Industry: Transportation
Product: Tamara
Application: Fleet maintenance optimization

A 5,000-Vehicle Overhaul Planned for May 2028. The Simulation Gives That Date a 1% Chance

A global logistics operator launched a fleet-wide mid-life overhaul programme — 5,000 vehicles cycled through depots in waves: condition assessment, teardown, parts, rebuild, recommissioning, hand-back. Scheduled the orthodox way, with most-likely durations rolled through the network, it showed a 670-day, May 2028 finish. Every vehicle off the road bleeds revenue, so the date mattered. It was also the answer to a question no fleet manager should ask — what if every long-lead part arrives on its most-likely date and every depot wave runs clean?

Rebuilt in Tamara, Vose Software's Monte Carlo project risk tool, with each stage carrying a Beta-PERT duration and six discrete risks layered on, the date dissolves into a distribution: P50 November 2028, P90 March 2029, and a probability of finishing by the published May 2028 plan of just 1%. The S-curve puts the deterministic plan at the very foot of the curve.

The gap between the May 2028 plan and the P80 finish is 279 days — roughly 9.2 months. That gap is the programme's true contingency, and it lives in the parts pipeline.

Why a Single Critical-Path Date Fails

A deterministic roll-up adds most-likely durations along one assumed chain. Durations are right-skewed — a stage can finish a little early but overrun a lot — so the mean simulated finish is 868 days against the 670-day plan. And with two depot waves drawing on a shared parts pool, whichever wave waits longest for parts in a given iteration drives the merge into recommissioning.

Where the Duration Risk Actually Lives

Tamara ranks each stage by cruciality — the rank-correlation between its duration and the programme finish.

Long-lead parts procurement dominates (cruciality 0.64), well ahead of the engine & drivetrain rebuild (0.51) and wave-A teardown (0.29). The message is unambiguous: this is a supply-chain programme wearing a maintenance schedule. Buying certainty on parts lead-time — dual-sourcing, framework contracts, forward ordering — moves the finish more than any improvement to depot throughput.

The same simulation places every stage in time as a band, not a bar:

The whiskers widen downstream because uncertainty compounds: recommissioning inherits the spread of teardown, rebuild and refit, so hand-back carries the accumulated variability of the whole programme.

The Discrete Risks That Drive the Tail

Six discrete events were modelled as Bernoulli risks. Ranking them by expected schedule impact (probability × delay) gives a clean Pareto:

Four of the six events carry ~80% of the expected discrete-event delay — a spare-part lead-time blowout (7.1 weeks expected), a technician/skill shortage (4.5), hidden corrosion driving extra rework (3.3) and a telematics supplier slip (2.4). The single largest, by a wide margin, strikes the parts pipeline — echoing the tornado.

Schedule Risk Is Cost Risk

Every day the programme runs long keeps vehicles off the road, so downtime cost rides directly on the schedule distribution. Tamara let the team price a mitigation package together — predictive-maintenance triage, a dual-sourced parts framework and a technician training/agency surge — and compare before/after on one axis:

Without mitigation the programme cost, including downtime, runs to a mean of $109M and a P90 of $127M, with a 21% probability of breaching the $120M budget. The mitigation package cuts the P80 finish by 131 days (949 → 818 days), and because downtime drives cost, the overrun probability falls from 21% to 1% — the package pays for itself many times over in avoided fleet downtime.

What Tamara Changed

• The hand-back commitment moved from the 1%-likely May 2028 date to a risk-informed P80, so depots and customers planned against a real date.
• A 9.2-month contingency was quantified and funded, rather than discovered one late parts shipment at a time.
• Risk-reduction effort was redirected to the parts pipeline — cruciality 0.64 — instead of being spread across depot throughput.
• A dual-sourcing and predictive-triage package was approved on its tail-clipping effect, cutting budget-overrun probability from 21% to 1%.

Tamara Functionality Used

• Monte Carlo schedule simulation over the overhaul network, with Beta-PERT durations and parallel-wave merge logic.
• Discrete risk-event modelling (Bernoulli occurrence × Triangular impact) for parts, labour and rework risks.
• Criticality and cruciality analysis naming parts lead-time as the governing driver.
• Stochastic Gantt and cumulative S-curve for communicating schedule uncertainty to fleet stakeholders.
• Integrated cost–schedule modelling linking programme duration to fleet-downtime cost.
• Scenario comparison quantifying the mitigation package's before/after impact on both the P80 finish and the overrun probability.

A fleet overhaul is not a date; it is a distribution governed by the part you are waiting on. Tamara is what turns "when is the fleet back?" into a probability operations and finance can both sign.