Strategic Engineering

Motivation

Urban energy infrastructure often faces conflicting design pressures: economies of scale promote large centralized facilities, while uncertainty and financial constraints encourage phased, smaller deployments. This case study, motivated by Singapore’s waste management challenges, investigates how design flexibility can improve lifecycle economics when both economies of scale and the time-value of money are significant design factors

Methodologies

• Simulation-Based Real Options Analysis: Quantified flexibility value (VOF) for expanding modular capacity under uncertainty.
• Dynamic Capacity Planning Models: Incorporated decision rules to guide expansion timing and location.
• Monte Carlo Simulation: Generated 2,000 stochastic scenarios of waste generation using geometric Brownian motion.
• Sensitivity Analysis: Tested how economies of scale and discount rate interact to affect value.
• Decision Rule Optimization: Used full-factorial enumeration to identify optimal expansion triggers

Insights

• Flexibility Pays Off: Introducing modular expansion increased expected net present value (ENPV) by over 30% compared to rigid centralized systems.
• Decentralized Advantage: Flexible decentralized configurations outperformed centralized ones, improving ENPV from S$22M to S$29M.
• Reduced Downside Risk: Downside exposure dropped sharply (5% NPV > S$23M vs. S$12M for rigid design).
• Economic Drivers: Weak economies of scale and higher discount rates increase the benefit of flexible, modular systems.
• Practical Outcome: Results guide policymakers in designing urban waste systems that evolve with demand rather than locking into static capacity.

Training

Relevant lectures and skills:

• Real Options Analysis
• Monte Carlo Simulation
• Scenario Planning
• Sensitivity Analysis
• Dynamic Programming

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