£6M+
procurement savings identified in pilot
100+
analytics use cases inventoried and prioritised
Target operating model for analytics fully defined
Phased Azure platform roadmap delivered
Identified over £6M in procurement cost savings through an analytics pilot for a premium drinks manufacturer, delivered alongside a prioritised portfolio of 100+ use cases, a target operating model, and a phased Azure platform roadmap. The engagement moved analytics from aspiration to a funded, evidence-backed delivery programme.
The Problem
A premium drinks manufacturer was partway through a broader transformation programme. Leadership knew that data and analytics should be central to the effort, but the organisation had no structured view of where analytics could create value, what it would take to deliver, or in what order to proceed.
The Solution
We ran a structured engagement that moved from inventory through prioritisation to roadmap and proof of value, all within a single programme of work.
The starting point was use-case discovery. We reviewed existing strategy documents and operational data, then ran cross-functional workshops with commercial, supply chain, procurement, finance, and IT stakeholders. Each workshop had a defined scope, pre-read material, and a structured template forcing participants to articulate the business problem, data required, expected value, and barriers to delivery. The output was a register of over 100 use cases, each described in enough detail to be assessed consistently. We then ranked them against a value-versus-feasibility framework, separating use cases into actionable tiers: quick wins, strategic bets, incremental improvements, and deprioritised items. Forcing this separation required stakeholders to confront trade-offs and made prerequisites visible before delivery began.
Alongside prioritisation, we designed a target operating model covering governance, roles, processes, and ways of working. We mapped skills gaps across data engineering, data science, data literacy, and data governance. And we translated the prioritised portfolio into a phased Azure platform architecture, deliberately incremental: the minimum platform footprint needed for the first wave of use cases, with subsequent increments driven by later waves.
Strategy without delivery is just a document. We ran a procurement analytics pilot alongside the strategic workstreams, consolidating data from multiple purchasing systems and applying analytical techniques to identify savings opportunities: supplier consolidation, contract compliance gaps, demand aggregation, and specification rationalisation. The pilot surfaced over £6 million in cost-saving opportunities, validated with procurement leads and finance. Beyond the number itself, it demonstrated that the prioritisation framework could identify genuinely valuable use cases and established reusable delivery patterns for the broader roadmap.
Results and Impact
| Metric | Value |
|---|---|
| Use cases identified | 100+ across commercial, supply chain, procurement, and finance |
| Procurement savings identified | £6m+ in cost-saving opportunities surfaced by the pilot |
| Operating model coverage | Governance, roles, processes, and ways of working fully defined |
| Skills gaps mapped | Data engineering, data science, data literacy, data governance |
| Platform roadmap | Phased Azure architecture aligned to use-case delivery waves |
| Delivery patterns established | Reusable approach validated through procurement pilot |
The £6 million figure represented savings opportunities identified, covering supplier consolidation, contract compliance, demand aggregation, and specification rationalisation across the procurement spend base. More importantly, it demonstrated that a structured, evidence-based approach to analytics could surface material value quickly, which was the argument the programme needed to secure continued investment.
Key Takeaways
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Pair strategy with early delivery. The procurement pilot changed the tone of the entire engagement. Before it, the strategy was a set of recommendations; after it, the strategy had a proof point. Programmes that spend twelve months on roadmaps without delivering anything tangible lose credibility and momentum.
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Force trade-offs, not wishlists. Workshops that ask "what analytics would you like?" produce long, undifferentiated lists. Workshops that ask "given these constraints, which three use cases would you fund first?" produce decisions. The prioritisation framework only worked because it required participants to confront feasibility barriers and make choices.
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Sequence the platform behind the use cases. Building a comprehensive data platform before knowing what it needs to support is a reliable way to overspend and underdeliver. Defining the minimum platform for the first wave and extending incrementally kept the technology investment proportionate and justified.