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Central Data Repository and Audit Trail Cut Forecasting Time Down by Hours
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Mining
Applicable Functions
- Business Operation
- Quality Assurance
Services
- Software Design & Engineering Services
- System Integration
The Challenge
The finance team of a leading North American mining company faced significant challenges in managing their forecasting data. All forecasting data was stored in a single, complex workbook that was prone to being lost or damaged. The use of uncontrolled spreadsheets allowed unauthorized changes to data, formulas, and calculations, making it difficult to track the status of the forecast or determine which user made specific changes without exchanging emails or phone calls. Additionally, making real-time changes to the forecast was cumbersome because relevant information was scattered across disparate spreadsheets that required manual sorting and review.
About The Customer
The customer is one of North America's leading mining companies, responsible for forecasting the value of all their shipments. The finance team had to manage a 30-page linked workbook that contained numerous factors affecting the value of shipments before they reached customers. The need for real-time changes to the forecast was critical, given the dynamic nature of the mining industry. The company required a robust solution to streamline their forecasting process, enhance data security, and improve overall efficiency.
The Solution
Vena provided a comprehensive solution by integrating a secure, central database that seamlessly synced with the client's General Ledger (GL) and Ellips. This central repository automatically stored all the company's up-to-date spreadsheets, templates, assumptions, and drivers, eliminating the need for manual work. Vena's detailed audit trail feature date-, time-, and user-stamped all changes made to spreadsheets, enabling managers to easily roll back to previous versions and compare changes over specific time periods. The finance team could now create quarterly, weekly, monthly, and even daily forecasts, and perform what-if modeling by changing assumptions and visualizing their effects on forecast scenarios. This allowed for more informed decision-making and improved the overall forecasting process.
Operational Impact
Quantitative Benefit
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