- Modeling and Forecasting
Modeling Techniques: Application of predictive and descriptive models using tools such as R, Python, and Excel to forecast market trends and optimize decision-making.
Statistical Analysis: Use of regression analysis, Monte Carlo simulations, and Bayesian analysis to interpret complex data.
Example: Predicting energy consumption trends for a public utility, enabling resource optimization and a reduction in operational costs.
- Business Intelligence (BI)
BI Tools: Development of interactive dashboards with Power BI, Tableau, and QlikView for real-time data visualization.
Data Warehousing: Design and management of data warehouses to centralize information and facilitate access to critical data.
Practical Case: Implementation of a BI system for a manufacturing company, improving strategic decision-making through better analysis of production performance.
- Process Optimization
Lean Six Sigma Methods: Application of Lean Six Sigma methodologies to identify inefficiencies, reduce variabilities, and improve processes.
Techniques: Use of process mapping (Value Stream Mapping) and root cause analysis to solve problems.
Case Study: Optimization of production processes for a factory, reducing waste by 15% and improving productivity by 10% through root cause analysis.