020). Data Mining: Concepts, Models, Methods, and Algorithms (3rd ed.). Wiley.

Kaplan, D. (1997). Structural Equation Modeling: Foundations and Extensions. Sage Publications.

Kotler, P., & Keller, K. L. (2019). Marketing Management (15th ed.). Pearson Education.

Logan, J. D. (2013). Applied Mathematics (4th ed.). Wiley.

Mintzberg, H., Raisinghani, D., & Théorêt, A. (1976). The structure of “unstructured” decision processes. Administrative Science Quarterly, 21(2), 246-275. https://doi.org/10.2307/2392045

Provost, F., & Fawcett, T. (2013). Data Science for Business. O'Reilly Media.

Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media, Inc.

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

Saaty, T. L. (2008). Decision Making with the Analytic Hierarchy Process. International Journal of Services Sciences, 1(1), 83-98.

Saltelli, A., Chan, K., & Scott, E. M. (2000). Sensitivity Analysis. Wiley.

Shmueli, G. et al. (2020). Data Mining for Business Analytics. Wiley.

Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python (3rd ed.). Wiley.

Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies (3rd ed.). McGraw-Hill.

Simon, H. A. (1960). The New Science of Management Decision. Harper & Brothers.

Simon, H. A. (1977). The Structure of Ill-Structured Problems. Artificial Intelligence, 4(3-4), 181-201. https://doi.org/10.1016/0004-3702(73)90011-7

Smith, J., & Anderson, H. (2020). AI and Customer Behavior Prediction in E-Commerce. Journal of Retail Analytics, 16(2), 35-42.

Taha, H. A. (2017). Operations Research: An Introduction (10th ed.). Pearson.

UPS. (2022). 2022 Corporate Sustainability Report. https://www.ups.com/sustainability

Wedel, M., & Kamakura, W. A. (2012). Market Segmentation: Conceptual and Methodological Foundations (2nd ed.). Springer.

Winston, W. L. (2004). Operations Research. Duxbury Press.

Winston, W. L., & Goldberg, J. B. (2004). Operations Research: Applications and Algorithms (4th ed.). Duxbury Press.

Zarsky, T. Z. (2016). Incompatible: The GDPR in the Age of Big Data. Seton Hall Law Review, 47(4), 995-1020.

DATA ANALYTICS AND DECISION-MAKING: APPLICATIONS OF MATHEMATICAL MODELS IN BUSINESS OPERATIONS