Product Specifications
This section gives you quick and easy detailed product information on Benchum Decision.
User interface Windows application, fully menu-driven, color-coded toolbars, online context-sensitive help (What's This?), tooltips
Versions Single-user version
Network version
Web host version for intranets
Update Download and upload via FTP (File Transfer Protocol), password protection
System requirements
To use Benchum Decision effectively, these are system requirements for all versions:
Computer type IBM-compatible Pentium PC
Operating system Microsoft Windows NT, 2000, XP, or Vista
Memory 128 Mbytes of RAM
Hard disk 300 Mbytes of available hard disk space
Video display VGA video 800x600 resolution or higher, color mode of high color 16-bit or greater
Optional An Internet connection, plus an ISP (Internet Service Provider)
Network version Novell Netware or Microsoft Windows network
Web host version Windows NT, 2000, XP, Server or Vista,
Unix or Linux (on request) server
Supported file formats
Benchum Decision can save the listed report and image file formats, and open and save the listed spreadsheet file formats. The internal database uses a proprietary component file format.
Reports PDF (Portable Document Format)
EPS (Encapsulated PostScript”)
Spreadsheets XML (Extensible Markup Language)
TXT (Tabbed Text)
CSV (Comma Separated Values)
Images EMF (Enhanced Windows Metafile Format)
BMP (Windows Bitmap)
TIFF (Tag Image File Format)
GIF (Graphic Interchange Format)
PNG (Portable Network Graphic)
JPEG (Joint Photographic Experts Group)
Financial and statistical functions
All versions of Benchum Decision offer the following functions in easy-to-understand groupings:
Capital budgeting Net present value, internal rate of return (IRR), modified IRR, payback, profitability index, and others
Smoothing Moving average, exponential smoothing, polynomial smoothing
Trend analysis Linear, exponential, multiplicative, reciprocal, S-curve, quadratic, polynomial trend, test statistics
ARIMA Autoregressive integrated moving average modeling;
rule based selection process, test statistics
Regression models Automatic variable selection: forward (base model) and backward (maximum model) selection, test statistics
Artificial neural networks Multilayered, feed forward networks with back propagation learning
Expert system Rule based, modular rule bases for mini applications;
rule parser, inference interpreter with forward chaining, explanation component