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Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in Scenarios, Trade-offs and Sensitivity Analysis

Online postgraduate diploma of 800 hours in scenarios, trade-offs and sensitivity analysis. 8 modules covering multi-criteria decision structuring, scenario planning, AHP, TOPSIS and PROMETHEE, multi-objective optimisation, local and global sensitivity analysis and robust decision making in line with ISO 31000:2018, IEC 31010:2019 and ISO 15288:2023, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Structure technical decision problems with conflicting criteria.

02

Build consistent exploratory scenarios.

03

Apply multi-criteria methods such as AHP, TOPSIS and PROMETHEE.

04

Obtain and interpret Pareto fronts in multi-objective optimisation.

05

Perform local sensitivity analysis to identify thresholds.

06

Apply global sensitivity analysis with Sobol indices.

07

Communicate robust decisions to management and stakeholders.

Profile

Who it's for

  • Decision analysts.
  • Systems engineers balancing cost, performance and risk in design choices.
  • Technical strategy consultants.
  • Energy planners.
  • Project engineers.
  • Risk analysts.
  • Engineering or science graduates seeking to enter the energy sector.
Standards & tools

What you'll work with

  • ISO 31000:2018 (Risk management)
  • IEC 31010:2019 (Risk assessment techniques)
  • ISO 15288:2023 (System life cycle processes)
  • ISO 14040:2006 (Life cycle assessment: principles)
  • ISO 50001:2018 (Energy management systems)
  • JCGM 101:2008 (Propagation of distributions using a Monte Carlo method)
  • Python with SALib
  • pymoo
  • EMA Workbench
  • Expert Choice
  • Microsoft Excel with @RISK
Syllabus

8 modules

  1. 1.1Structure of a decision problem
  2. 1.2Alternatives, criteria and attributes
  3. 1.3Conflicting objectives
  4. 1.4Dominance and the Pareto frontier
  5. 1.5Decision-maker preferences
  6. 1.6Individual and group decisions
  7. 1.7Cognitive biases in decision-making
  8. 1.8Documenting the decision logic
  9. 1.9Cases in engineering design
  10. 1.10Workshop: structuring an investment decision
  1. 2.1Exploratory and normative scenarios
  2. 2.2Identifying drivers and uncertainties
  3. 2.3Shell axes method
  4. 2.4Morphological matrix analysis
  5. 2.5Scenario consistency
  6. 2.6Narratives and quantification
  7. 2.7Scenarios in energy and climate
  8. 2.8Early signals and indicators
  9. 2.9Adaptive planning through decision pathways
  10. 2.10Practical exercise: four scenarios for an industrial plant
  1. 3.1Weighted sum and its limits
  2. 3.2Analytic hierarchy process (AHP)
  3. 3.3TOPSIS distance-to-ideal method
  4. 3.4Outranking methods: ELECTRE and PROMETHEE
  5. 3.5Multi-attribute utility theory (MAUT)
  6. 3.6Criteria normalisation
  7. 3.7Eliciting weights and swing weighting
  8. 3.8Consistency of judgements
  9. 3.9Decision support software
  10. 3.10Lab: AHP and TOPSIS in Python
  1. 4.1Weighted sum and epsilon-constraint methods
  2. 4.2NSGA-II and NSGA-III evolutionary algorithms
  3. 4.3Quality indicators: hypervolume
  4. 4.4Visualising Pareto fronts
  5. 4.5Parallel coordinates and scatter plots
  6. 4.6Selecting solutions on the front
  7. 4.7Marginal rates of substitution
  8. 4.8Trade-offs in product design
  9. 4.9Trade-offs in system operation
  10. 4.10Practical exercise: cost-emissions Pareto front with pymoo
  1. 5.1Derivatives and elasticities
  2. 5.2One-at-a-time (OAT) analysis
  3. 5.3Tornado diagrams
  4. 5.4Break-even analysis
  5. 5.5Weight sensitivity in multi-criteria analysis
  6. 5.6Ranking stability
  7. 5.7Decision threshold analysis
  8. 5.8Limitations of the local approach
  9. 5.9Communicating sensitivity results
  10. 5.10Lab: tornado diagram for a cost model
  1. 6.1Morris method
  2. 6.2First-order and total Sobol indices
  3. 6.3Saltelli sampling
  4. 6.4FAST and eFAST
  5. 6.5Moment-based and density-based methods
  6. 6.6Metamodels for sensitivity
  7. 6.7Factor prioritisation and factor fixing
  8. 6.8Computing indices with SALib
  9. 6.9Interpreting interactions
  10. 6.10Practical exercise: Sobol indices for an energy model
  1. 7.1Robust decision making (RDM)
  2. 7.2Scenario discovery with PRIM
  3. 7.3Regret analysis
  4. 7.4Decision matrices under uncertainty
  5. 7.5Visualisation for management
  6. 7.6Participatory workshops with stakeholders
  7. 7.7Traceability of assumptions
  8. 7.8Independent review of the analysis
  9. 7.9Lessons from real cases
  10. 7.10Case study: robust capacity expansion decision
  1. 8.1Problem and stakeholders
  2. 8.2Building the case scenarios
  3. 8.3Criteria and alternatives
  4. 8.4Evaluation model
  5. 8.5Trade-off analysis
  6. 8.6Decision sensitivity analysis
  7. 8.7Robust decision recommendation
  8. 8.8Communication plan
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with SALib, pymoo, EMA Workbench, Expert Choice and Microsoft Excel with @RISK, in free, academic or evaluation versions. Practical work is based on real procedures for building scenarios through expert workshops, global sensitivity analysis of technical models and exploring Pareto fronts for design decisions. Assessment is continuous by module plus the final project.

Standards covered: ISO 31000:2018, IEC 31010:2019, ISO 15288:2023, ISO 14040:2006, ISO 50001:2018, JCGM 101:2008.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 31000:2018, IEC 31010:2019 and ISO 15288:2023 applied to real cases, not as a theoretical annex.
Industry software:Python with SALib, pymoo, EMA Workbench, Expert Choice and Microsoft Excel with @RISK, in free, academic or evaluation versions.
Final project:a complete decision analysis of a real technical problem, from scenarios and criteria to trade-offs, sensitivity analysis and a robust recommendation, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Problem and scenarios
Define the decision problem and stakeholders and build consistent scenarios for the case.
Criteria and evaluation model
Set criteria and alternatives and build a multi-criteria evaluation model.
Trade-off analysis
Generate and explore the Pareto front of the competing objectives.
Sensitivity analysis
Test the decision with local and global sensitivity analysis.
Final project
Present a robust recommendation and communication plan, reviewed by your tutor.
Career opportunities

Where this takes you

Decision analystSystems engineerTechnical strategy consultantEnergy plannerProject engineerRisk analyst
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python with SALib, pymoo, EMA Workbench, Expert Choice and Microsoft Excel with @RISK, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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