STOILOV, T.A.; Stoilova, K.P. Quantitative Planning Credit Policy for Business Management. Preprints2023, 2023121397. https://doi.org/10.20944/preprints202312.1397.v1
APA Style
STOILOV, T.A., & Stoilova, K.P. (2023). Quantitative Planning Credit Policy for Business Management. Preprints. https://doi.org/10.20944/preprints202312.1397.v1
Chicago/Turabian Style
STOILOV, T.A. and Krasimira Petrova Stoilova. 2023 "Quantitative Planning Credit Policy for Business Management" Preprints. https://doi.org/10.20944/preprints202312.1397.v1
Abstract
This study considers the values of the credit payments that are made at random values for both payment size and number. The simultaneous existence of the two random variables increases the total value of the volatility of the payment process. The dependence for the simultaneous influence of the two random variables is derived by applying conditional probabilistic formulations. Formal relations apply to animal husbandry credit payments. The credit policy was analyzed for the cases of regular payments and with a stochastic number of payments. A recommendation for the upper level of credit payments is proposed based on a model predictive approach using historical payment data. A credit management algorithm is derived, in which decision-making for credit payments is formally derived and numerically discussed. The application of derived quantitative relations is empirically applied to real data on animal husbandry. The recommendations from the dependencies obtained allow a reduction in the mean values of credit payments, which correspond to a reduction in the credits used for business management, without a change in business policy.
Keywords
algorithm for active credit management; stochastic number and values of payments, conditional formalization; resource allocation
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
