In the domain of space launch vehicles, three alternatives emerge: reusable, partially reusable, and expendable rocket designs. This analysis investigates their economic performance across three stages: first, with predetermined assumptions; second, navigating uncertainty factors like development time and demand dynamics; and finally, evaluating their flexibility to determine the best investment.

The Viability of Space Payload Transportation

In the dynamic realm of space exploration and commercial viability, a critical question looms: Is the substantial initial investment in high-complexity systems justified for businesses? This inquiry intersects with the risk and uncertainty inherent in creating reusable space vehicles, prompting a meticulous evaluation of cost-benefit dynamics.

The space industry is witnessing rapid development, with commercial launchers offering rides to orbit for various payloads. While some focus on small, expendable rockets like Astra and Rocketlab, others, like SpaceX, pursue ambitious fully reusable spacecraft. Current trends show a surge in payload traffic to orbit driven by commercial needs, alongside a gradual decline in the cost-to-orbit trajectory. However, historical lessons, such as the challenges faced by the Space Transportation System (STS), underscore the complexities of achieving semi-reusability.

Looking ahead, proposals for fully reusable systems by companies like Relativity Space and SpaceX signal a potential paradigm shift. The allure of fully reusable "heavy lift" rockets holds promise, with emerging sectors such as earth imaging and satellite servicing driving demand for LEO/GEO launches. As the industry navigates these shifts, the quest for efficiency and sustainability takes center stage, shaping the trajectory of space exploration and commerce.

Related Works

Looking back at studies like "The Space Shuttle Decision: NASA’s Search for a Reusable Space Vehicle" and "Economic Model of Reusable vs. Expendable Launch Vehicles" gives us a solid foundation for understanding how to build cost-effective rockets ready for space travel. Exploring space isn't just about overcoming physical barriers—it's also about navigating financial ones.

Economic analysis becomes crucial in this endeavor. By tackling uncertainties with a "probabilistic" mindset, as discussed in "An Overview of Methods to Evaluate Uncertainty of Deterministic Models in Decision Support," and embracing flexibility in design, as highlighted in "Flexibility in System Design and Implications for Aerospace Systems," we're better equipped to tackle the challenges of building launch systems. Now, fast forward to today, where US-based commercial launch providers are gearing up to sling satellites into Low Earth Orbit (LEO) and Geostationary Orbit (GEO), capitalizing on a booming demand for space cargo.

A pragmatic SWOT analysis helps us size up the industry, pinpointing strengths, weaknesses, opportunities, and threats. And when it comes to picking the right rocket for the job, we've got options: go expendable, semi-reusable, or aim for fully reusable vehicles like the Starship. These alternatives offer different price tags, challenges, and potential payoffs. As we strive for innovation and affordability in equal measure, it's clear: each launch is a step toward unlocking the mysteries of the cosmos.

Methodology

Preliminary Market Analysis

The analysis considers costs, compound annual growth rate (CAGR) of demand, Minimum Acceptable Rate of Return (MARR), and delivery destinations for three SpaceX rockets: Falcon 1, Falcon 9, and Starship-Superheavy. Costs vary based on reusability and payload capacity. CAGR of demand for space-launch is conservatively estimated at 5.3%. MARR is set at 20% due to high risk. Revenue primarily comes from delivering objects to Low Earth Orbit (LEO) and Geostationary Orbit (GEO), with other potential revenue streams being marginal.

Evaluation Approaches and DCF

The deterministic business case involves initial capital expenditures (CAPEX) for land, research and development (R&D), and factory construction, treated as setup costs at present value within the model, with a straightforward conversion between present and annual worth. During R&D and construction, no launches take place. Operating costs include expenses for new ships, launch operations, and refurbishment, assuming a one-year construction cycle for new ships based on demand and/or lifecycle considerations, and following refurbishment cycles per ship, considering their reusability. Salvage value (SV) is calculated as a fraction of unrealized flights within the lifecycle of all operating ships, scaled by production cost, using a linear depreciation model, and discounted by the Minimum Acceptable Rate of Return (MARR). Cash flow is evaluated by deducting fixed and operating costs from revenues generated annually, with Discounted Cash Flow (DCF) obtained by discounting each value by the MARR.

Uncertainty Characterism and Modelling

Sources of uncertainty are consolidated into a single uncertainty model for alternative comparative analysis. Firstly, Year 1 demand uncertainty, given the short horizon, is expected to be minor and not sensitive to noise distribution type, addressed by multiplying the projected demand by a randomized factor. Secondly, uncertainty in the 24-year demand projection, with an expectation of upside surprises due to industry growth, is modelled using a right-skewed triangular distribution, with the average determined by the deterministic projection. Thirdly, growth rate uncertainty, reflecting fluctuations in demand due to economic conditions, etc., is addressed by a uniform distribution of deviation from the growth rate.

Destination uncertainty, accounting for fluctuations in demand composition, is treated as separable from overall demand uncertainty, modelled as noise in the ratio of demand between LEO/GEO destinations with an independent uniform random distribution. Lastly, R&D time horizon uncertainty, acknowledging variations in research into unknowns, is modelled with a binomial distribution centered around the deterministic R&D duration, generating binomial random numbers with a deviation of ±1 year from the projected horizon.

Flexibility

Since the main uncertainty is related to demand, the flexibility study will be centred around satisfying the demand dynamically in order to increase NPV. This can be achieved by building new ships on the basis of the most recent known demand evaluation. The approach can work differently for alternative scenarios, hence specific applications are described separately.

DCF Analysis

The DCF analysis evaluates three design alternatives. The Expendable option offers the lowest initial costs with no refurbishment cycle or salvage value. Based on Falcon 1 parameters, it yields a NPV of $135 million and an IRR of 23.61%. The payback period is 5 years without accounting for the time value of money and 13 years with it. The Semi-Reusable choice involves higher initial costs and a longer R&D period. The NPV is $28 million with an IRR of 20.26%. The discounted payback time is 28 years compared to 8 years without discounting. The Reusable option requires extensive R&D but promises increased revenues and reduced operating costs. It involves a complex calculation considering refurbishment and lifecycle schedules. The optimal number of units in the fleet is around 2 to 3. All alternatives prove economically viable, with the Expendable option displaying the best NPV and IRR. Payback time is strongly influenced by R&D duration, and excluding R&D time, the Reusable option shows the fastest capital recovery.

Uncertainty Analysis

The Uncertainty Analysis delves into the three alternatives under uncertainty. For the Expendable alternative, simulations under uncertainty, including demand and R&D time horizon variations, were conducted. Results from Monte Carlo simulation reveal a substantial portion of simulations yielding negative NPV outcomes, emphasizing the limitations of deterministic analysis for economic decisions. Despite higher values for certain metrics, such as value at gain and maximum value, the uncertainty underscores the necessity for flexible design approaches to mitigate downside risk. The Semi-Reusable alternative faces significant disruptions due to uncertainties introduced in demand fraction and development time, resulting in an NPV histogram skewed towards negative values, indicating unfavorable outcomes. Meanwhile, for the Reusable alternative, uncertainties in demand projection and R&D time are modelled, with a Monte Carlo simulation demonstrating a right-skewed NPV distribution. Although the comparison with deterministic NPV results is not entirely favorable, the reusable option remains profitable. However, the presence of notable risk, driven by the selected uncertainty parameters, highlights the possibility of failure despite potential returns. In summary, uncertainty impacts all alternatives adversely, with the Reusable alternative showing relatively better resilience due to its cost efficiency and discounted sensitivity to future demand fluctuations.

Flexibility Consideration & Analysis

The Flexibility Analysis encompasses three design alternatives. In the Expendable Flexible alternative, adaptability to demand is introduced, with production adjusting annually to meet current demand levels. This flexible approach significantly improves economic potential by eliminating much of the downside risk observed in the non-flexible design. Additionally, starting production from zero and scaling up gradually to match demand enhances cost efficiency, leading to reduced CAPEX. Similarly, in the Semi-Reusable Flexible alternative, flexibility primarily around demand size shows a notably positive impact, mitigating the most negative outcomes while not entirely eliminating them. The flexible approach markedly reduces downside risk compared to the non-flexible scenario, resulting in a substantially greater mean NPV and value at gain. Lastly, in the Reusable Flexible alternative, flexibility in production based on previous year demand is implemented, gradually increasing fleet size to accommodate fluctuations. Although the flexible approach doesn't show significant improvement against the deterministic scenario due to high MARR discounting delayed benefits, uncertainty statistics notably improve. The mean NPV increases by 130%, underlining the importance of flexibility in maximizing economic outcomes across all design alternatives.

Results Discussion

Table 1 presents the economic and statistical evaluation of all alternatives across different scenarios. The Expendable alternative demonstrates the best results under deterministic conditions but proves to be highly sensitive to uncertainty, which it can effectively address by adjusting to current demand levels. Flexibility enhances the performance of all alternatives, with a more pronounced effect observed for shorter research horizons due to discounting. The Reusable alternative holds the largest potential for gains, yet its benefits are heavily discounted by MARR due to long R&D time. However, it exhibits good resilience towards uncertainty owing to lower operating costs and high payload, indicating inherent flexibility. Similarly, the Semi-Reusable alternative shares this flexibility and benefits from shorter R&D time, although it faces higher operational costs due to frequent refurbishments. In a flexible scenario, it outperforms the Reusable alternative but cannot reach the level of the Expendable alternative.

In conclusion, after analyzing three investment opportunities in space launch systems under uncertainty and flexibility, it's evident that quick turnaround is vital at high risk levels, as the time value of potential returns diminishes during the developmental period. The level of risk associated with creating reusable vehicles may not be attractive for investors, explaining the saturation of the private launch market with small, expendable launchers. Future improvements to the model could include acquiring more accurate parameter values, considering cost variability and price volatility, and exploring additional flexibility cases prioritizing lucrative deliveries and other revenue streams.