"Simplifying complexity"
Cutting-edge advanced analytics for solving real problems
Evolution of analytics
Analytics has evolved from classical statistics to
self-learning algorithms, from:
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Modeling non-linearities with a high capacity.
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Incorporating multiple data sources.
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Delivering adaptive predictions in real time.
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Delivering greater precision and strategic context, among others.
That is, moving from retrospective analysis (descriptive and diagnostic) to prospective analysis (predictive and prescriptive); from information to optimization.
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Our focus
Predictive and prescriptive analysis, oriented to the autonomous or semi-autonomous analysis of data or content.
We develop and implement artificial intelligence, machine learning, and other tools and techniques more advanced than those of traditional intelligence, in order to obtain deeper knowledge, make predictions or generate recommendations.
Advanced mathematical modeling
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Development of complex mathematical models that are presented in processes.
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Simulation of scenarios for strategic and operational decision-making.
Operations research and optimization
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Application of optimization algorithms to maximize efficiency, reduce costs and improve the use of assets throughout the value chain.
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Optimization of production planning, operational continuity, predictive maintenance, inventory management and logistics routes.
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Optimization of resource allocation and scheduling of operations.
Complex Problem Solving
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Analytical and structured approach to address non-trivial technical and operational challenges.
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Interpretation of real-world problems in mathematical formulations to develop and deliver innovative and efficient solutions.
Graph theory
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Transport networks and supply chain optimization.
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Analysis of flows and bottlenecks.
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Modeling and optimization of supply chain.
