Original scientific paper
https://doi.org/10.18045/zbefri.2026.1.12
Enhancing Decision Support for Resource Allocation Through Markov Chain-Based Receivables Forecasting: A Robustness Analysis Using Monte Carlo Simulation
Đorđe Kotarac
; University of Belgrade, Faculty of Agriculture
*
Zoran Popović
; University of Belgrade, Faculty of Economics and Business
Goran Petković
; University of Belgrade, Faculty of Economics and Business
* Corresponding author.
Abstract
Efficient resource allocation by individuals, firms, and society represents one of the central issues in economic theory and practice. The achievement of economic objectives depends on the ability to allocate scarce resources under uncertainty. Receivables collection forecasting contributes to more efficient and lower-risk resource allocation by supporting decisions regarding the future allocation of corporate assets. This study develops and applies a forecasting framework based on an absorbing Markov chain model, complemented by a Monte Carlo robustness analysis, to estimate collection probabilities, expected collection times, and projected cash inflows from receivables over both short- and long-term horizons. The proposed approach enables the quantification of collection dynamics and provides additional
information for financial planning and investment decisions. The findings indicate that reliable receivables forecasting can improve asset allocation efficiency and reduce decision-making risk, thereby supporting improvements in corporate profitability.
Keywords
accounts receivables forecasting; decision-making; energy industry; Markov chains; Monte Carlo simulation
Hrčak ID:
348628
URI
Publication date:
30.6.2026.
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