Every forecast, every plan, every optimisation Oritiq or any other system produces rests on the same foundation: the master data. And master data is rarely clean. Item codes are duplicated across systems, so the same SKU is planned three different ways. Lead times were set three years ago and never updated. BOMs don’t reflect the product actually being built. Supplier records carry missing or conflicting information.
The dangerous part is that bad master data doesn’t announce itself. The planning system runs, the numbers come out, and they look authoritative. Nobody sees that the lead time driving the safety stock is stale, or that two item codes are quietly double-counting the same demand. The output is precise, confident, and wrong.
Garbage in. Confident wrong answers out.
Most organisations treat master data as a one-time cleanup project: scrub it, load it, move on. But master data drifts continuously. New items are created without discipline, lead times shift, suppliers change terms. Within months, the cleaned data is dirty again. Treating it as a project rather than a live asset means the corruption always comes back, and every downstream decision inherits it.
The same SKU exists under multiple codes across systems. Demand is double-counted, inventory is fragmented, and planning treats one product as several.
Lead times set years ago and never revisited still drive today's safety stock and replenishment. The buffer protects a reality that no longer exists.
The bill of materials doesn't reflect what's actually being built. Material planning is accurate to a product that shipped two revisions ago.
Supplier master data carries missing, duplicated, or contradictory information. Procurement acts on records that don't agree with each other.
Master data is scrubbed once, then drifts again. Treated as a project rather than a live asset, the corruption always returns.
A confident answer built on corrupt data is more dangerous than no answer at all.
Oritiq's supply chain master data management treats data quality as a continuous discipline, not a one-time project. It surfaces drift as it happens, reconciles duplicates into a single trusted record, and governs the data that every other Oritiq capability depends on, so the decisions built on it can be trusted.
Master data doesn't fail all at once; it drifts. Oritiq monitors the master data continuously and surfaces the drift as it happens: a lead time that's grown stale, an item created outside standard, a BOM diverging from the product. Data quality becomes an ongoing signal rather than a periodic audit.
Issues are flagged with their downstream consequence attached (this stale lead time is inflating safety stock on these SKUs) so data stewards fix what matters most, not just what's easiest to find.
The same item, supplier, or location living under several records is the most corrosive master data problem: it fragments demand, splits inventory, and double-counts everything. Oritiq detects duplicate and near-duplicate records across systems and reconciles them into a single golden record: one trusted version, mapped back to every source.
Deduplication isn't a one-off merge. As new records are created, the engine catches the duplicates as they form, so the golden record stays golden instead of re-fragmenting the moment the cleanup ends.
A lead time set three years ago and never revisited is one of the most damaging master data errors: it silently drives the wrong safety stock and replenishment on every SKU it touches. Oritiq's AI continuously monitors actual lead-time performance against what the master data claims, detects the drift, and updates the master to reflect reality.
Instead of a static field nobody maintains, lead time becomes a living value the system keeps current, learning from actual supplier and internal performance, so the plans built on it are calibrated to how the operation runs today, not how it ran years ago.
Clean data stays clean only if someone owns it. Oritiq builds master data governance into the workflow: clear ownership by domain, approval workflows for new and changed records, and validation rules that stop bad data at the point of entry rather than catching it downstream.
Every change is auditable: who created or modified a record, when, and why. Master data governance becomes a living discipline embedded in the operation, so the data quality earned in cleanup is sustained by design, not by heroics.
Surfaces master data drift as it happens (stale lead times, non-standard items, diverging BOMs) with each issue flagged alongside its downstream consequence so stewards fix what matters most.
Detects duplicate and near-duplicate records across systems and reconciles them into a single golden record, mapped back to every source, and catches new duplicates as they form.
Continuously monitors actual lead-time performance against the master, detects drift, and updates the value, so lead time is a living field calibrated to how the operation runs today, not years ago.
Clear domain ownership, approval workflows for new and changed records, and validation rules that stop bad data at entry rather than catching it downstream.
Purpose-built for the master data domains that drive supply chain decisions (supplier master data, item master, BOM, and location) reconciled and governed as one.
Master data as a live asset: clean by monitoring, not by one-time cleanup.
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