Define the decision
Clarify objectives, stakeholders and success criteria.
A process can work technically and still be the wrong investment. Demand, operating costs and deployment timing can change the answer.
My work with the Strategic Engineering Laboratory at Imperial connects resource engineering to decisions under uncertainty. In the ESA-supported space resources value-chain programme, I work on modelling the business case for lunar resource systems: connecting technical choices with costs, markets and deployment strategies.
A fixed plant commits its full capacity upfront. A staged plant starts smaller and expands if demand justifies it.
NET RETURN / ARBITRARY UNITS · VERTICAL LINE = ZERO
Demand d ranges from 0 to 100. Fixed: capacity 100, upfront cost 65, return = d − 65. Staged: initial capacity 40 at cost 30; optional expansion adds capacity 60 at cost 45. Staged return = max(min(d,40) − 30, d − 75): expansion is chosen only above demand 85. All values are arbitrary units. The chart shows net return, including negative values. No probabilities, discounting or time-to-expand are modelled.
A deliberately simple teaching example, not an ESA model, forecast, optimisation or investment recommendation. Adaptability can limit downside, but it has a cost.
Clarify objectives, stakeholders and success criteria.
Identify plausible ranges and the drivers that matter.
Link the technology, operations and economics.
Consider what to commit, stage or defer.
Explore alternatives across multiple scenarios.
Assess whether flexibility earns its extra cost.
Make risk and reward explicit for decision-makers.
A concise adaptation of the SEL method. Explore the ESA value-chain programme ↗