Automatic P2P Cash Flow Forecasting Across Three Cycle Times
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional procure-to-pay (P2P) processes are time-consuming, labor-intensive, and prone to disruptions, with a need for improved cash flow forecasting to enhance decision-making by treasurers.
Innovation Solution
A computer-implemented method for automatic data forecasting in P2P processes using statistical analysis algorithms to predict cash flow cycles, including requisition-to-purchase-order, purchase-order-to-invoice, and invoice approval times, based on historical data and Gaussian distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional P2P processes are used, then manual processing and monitoring are possible, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs automatic cash flow forecasting by self-processing historical data through statistical algorithms without requiring manual intervention. The forecasting manager autonomously executes the forecasting process, eliminating the need for manual data processing and monitoring while improving efficiency and reducing time loss.
2Reliability
If traditional P2P processes are used, then simple processing is possible, but the process is prone to disruptions and human errors
Solution Approach 1:
The system replaces manual mechanical processing with automated computer-implemented statistical algorithms. The forecasting manager uses Gaussian distributions and statistical models to automatically process historical data, eliminating human errors and disruptions while improving process stability and reliability.
3Measurement precision
If detailed statistical analysis is performed, then accurate cash flow forecasts are achieved, but computational complexity increases
Solution Approach 1:
The system changes the parameters of analysis by focusing on specific statistical parameters (mean, standard deviation) from historical data rather than performing exhaustive complex analysis. The forecasting manager applies Gaussian distributions with these parameters to generate accurate forecasts while managing computational complexity through parameter-based statistical modeling.
Data Source
AI summary
A computer-implemented method for automatically data forecasting a cash flow in a Procure-to-Pay (P2P) process for a company includes receiving historical data and a requisition request from a user, generating three procurement models, predicting and/or determining amounts and transaction data associated with three cycle times, outputting the amounts and transaction data, generating a work order to improve liquidity planning, and displaying the work order on a user interface. Each of the three procurement models is based at least in part on historical data. The first procurement model is based at least in part on a requisition request and predicts requisition-to-purchase-order cycle time data. The second procurement model is based at least in part on a purchase order and predicts purchase-order-to-invoice cycle time data. The third procurement model is based at least in part on an invoice and predicts invoice approval cycle time data.