Supply Chain solutions
Real-time Analytics for Supply Chain
If you've ever had a data initiative die in handover, you know the problem isn't the model — it's the moment the consultants leave. Beryl Analytics pairs into supply chain operators from day one so the system runs itself before we step back.
Why supply chain teams choose Beryl Analytics for real-time analytics
- One slice, working, in six weeks. No 18-month roadmaps that quietly stall. The first real-time analytics slice is small, complete, and measurable inside the first sprint.
- Data contracts before models. We formalise the inputs your model depends on — schemas, freshness, ownership — so the system doesn't silently rot when an upstream team changes a field.
- Operator-grade UX. real-time analytics outputs render inside the tools your team already uses (your CRM, your ticketing system, your dashboards) — not yet another tab they have to remember.
- Right-sized stack. supply chain operators don't need a Snowflake plus Databricks plus dbt cathedral to start. We pick the minimum infrastructure that ships value, then grow it deliberately.
- Outcome documentation. Every result is written up with the methodology, caveats, and ablation. Your CFO, auditor, and incoming team lead can all retrace why we built what we built.
How we deliver real-time analytics engagements
- 01
Discovery (week 1-2)
We meet your operators, map data sources, and pressure-test the business case. Half the value is sometimes in killing the wrong initiative and reframing the right one.
- 02
Pilot build (week 3-6)
One vertical slice end-to-end: ingest, model, dashboard, monitoring. Real data, real users, measurable result before we expand.
- 03
Productionise (week 7-12)
Hardening, governance, lineage, runbooks, observability. Pair-programmed with your team so they own it by handover.
- 04
Scale & evolve
Expansion into adjacent use cases, retraining cadence, model performance reviews, and a roadmap that compounds.
Frequently asked questions about Real-time Analytics for Supply Chain
How long does a typical Real-time Analytics engagement take for a supply chain business?
Most real-time analytics 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 Real-time Analytics 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 real-time analytics 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 Real-time Analytics 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. Real-time Analytics 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 real-time analytics engagements have been with regional supply chain operators.