- Financial Management
- Customer Relationship Management
- Order Management
- Procurement Management
- Inventory Management
- Warehouse Management
- Manufacturing Management
- Planning Management
- Business Intelligence
- Project Accounting
NGRERP software includes a sophisticated demand and sales forecasting software solution. Manufacturing and distribution organizations can manage an infinite number of forecasts in NGRERP, which can be entered manually or automatically generated through a series of algorithms to determine anticipated future demand on a product-by-product basis. Once created, forecastsare used as inputs to NGRERP's material requirements planning (MRP) and distribution requirements planning (DRP) processes.
A variety of different data dimensions are available for sales and demand forecasting. These include break-outs of data by:
- Geography – from as detailed as 9-digit postal codes to as aggregated as user definable regions
- Time periods – from as detailed as a day to as aggregated as a year, with up to 365 different periods in any forecast
- Customers – from as detailed as an individual customer ship-to location through any one of four additional user-defined customer hierarchal aggregate levels
- Products – from as detailed as an individual SKU through any of four additional user defined product hierarchal aggregate levels
In addition, each individual forecast can be generated using a different date range of historical information. NGRERP's sales forecasting software functionality is capable of utilizing more recent sales trends rather than long term trends.
Each time a forecast is generated, NGRERP runs a series of statistical forecasting algorithms and determines which of the various demand and sales forecasting methods represents the best fit to the historical data. It then chooses this forecasting technique to project future demand. As a result, a number of different forecasting techniques may be utilized in a given forecast since the best forecasting technique is chosen for each product, customer, time period, and geographical data combination.