Salish Ops

Operational Intelligence

Predictive and diagnostic layers computed from the archive: what is about to go wrong, what it costs, and what an operator could do about it. Everything on this page is modelled from observed data and labelled with its method — days of archive so far, so confidence grows daily. BC Ferries · published AIS · observed

Operations health score

/100

Executive brief rule-generated

Cascading delay predictor live

Knowing which sailings will inherit a delay means passengers can be warned before they leave home, not at the berth.

Passenger delay cost estimated

The budget line that justifies (or kills) any operational fix.

Recommendations rule engine

Hidden patterns auto-detected

Where delays come from

Fix the biggest bar first — attacking a 7% cause can't move the network.

Vessel performance profiles published AIS

VesselClassnOn-time Avg delayRecovery/legMedian turn Windy ΔOTP

Vessels that add delay every leg need slack scheduled around them; vessels that recover can absorb tight turns.

Terminal performance published AIS

TerminalDeps w/ actualsOn-time dep Med dep delayTurn medTurn p90 RecoveryQueue %/h

Terminals that absorb inbound lateness are the network's shock absorbers — the ones that don't are where berth investment buys the most reliability.

Experience × Operations X meets O Reddit · public

DayPostsSentiment NegativeOn-timeSellouts Gust

Operational metrics say what happened; riders say what it felt like. When these two disagree, the gap is the story.

Scenario simulator first-order model

Turns "we should add a sailing" from an opinion into a number someone can challenge.