Accurate cash flow forecasting is essential for successful management of firms and it becomes especially critical during uncertain market and credit conditions. Without accurate cash flow forecasting, a firm may fail to meet its short-term obligations and risk bankruptcy. Accurate cash flow forecasting can be limited by a number of factors including changes in macro-economic conditions that influence liquidity in the economy, customer payment behavior that can vary from time to time as well by industry, and dynamics of the particular supply chain itself. We develop stochastic financial analytics for cash flow forecasting for firms by integrating two models: (1) Markov chain model of the aggregate payment behavior across all customers of the firm using accounts receivable aging and; (2) Bayesian model of individual customer payment behavior at the individual invoice level. As the stochastic dynamics of cash flow evolves every day, the forecast can be updated every time an invoice is paid. The proposed model is back-tested using empirical data from a small manufacturing firm and found to differ 3-6% from actual monthly cash flow, and differs approximately 2-4% compared to actual annual cash flow. The forecast accuracy of the proposed stochastic financial analytics model is found to be considerably superior to other techniques commonly used. Furthermore, in computer simulation experiments, the proposed model is found to be largely robust to supply chain dynamics, including when subjected to severe bullwhip effect. The proposed model has been implemented in Excel, which allows it to be easily integrated with the accounts receivable aging data, making it practicable for small and large firms.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Economics and Econometrics
- Management Science and Operations Research
- Industrial and Manufacturing Engineering