Multi-Industry — Enterprise Analytics
Reduction in effort to deploy analytics into a new business unit
Business units live on the shared toolkit within six months
About
Several business units within the group wanted to replace ad-hoc, one-off analytics builds with a single reusable toolkit. Eight separate forecasting codebases and five separate solver stacks were driving maintenance cost up 40% and stretching new deployments to nine months. We packaged a Dockerised, reusable OR and BI template pipeline any business unit could clone in a day.
Industry
Multi-Industry — Enterprise Analytics
Company size
10,000+ employees
Founded
1965
The Company
A conglomerate with eight versions of the same problem
Several business units across a large multi-industry group had each independently built their own forecasting and optimisation capability over the years — reasonable in isolation, but the group as a whole ended up with eight separate forecasting codebases and five separate solver stacks doing largely overlapping work.
None of the implementations talked to each other, none shared a common testing or deployment standard, and each carried its own maintenance burden that fell on whichever small team had originally built it.
The challenge
Nine months to stand up analytics a new business unit had seen work elsewhere
A new business unit wanting the forecasting or optimisation capability another BU already had working couldn't simply adopt it — the underlying codebase was typically too specific to the originating BU's data and tooling to reuse directly, so deployments were rebuilt largely from scratch.
That rebuild cycle routinely took nine months and drove overall analytics maintenance cost up 40% across the group, since every BU was independently maintaining, patching, and re-testing its own version of essentially the same forecasting logic.
The Solution
A cloneable, Dockerised template pipeline
We packaged the group's most proven forecasting and optimisation patterns into Dockerised Python libraries with standardised Power BI themes and Optuna-driven hyperparameter tuning built in, distributed through a GitLab template pipeline any business unit could clone directly rather than rebuild from a specification document.
The template handled the parts of every deployment that had previously been rebuilt from scratch — data connectors, model scaffolding, dashboard theming — leaving each business unit to configure only what was genuinely specific to its own data and use case.
The Results
45% less effort per deployment, three business units live in six months
Deployment effort for a new business unit fell 45%, and three business units were live on the shared toolkit within six months of the template's release — a pace the previous nine-month, from-scratch rebuild cycle could not have matched.
Consolidating eight forecasting codebases toward a single reusable template also materially reduced the group's ongoing maintenance burden, since a fix or improvement made once now propagates to every business unit using the template rather than needing to be reimplemented separately in each.
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