Invoice level forecasting with discrete survival methods for effective forecasting of account receivables in supply chain

Abstract

The objective of this study is to obtain accurate and timely Account Receivables forecasts that can feed into the Cash Flow Forecasting (CFF) for a generic wholesales scenario in a supply chain company. The main components of the CFF are the Account Receivables (AR), Account Payables (AP) and Working Capital (WC). The focus of this work is on AR, in particular on how they contribute to the overall cash flow forecasting. The prediction of AR is based on the predicted payment date of each invoice, which can be obtained from the time to payment given the issue date. In this context, we propose the use of discrete survival methods for predicting the time to payment of issued invoices. To obtain more accurate predictions for time to payment, we fit a generalized linear mixed effects model with a time-varying baseline hazard function. This approach provides greater precision for subsequent account receivables predictions compared to the prediction based on simply the payment terms of each invoice, standard time series and classification models, and continuous-time survival methods. We demonstrate the approach on two financial data sets.