Module 1
Prescriptive Decision Framing
- From prediction to recommendation
- Decision variables, objectives and constraints
- Feasibility, optimality and business rules
- Model scope, assumptions and decision boundaries
Advanced and Strategic
Prescriptive Analytics, Optimisation and Simulation
Use optimisation, simulation and trade-off analysis to recommend actions and improve decisions under constraints and uncertainty.
Course overview
Move from predicting what may happen to evaluating what should be done.
Prescriptive Analytics, Optimisation and Simulation develops the modelling logic required to compare actions when resources are limited, objectives compete and outcomes are uncertain.
Participants translate business choices into decision variables, objectives and constraints; explore optimisation approaches; use simulation to understand variability; and evaluate trade-offs and robustness before recommending action.
The course focuses on model formulation, interpretation and decision quality rather than mathematical sophistication for its own sake.
Learning outcomes
What participants will be able to do.
- Identify decisions suitable for prescriptive analytics.
- Formulate objective functions, decision variables and constraints.
- Build and interpret linear optimisation models.
- Recognise integer, binary and multi-objective decision structures.
- Use Monte Carlo simulation to represent uncertain inputs and outcomes.
- Explain discrete-event simulation concepts for operational systems.
- Conduct trade-off, scenario and sensitivity analysis.
- Translate model outputs into implementable recommendations with caveats.
Who should attend
Designed for professionals who use data, analysis or evidence to improve decisions.
- Advanced business and data analysts.
- Operations research and planning professionals.
- Supply-chain, logistics and workforce planners.
- Finance, portfolio and resource-allocation teams.
- Managers sponsoring optimisation or simulation initiatives.
Prerequisites
Professionally relevant and application-focused.
Participants should have solid analytical literacy and be comfortable with spreadsheets or analytical models. Prior optimisation experience is not required.
Course structure
A five-Module journey from analytical understanding to workplace application.
The sequence may be delivered across five days or adapted to another approved format while preserving the learning outcomes and the five connected Modules.
Module 2
Linear and Resource-Allocation Optimisation
- Linear-programming formulation
- Capacity, demand and balance constraints
- Resource allocation and mix decisions
- Interpreting optimal solutions and shadow-value concepts
Module 3
Integer, Binary and Multi-Criteria Decisions
- Yes/no and discrete decisions
- Scheduling and selection structures
- Multiple objectives and trade-offs
- Scenario constraints and policy rules
Module 4
Simulation for Uncertainty and Operations
- Monte Carlo simulation logic
- Input distributions and dependency awareness
- Output distributions and risk measures
- Discrete-event simulation concepts for process systems
Module 5
Robust Decisions, Trade-Offs and Implementation
- Sensitivity and scenario analysis
- Robustness and model risk
- Stakeholder constraints and implementation realities
- Integrated prescriptive-analytics case and recommendation
Learning experience
Move from understanding to application, production and workplace value.
Understand
Connect concepts with business decisions
Clarify methods, assumptions, evidence requirements and the decision context before applying tools.
Apply
Work through realistic analytical cases
Use datasets, scenarios and decision questions to practise analytical judgement in context.
Produce
Create practical analytical outputs
Develop artefacts that can be adapted to reporting, modelling, governance or decision-support work.
Review
Challenge evidence and analytical choices
Use peer review, validation criteria and facilitated critique to improve analytical reasoning.
Transfer
Apply the learning at work
Identify how to adapt the methods to organisational data, decisions, systems and governance requirements.
Completion requirements
Demonstrate participation, application and professional judgement.
- Participate actively in case discussions, data exercises and analytical workshops.
- Complete the principal analytical or decision-support outputs assigned during the programme.
- Contribute to the integrated case, model, dashboard or application workshop.
- Complete knowledge checks and a workplace application or study plan.
Participant outputs
Leave with practical analytical artefacts.
- Prescriptive decision formulation.
- Optimisation model and constraint map.
- Resource-allocation analysis.
- Simulation model specification.
- Trade-off and robustness assessment.
- Management recommendation brief.
Delivery options
Select the learning format that fits your people and analytical environment.
Instructor-led
Live Classroom
Facilitated face-to-face learning with analytical cases, datasets, modelling tasks, discussion and immediate feedback.
Instructor-led
Live Virtual Classroom
Interactive online delivery using collaborative workspaces, data exercises, breakout analysis and guided model development.
Flexible
Blended Learning
A structured combination of preparation, live facilitation, applied assignments, analytical work and follow-up application.
Organisation-specific
Corporate and In-Company
Tailored delivery aligned with organisational datasets, measures, tools, governance, decisions and analytics maturity where appropriate.
Frequently asked questions
Course information and participation.
Is advanced mathematics required?
Participants need analytical confidence, but the course focuses on formulation and interpretation. Mathematical detail is introduced only to support correct modelling decisions.
Which optimisation or simulation tools are used?
Tool choice can vary by delivery environment. The transferable focus is on problem formulation, model logic, validation and decision interpretation.
Does the course include Monte Carlo simulation?
Yes. Monte Carlo simulation is used to explore uncertain inputs and outcome distributions.
Can we use our resource-allocation problem?
Yes. Corporate delivery can incorporate an approved planning, capacity, portfolio or operational optimisation case.
Does completion provide professional certification?
The course develops prescriptive-analytics, optimisation and simulation capability and may provide an INDENTRA course-completion record where applicable. It is not a third-party certification.
Take the next step
Improve resource-allocation decisions with explicit constraints and evidence.
Discuss an advanced analytics programme aligned with your organisation’s planning, capacity, portfolio or optimisation decisions.