Supply Chain solutions
Demand Forecasting for Supply Chain
Plenty of consultancies will sell supply chain operators a demand forecasting platform. Beryl Analytics sells supply chain operators an operating model — the workflows, ownership, and review cadences that make analytics actually drive decisions. The tech is the easy part.
Why supply chain teams choose Beryl Analytics for demand forecasting
- Deep-domain models. Every demand forecasting 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 demand forecasting 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 Demand Forecasting for Supply Chain
How long does a typical Demand Forecasting engagement take for a supply chain business?
Most demand forecasting 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 Demand Forecasting 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 demand forecasting 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 Demand Forecasting 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. Demand Forecasting 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 demand forecasting engagements have been with regional supply chain operators.