In an increasingly competitive and dynamic business landscape, organizations are continuously seeking innovative approaches to optimize their operations and achieve sustainable growth. One crucial aspect of organizational success lies in the strategic allocation of resources. Efficient resource allocation ensures that organizations can maximize their potential while minimizing costs, thereby enhancing overall performance and fostering a competitive edge. However, the complexity of modern business environments poses significant challenges in making informed resource allocation decisions. This is where predictive analytics emerges as a powerful tool, offering the potential to transform strategic resource allocation processes.
Martin, J. (2021). Predictive Analytics for Strategic Resource Allocation. Management Analytics and Decision, 3(2), 24. doi:10.69610/j.mad.20211030
ACS Style
Martin, J. Predictive Analytics for Strategic Resource Allocation. Management Analytics and Decision, 2021, 3, 24. doi:10.69610/j.mad.20211030
AMA Style
Martin J. Predictive Analytics for Strategic Resource Allocation. Management Analytics and Decision; 2021, 3(2):24. doi:10.69610/j.mad.20211030
Chicago/Turabian Style
Martin, James 2021. "Predictive Analytics for Strategic Resource Allocation" Management Analytics and Decision 3, no.2:24. doi:10.69610/j.mad.20211030
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ACS Style
Martin, J. Predictive Analytics for Strategic Resource Allocation. Management Analytics and Decision, 2021, 3, 24. doi:10.69610/j.mad.20211030
AMA Style
Martin J. Predictive Analytics for Strategic Resource Allocation. Management Analytics and Decision; 2021, 3(2):24. doi:10.69610/j.mad.20211030
Chicago/Turabian Style
Martin, James 2021. "Predictive Analytics for Strategic Resource Allocation" Management Analytics and Decision 3, no.2:24. doi:10.69610/j.mad.20211030
APA style
Martin, J. (2021). Predictive Analytics for Strategic Resource Allocation. Management Analytics and Decision, 3(2), 24. doi:10.69610/j.mad.20211030
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Turban, E., Arras, J., & McLean, R. E. (2006). Information Technology for Management: Digital Strategies for Insight, Action, and Success (3rd ed.). John Wiley & Sons.
Han, J., Kamber, M., & Pei, J. (2006). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
Chatfield, C. (1975). Analysis of Time Series: An Introduction (3rd ed.). Hodder Education.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
Aladwani, A. M., & Soin, D. (1993). Artificial neural networks for inventory control. International Journal of Production Economics, 29(1), 33-44.
Geman, G., Bresson, X., & Muandet, N. (2012). Support vector machines for pattern recognition. IEEE Signal Processing Magazine, 29(1), 55-73.
Bowerman, B. L., O'Connell, R. T., & Murtagh, F. (2005). Linear Statistical Models: An Applied Approach (5th ed.). Jones and Bartlett Learning.
D'Souza, B. V., & Chawla, N. V. (2003). Data quality issues in data mining. In Proceedings of the IEEE International Conference on Data Mining (ICDM'03) (pp. 35-44).
Hripcsak, G., & Mark, R. (2001). Predictive models for disease diagnosis. Studies in Health Technology and Informatics, 84, 1029-1033.
Wang, Y., et al. (2011). Ensemble learning for financial time series forecasting. Expert Systems with Applications, 38(2), 1432-1440.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring efficiency in decision making: models and methods. European Journal of Operational Research, 2(6), 429-458.
Kettner, P. W., et al. (1998). Aligning measures to strategy: the Balanced Scorecard in action. California Management Review, 40(3), 53-78.