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How a Construction Company Streamlined Forecasting Across 900+ Projects to Gain Continuous Visibility and Avoid Cash Crunches
Technology Category
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Enterprise Resource Planning Systems (ERP)
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Construction & Infrastructure
Applicable Functions
- Business Operation
Services
- System Integration
- Software Design & Engineering Services
The Challenge
Forecasting was done with FP&A and ERP reports as inputs. An indirect method was used where data was taken from balance sheet and income statement to generate only longer-term forecasts with poor insights. With 900+ projects in hand, gathering data from multiple divisions was tedious and error-prone. Delays in reporting from these divisions affected continuous visibility. Forecast models lacking key drivers for construction & engineering business provided no short-term visibility resulting in frequent cash crunches and high-interest loans.
About The Customer
The customer is a large construction company based in the United States with a revenue of $600 million. The company handles over 900 projects across various divisions, making data gathering and forecasting a complex and error-prone process. The company previously relied on indirect methods for forecasting, using balance sheets and income statements, which provided poor insights and lacked short-term visibility. This often resulted in frequent cash crunches and the need for high-interest loans. The company sought a solution to streamline its forecasting process, improve accuracy, and gain continuous visibility into its cash flow.
The Solution
The company implemented HighRadius' Cash Forecasting Cloud and Cash Management Cloud solutions. These AI-enabled platforms provided daily forecasts at the GL account level and integrated seamlessly with the company's existing ERP and FP&A systems. The solution utilized machine learning to predict invoice-level payment dates and vendor payment history, significantly improving the accuracy of accounts receivable and payable forecasts. The system also offered flexible models for other operational cash flow categories, such as payroll and expense reimbursements, and allowed for the configuration of non-operational cash flows like tax and investments. The integration of forecasts from the FP&A team further fine-tuned the accuracy of medium and long-term cash forecasts. The closed-loop machine learning feedback system continuously improved forecasting accuracy by comparing forecasted versus actual cash positions.
Operational Impact
Quantitative Benefit
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