A rush freight charge shows up in logistics; its cause lives in procurement. A stockout's cost lands in three departments that will never blame inventory. Case-based tools miss this by construction, because the case was never a single id — the case is a graph, and the object-centric log is what keeps it.
Where the method has already drawn blood: stockouts whose true cost reprices ×3.4 once warehouse handling, redeliveries, and expedites are attributed home; fragmentation exposed as symptom rather than cause; collections drag and delivery failure, single-counted on one ledger.
Maverick buying, invoices arriving before their purchase orders, payment blocks set and quietly released — the patterns the method grew up on, verified against the OCEL standard's own published example before a single customer log.
Late delivery against the promised date, cancellations, and fraud holds — validated against real public commerce data where over half of all shipments run late, so the detectors meet mess before they meet you.
You don't have to believe a leakage thesis to want object intelligence: your operational data on the open OCEL 2.x standard, one confirmed mapping, every serialization out, no pipeline code to own — and no lock-in to apologize for later.
Objective intelligence, taken literally: every figure ships as a floor, not an estimate — the portion provable from process data alone, with its population and both confidence bands published. The disagreement is never about whether events happened, only about what a pick costs, and you own that input.
Forty bots and no one can say what they saved? Rank the roadmap by recovered dollars instead of by whoever lobbied hardest — and re-rank it as the data moves, with before-and-after measurement on every intervention.
Drop a table export into the free studio and look at your own seams. No account, nothing uploaded, and an honest first number before anyone talks to a salesperson.
Free · no account · nothing uploaded
