Industry: Engineering Product: Tamara Application: Structural design optimization
An engineering firm faced a structural-design choice on an urban bridge: deliver the architecturally ambitious baseline structure, or an optimised route — a simpler structural form, phased construction and dual-source procurement — that cost slightly more upfront but promised a calmer risk profile. Optimisation only pays if it attacks the activities that genuinely drive the outcome: the ones that are both frequently on the critical path and high-impact when they slip. The first job, then, is to map exactly those.
Rebuilt in Tamara, Vose Software's Monte Carlo project risk tool, with Beta-PERT durations and costs on every activity and six discrete risk events layered on top, that map is a scatter of each activity's criticality index (how often it lands on the critical path) against its cruciality (how strongly its duration moves the finish). The top-right corner is where optimisation effort pays:
Material procurement leads (cruciality 0.53, critical in 83% of runs) — the prime candidate for dual-sourcing — followed by superstructure assembly (0.44, 100% critical) and detailed structural design (0.44, 100% critical). These three are precisely the activities the optimised design targets through a simpler structure, phased assembly and dual-source procurement. Low-cruciality activities such as inspection and commissioning are not worth re-engineering, however visible they are on the bar chart.
The same ranking, drawn as a tornado, confirms the de-risk shortlist by cruciality:
Material procurement dominates (cruciality 0.53), followed by superstructure assembly (0.44) and detailed structural design (0.44). Against the deterministic plan of 920 days, the simulated P50 finish is day 1,072 and the P80 sits 231 days (about 7.6 months) beyond the plan — a 3% chance of meeting the headline date. Optimisation attacks the activities the model identifies as the real drivers, not the ones that are merely visible.
The same simulation places every activity in time as a band, not a bar:
The whiskers widen downstream because uncertainty compounds along the chain: procurement, fabrication and assembly carry the widest whiskers — the same activities the scatter flags — and because they sit in series the spread compounds toward completion.
The optimised design trades a small upfront premium for a markedly tighter tail. Both routes are simulated on the same network, the optimised case re-running with reduced risk probabilities and impacts:
Against a $179M budget, the baseline design carries a mean of $173M, a P90 of $188M and a 30% probability of overrun; the optimised design lands at a $167M mean, a $180M P90 and a 12% overrun probability. Because schedule slip feeds extended general-conditions and urban traffic-management overhead, the optimised design also pulls the P80 finish in by 72 days (1,151 → 1,079 days). The two effects are coupled, and the decision is not about avoiding a breach but about the lower mean and the tighter tail the optimised design buys.
The six discrete events were modelled as Bernoulli risks. Ranking them by expected schedule impact (probability × delay) gives a clean Pareto:
Five of the six events carry ~80% of the expected discrete-event delay — a material-procurement delay (5.1 weeks expected), adverse ground conditions (4.2), a contractor-productivity shortfall (3.5), design revision/rework (3.1) and weather-related disruption (3.1). The optimised design's interventions — dual-sourcing, a simpler form and phased construction — map directly onto the top of this list.
Structural design optimisation is a choice between distributions, not point estimates. Tamara is what lets the firm see the tail it is buying down — and prove the optimised design pays for itself.