Supply Chain solutions
Anomaly Detection for Supply Chain
Whether you're modernising a legacy data stack or building greenfield, Beryl Analytics's anomaly detection practice gives supply chain operators the same calibre of analytics engineering you'd find in the world's top product companies.
Why supply chain teams choose Beryl Analytics for anomaly detection
- Deep-domain models. Every anomaly detection model we build is tuned to the realities of supply chain operators — not the synthetic benchmarks you see in vendor pitches.
- Production-ready, not throwaway. We ship pipelines, monitoring, alerting, and runbooks — the boring stuff that decides whether the system survives contact with reality.
- Operator-first design. Insights live inside the tools your team already uses, with thresholds and ownership matched to how decisions actually get made.
- Governance built in. Lineage, explainability, and access controls aren't an afterthought — they're scoped from day one and signed off with your security team.
- Outcomes measured in dollars. We track impact in revenue, cost avoided, or risk reduced — never in dashboard counts.
How we deliver anomaly detection engagements
- 01
Data audit (week 1)
A focused review of what data you have, where it lives, and what shape it's in. Outputs a written read with the gotchas and where to start.
- 02
Contract & instrument (weeks 2-3)
We formalise the inputs the system will depend on — schemas, freshness SLAs, ownership — and instrument anything missing. No model without solid inputs.
- 03
Model + interface (weeks 4-7)
The model itself plus the surface your operators will actually use. Built together so the analysts who debug it know exactly what each output means.
- 04
Soft launch & calibration (weeks 8-10)
Live in a small slice of the business. We watch every decision the system informs, calibrate, and only then expand.
- 05
Full rollout
Scale to the full surface area with documentation, training, and an on-call playbook your team owns end-to-end.
Frequently asked questions about Anomaly Detection for Supply Chain
How long does a typical Anomaly Detection engagement take for a supply chain business?
Most anomaly detection projects for supply chain operators land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger supply chain programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Anomaly Detection project in supply chain?
Minimum viable inputs are 12-18 months of historical transactional or operational data, basic entity reference tables, and access to the systems that will consume the output. We can work with messy data — cleaning is part of the engagement.
Can Beryl Analytics integrate anomaly detection with our existing supply chain operators systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of supply chain-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Anomaly Detection engagement?
Before we model anything, we agree the business decision the output will change and the dollar metric we're targeting — revenue lifted, cost avoided, or risk reduced. Anomaly Detection engagements in supply chain typically return 4-12x within the first year.
Do you work with supply chain businesses outside major NZ and AU cities?
Yes. We deliver remotely across New Zealand and Australia and visit on-site for discovery, key workshops, and go-live. Distance is not a blocker — many of our highest-impact anomaly detection engagements have been with regional supply chain operators.