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What is GUMsim?

Measurements inevitably experience variations from their true values, a scenario referred to as measurement uncertainty, which is particularly relevant when evaluating or calibrating measurement instruments or methods. Accurately quantifying this uncertainty is crucial for maintaining quality control. GUMsim®, compliant with the most recent Guide to the Expression of Uncertainty in Measurement (GUM) and its supplement 1, employs advanced computational algorithms that support a more efficient assessment of measurement uncertainty in accordance with ISO/IEC 17025 standards. The assessment of measurement uncertainty relies on a mathematical framework and statistical evaluation of all factors that affect measurement results. To simplify this procedure, GUMsim offers an intuitive input interface suitable for various measurement models. Furthermore, it includes multiple pre-defined application models, which serve as templates designed to aid users in carrying out specific evaluations, facilitating the initiation of measurement uncertainty assessments. This aspect not only improves the overall user experience but also promotes uniformity in measurement practices across diverse applications, ultimately reinforcing the reliability of measurement results. As a result, users can approach their uncertainty evaluations with greater confidence and efficiency.

What is Analytic Solver?

Analytic Solver Optimization provides full compatibility with the Excel Solver and is adept at tackling various traditional optimization problems without uncertainty, regardless of their size or complexity. Its distinguishing feature lies in its ability to algebraically analyze the structure of your model while harnessing the complete processing power of multiple cores available on your computer. This tool can efficiently solve nonlinear models that are up to ten times larger and linear models that can be forty times larger than those manageable by the Excel Solver, resulting in significantly quicker solutions and the ability to incorporate Solver Engines capable of handling millions of variables. Furthermore, Analytic Solver Simulation offers a highly intuitive yet powerful platform for conducting Monte Carlo simulations, performing risk analysis, creating decision trees, and optimizing simulations, all while utilizing Frontline’s advanced Evolutionary Solver. With an extensive selection of 60 probability distributions, including compound distributions, automatic fitting, rank-order correlations, and copula-based correlations, as well as 80 statistical measures, various risk assessments, Six Sigma functions, and the potential for multiple parameterized simulations, it emerges as a comprehensive resource for intricate analytical requirements. This rich array of features guarantees that users can conduct thorough analyses efficiently and accurately, solidifying its status as an essential tool in both optimization and simulation fields. Ultimately, the versatility and power of Analytic Solver make it a go-to choice for professionals seeking to enhance their analytical capabilities.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

€870 one-time payment

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

Windows

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

QuoData

Company Location

Germany

Company Website

www.quodata.de/gumsim

Company Facts

Organization Name

Frontline Systems

Company Website

www.solver.com

Categories and Features

Statistical Analysis

Statistical Simulation

Categories and Features

Predictive Analytics

AI / Machine Learning
Data Mining
Demand Forecasting
Modeling & Simulation
Sentiment Analysis

Risk Management

Dashboard
Risk Assessment

Simulation

Continuous Modeling
Monte Carlo Simulation
Stochastic Modeling

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