Energy solutions
Inventory Optimisation for Energy
For energy companies considering inventory optimisation, the question is rarely "can it be done" — it's "can it be done in a way the business will actually adopt." That's where Beryl Analytics comes in.
Why energy teams choose Beryl Analytics for inventory optimisation
- One slice, working, in six weeks. No 18-month roadmaps that quietly stall. The first inventory optimisation 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. inventory optimisation 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. energy companies 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 inventory optimisation engagements
- 01
Discovery sprint (week 1)
Two days on-site with your operators to map the workflow, half a day with leadership to align on the dollar metric, and an afternoon writing the scope memo we'll work to.
- 02
Spike the riskiest assumption (weeks 2-3)
Before committing to the build, we attack the assumption most likely to kill the project — usually data availability or operator adoption. A negative result here saves months.
- 03
Build, in public (weeks 4-8)
Daily commits to a shared repo your engineers can read. Weekly demo to the operator group. Nothing is built in private.
- 04
Production cutover (weeks 9-10)
A planned cutover with a rollback plan, monitoring, and a human in the loop for the first fortnight. We don't walk away from cold launches.
Frequently asked questions about Inventory Optimisation for Energy
How long does a typical Inventory Optimisation engagement take for a energy business?
Most inventory optimisation projects for energy companies land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger energy programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Inventory Optimisation project in energy?
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 inventory optimisation with our existing energy companies systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of energy-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Inventory Optimisation 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. Inventory Optimisation engagements in energy typically return 4-12x within the first year.
Do you work with energy 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 inventory optimisation engagements have been with regional energy companies.