Decision Support

We have formed a strong partnership with and highly recommend the prominent decision support statistical tool, Frontline Solvers. Many businesses have made substantial investments in data science, risk modeling, and classification models, only to discover that more economical options like Python or R may not always meet their needs. The most successful companies in data science rely on tools that consistently yield results, and Frontline Solvers is widely recognized for doing so. While we do not impose the use of any specific software, we enthusiastically suggest it to our clients when it aligns with their requirements. We are fully prepared to provide expert guidance on leveraging its capabilities.

Frontline Solvers

Frontline Systems offers predictive and prescriptive analytics software used by 10,000 organizations. Its solutions use mathematical optimization, business rules, simulation, risk analysis, data mining, text mining, and forecasting within applications like Excel or in the Azure Cloud using RASON.

Optimization

Unleashing the potential of optimization is imperative for identifying the optimal methods for resource allocation, encompassing financial resources, personnel hours, raw materials, inventory, energy, and warehouse space. Employing a sophisticated tool such as Frontline and inputting varied parameters enables the strategic analysis of decisions within distinct constraints, ultimately revealing the most effective and efficient approach. This optimal resolution has the potential to lead to substantial profit escalation, reduction in expenditures, or the attainment of the highest standards of quality.

Data Science

Harness statistical and machine-learning techniques to analyze data, make predictions, and uncover valuable patterns and relationships with Analytic Solver Data Science. It provides methods for analyzing data and includes statistical and machine-learning techniques for tasks such as classification, prediction, affinity analysis, data exploration, and reduction.

Risk & Simulation

In computer simulations, a mathematical model is utilized to emulate the real system, using variables to represent key numerical measures of inputs and outputs. Uncertain variables are integrated to account for elements not under control, while decision variables are included for parameters that can be controlled. Random number generators are employed to represent uncertain variables, and a simulation run includes hundreds or thousands of trials to assess the impact of uncertain variables and decisions on outcomes. The simulation model, often referred to as a risk model, evaluates impacts on areas such as profit and loss, investment returns, and environmental consequences. During model design, decisions are made on how numerical values for uncertain variables will be sampled in each trial.

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