Historical data offers management insight, but data is often cyclical. Businesses have busy times during the day or month, industries undergo output fluctuations, and some economic factors experience regular patterned trends.
The most substantial forecasting models estimate these underlying trends and identify common patterns found in historic data and separate them from random fluctuation, using the patterns to reconstruct expected values for the time series in the future. Accurately forecasting a time series is becoming a core business component, essential for supply chain and inventory management, yield management, staff planning, and more. With a forecasting model that delivers timely, accurate results, more business processes benefit from the knowledge stored in the underlying trends of historical data.
The ARGO Forecasting Engine calculates the expected future values of historical time-based data sets that range from volume and period data for incoming calls at a call center to transaction flow volumes for teller stations in a bank branch, or any time/value series of data.
Automatically Account for Time Patterns and Long-Term Trends
Because each data set has unique patterns based on hourly, weekly, and yearly trends, the forecasting engine creates a distinct model for each data set. These individualized models adjust automatically as new data is incorporated into the system through monthly updates, so that sustained patterns are discovered and included in future estimates, while single outlier values are handled without negatively impacting the forecasts.
Anticipate Cash Inventory Needs
Forecasting Engine plays a key role in ARGO Cash Inventory Optimization, a cash management solution proving systematic, denominational order recommendations, while monitoring inventories in real time. This powerful solution seamlessly integrates with ARGO Teller Payments to effectively manage ATM, branch, and central vault levels for enhanced efficiency and profitability.
Advanced real-time analytics produce optimized cash orders and inventory levels at the denominational level. The engines combine the data with business rules and parameters set up by each location to create forecasts and generate order recommendations. The result: precise amounts of currency on hand to reduce non-earning cash assets while delivering a superior customer experience.