User Stories
These user stories capture typical real-world use cases for fastspa features.
Network centrality metrics
- As a policy analyst, I need to identify bottleneck sectors (high betweenness centrality) so that I can prioritise interventions where disruptions or decarbonisation measures will propagate most effectively.
- As a supply-chain risk analyst, I need network topology summaries (density, in/out strength) so that I can compare how structurally concentrated different target-sector supply chains are.
Uncertainty quantification
- As an academic researcher, I need Monte Carlo uncertainty bounds on sector contributions so that I can report confidence intervals and avoid over-interpreting point estimates.
- As a model developer, I need Latin Hypercube Sampling so that I can achieve stable uncertainty estimates with fewer model runs.
- As a practitioner, I need sensitivity elasticities so that I can understand which input intensities dominate the reported total impact.
Consequential & policy scenario analysis
- As a policy analyst, I need to run perturbation (consequential) scenarios so that I can quantify how proposed interventions change total impacts and upstream hotspots.
Temporal & dynamic analysis
- As a researcher, I need to compare SPA results across multiple years so that I can identify whether supply-chain hotspots are structurally changing over time.
Loop detection & circular economy analysis
- As a circular economy analyst, I need explicit loop detection so that I can identify self-reinforcing cycles and discuss feedback dynamics in system-wide supply chains.
Semantic aggregation
- As an LCA practitioner, I need to aggregate granular IO sectors into stakeholder-friendly groupings so that I can communicate results without exposing proprietary or overly detailed sector classifications.
- As a government reporting team, I need hierarchical aggregation (e.g. division → group → industry) so that I can publish results aligned to standard statistical classifications.