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Dynamic optimization of cash flow management decisions: a stochastic model

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3 Author(s)
M. E. Pate-Cornell ; Dept. of Ind. Eng. & Eng. Manage., Stanford Univ., CA, USA ; G. Tagaras ; K. M. Eisenhardt

A stochastic method is proposed that is based on decision analysis and Bayesian updating to monitor cash flow and make short-term decisions when a liquidity squeeze appears possible. The uncertainties about the payment time of outstanding bills sent out to customers, and the updating of this information as the manager gets to know his customers through successive payments, is modeled. This updating is done through the use of conjugate probability distributions that allow closed-form analytical computation of the probability density functions for the payment of each client given past experience. The use of exponential utility functions allows simple computation of the benefits of this cash-flow monitoring system. This formulation is adapted to the case of small and new business with a small number of customers whose buying and paying schedules are critical for the firm. It can be particularly useful for new high-technology ventures as part of their strategy to manage short-term financial risk. An illustrative example is used to assess the benefits of such a monitoring system

Published in:

IEEE Transactions on Engineering Management  (Volume:37 ,  Issue: 3 )