Performance Optimisation
COURSE DURATION
3 Days
CLASS DESCRIPTION
Performance optimisation is a crucial element in achieving excellence across industries. It relies on the availability of high-quality models, efficient simulation techniques, and fast, comprehensive data analysis. Just as important is the ability to communicate insights clearly and effectively across teams to ensure alignment and informed decision-making. This three-day training program is designed to provide a structured approach to performance optimisation, incorporating best practices from various areas, to enhance efficiency, modelling, and data-driven decision-making.
LEARNING OUTCOMES
Understanding objectives, timing, communication and reporting.
- The Role of Clear Communication and Efficient Reporting in Performance Optimisation
- The Foundation of Performance Optimisation
Understanding the role of the simulation, the level of complexity required for the models, the importance of regular correlation, simplification, linearisation.
- The importance of choosing the right level of complexity and efficient models correlation
- Structuring Simulation and Models to optimise the performance
Understanding the limitations of the data sources, extract the best knowledge from the data and understand data-driven performance optimisation.
- Understanding limitations of sensors and real world data
- Extracting knowledge from the data and applying data-driven performance optimisation
COURSE STRUCTURE
Day 1: FUNDAMENTALS OF PERFORMANCE OPTIMISATION
The Role of Clear Communication and Efficient Reporting in Performance Optimisation
- Communication is a key factor in performance and decision making
- Efficient communication, reporting and data storytelling to support decisions
- Adaptability in communication for different stakeholders and cross-functional teams
- The role of feedback loops, adjustability and alignment
- Augmenting team performance leveraging AI
The Foundation of Performance Optimisation
- Defining performance optimisation in different industries
- Understanding the objectives, the main KPIs, constraints and trade-offs
- Understanding deadlines and milestones, agile and ‘hybrid’ approaches
- Process mapping
- Case Study: Development in Motorsport- Data driven decisions, marginal gains, rapid iteration
Final Considerations
- Collaboration and team work, lessons from rowing
- Balancing innovation and stability, fostering creativity while maintaining structure, brainstorming sessions.
- Case Study: The creation of the Sistine Chapel – preparation and refinement and continuous improvement
Day 2: EFFICIENT MODELLING AND SIMULATION FOR PERFORMANCE OPTIMISATION
Understanding the role of the simulation, the level of complexity required for the models, the importance of regular correlation, simplification, linearisation.
The importance of choosing the right level of complexity and efficient models correlation
- Strategies for modelling and simplifying complex systems
- The role of model correlation in achieving predictive accuracy
- Real time simulation and ‘digital twins’
Structuring Simulation and Models to optimise the performance
- Introduction to frequency analysis and time series analysis
- Linearisation of systems and example of applications
- Simplified and complex models in the optimisations strategies
- Managing uncertainty in the parameterisation, sensitivity studies
- Meta models
- Long term and short term goals
- Simulation Case Studies
Day 3: TESTING; REAL WORLD DATA AND DATA-DRIVEN PERFORMANCE OPTIMISATION
Understanding the limitations of the data sources, extract the best knowledge from the data and understand data-driven performance optimisation
Understanding limitations of sensors and real world data
- Understanding sensor accuracy, limitations, and measurements uncertainties
- Repeatability and statistical analysis
Extracting knowledge from the data and applying data-driven performance optimisation
- Techniques for signal processing and refining raw data
- Structuring data sources architecture for efficient analysis
- Data fusion: integrating multiple data sources for a comprehensive view
- Data analysis, interpretation and the role of models in extracting meaningful insights
- Data driven Performance optimisation
- Case study: The impact of data driven methodologies in high performance sports
WHO SHOULD ATTEND?
Engineers working on optimising the performance of complex systems, such as:
- Systems Engineers
- Systems Analysts
- Simulation Engineers
- Hardware Architects
- Performance Engineers
- Optimisation Engineers
- Automation Engineers