We employ one or more of the following techniques when solving customer problems.
The selection of a best element, with regard to some criterion, from some set of available alternatives.
Constraint programming (CP) solves combinatorial problems, pulling from artificial intelligence, computer science, and operations research.
Markov Decision Processes
Used for sequential planning and decision making.
Mixed Integer Programming
The decision variables are constrained to be integer values at the optimal solution, this expands the scope of useful optimization problems that you can define and solve.
Imitation of the operation of a real-world process or system over time.
Decision analysis (DA) is a systematic, quantitative, and visual approach to addressing and evaluating the important strategic decisions the businesses sometimes must make. A comprehensive decision analysis identifies sources of value and sources of risk, as well as performs the following:
In addition to determining whether or not a project is financially viable, decision analysis identifies the most optimal method of deploying capital for best results.
Value of Information
Realizing the Value of Information is crucial in determining where valuable resources of time and money are best invested.
Our analysis produces a large amount of quantitative information that can be difficult for some stakeholders to digest. Visualizations communicate high-level insights in an efficient manner, as well as aid in getting every person at the decision-making table on the same page.
We are committed to performing accurate, deterministic analyses that are underpinned by our family values for integrity and transparency. As strategic decisions engineers, we validate our analyses to ensure that they meet both the stakeholders’ requirements, as well as our own high standards.
Artificial intelligence uses models and data to make decisions or predict outcomes.
Machine learning is an advanced concept rooted in the principles of artificial intelligence. ML applications are trained with sophisticated models and large amounts of data. Over time, ML applications reprogram themselves to be more efficient and better suited to their specific tasks.
Data is a crucial source of value and insight for a firm, though sorting through it all can prove to be a leviathan task. Our clustering algorithms can be used to organize massive amounts of data into natural groups.
Machine learning algorithms can process and analyze existing data to construct prescriptive models for future performance. Since some methods of forecasting fall short in predicting stochastic events, we offer multiple approaches to provide the most complete deterministic picture.
Time Series Analysis
Understanding how data change over time is key to interpreting phenomena, identifying trends and predicting future performance.