Supply Chain & Data Consulting

Supply chains
run on decisions.
Decisions run
on data.

We assess, redesign, and implement the planning processes and data infrastructure that manufacturing and production organizations depend on — then hand them over, fully documented, ready to run.

Start a conversationSee what we do
Focus
Specialist, not generalist

Production planning, demand planning, and enterprise reporting — not broad advisory that spans every function.

Delivery
Practitioner-led

Recommendations are implemented alongside your teams, not handed over in a slide deck and left to you.

Technology
Technology-neutral

We work with what you already own before proposing new spend, and cost every option when we do.

Transfer
Knowledge built in

Training and documentation are part of every engagement. When we leave, your teams can run it independently.

What we do

Two practices, one connected brief

Supply chain performance and data quality are rarely separate problems. We address both — together, or independently — depending on where the greatest leverage sits.

Supply Chain Consulting

Specialized in production and demand planning. We establish rigorous planning processes and KPIs in organizations where informal practices have created gaps and risk.

  • Introduce best practices established in modern production organizations
  • Assess current practices to identify gaps and vulnerabilities
  • Recommend and implement agreed solutions and processes
  • Deliver training to planning and operations teams
  • Establish KPIs and the methods required to monitor them
  • Document improved processes for customer and auditor review

Data Flow & AI-Ready Infrastructure

The data foundation on which modern reporting and AI depend. We redesign how data moves, where it lives, and how it is governed — eliminating the failure points that distort planning decisions.

  • Review how data is stored and how reports are generated today
  • Assess gaps and vulnerabilities in current data practices
  • Recommend options across existing tools, open-source, and cloud platforms
  • Define a data strategy aligned to best practice and prepared for AI
  • Design and implement ETL pipelines and data architecture
  • Implement AI and machine learning solutions on prepared data
Technology

We work with what you have

Technology-neutral by principle. Deep expertise across the stack — from the spreadsheets at the centre of most operations to the cloud platforms that modernise them.

AI & Large Language Models
OpenAI / ChatGPTAnthropic ClaudeAzure OpenAI ServiceLLM IntegrationPrompt EngineeringRAG PipelinesAI Agents
Excel & Reporting
Power QueryPower BILive Data ModelsRefreshable WorkbooksMacro AutomationAdvanced FormulasReport Design
Data Architecture & Integration
Dimensional ModelingETL PipelinesAzureDatabricksMCP ConnectivityThird-party APIsData Integration
Governance & Security
Data GovernanceOwnership FrameworksSecure GatewaysAccess ControlsAudit Trails
Platforms & tools we work in
Microsoft AzureDatabricksPower BIExcel & Power QueryAzure Data FactorySQL ServerPythondbt
How we engage

Four stages. One through-line.

Every engagement — regardless of size — follows the same structured approach. The stages scale to the problem; the method does not change.

Assess

Understand the current state

Review current processes, data flows, and reporting against how the operation is expected to run. Identify and prioritise gaps.

Design

Agree the target state

Define the target process and data architecture. Options are costed and prioritised before implementation begins.

Implement

Build alongside your teams

Deliver the agreed solution working alongside the people who will own it. Not handed over — built together.

Embed

Transfer and hand over

Train users, stand up KPIs, and hand over documentation. Your teams can run the solution independently.

Engagement models

Diagnostic Review

A short, fixed-scope assessment with prioritised recommendations — for when the problem is unclear or investment is yet to be sanctioned.

Project Delivery

Design and implementation of an agreed solution to a defined scope, with a clear output and structured handover.

Ongoing Support

Retained capacity to maintain, extend, and monitor what has been built as the operation evolves.

Training Only

Advanced Excel, enterprise reporting, and data modelling delivered directly to your teams.

Outcomes

What a well-run engagement delivers

  • Planning decisions made from a single, trusted set of numbers

  • Manual reporting effort reduced through refreshable, connected reports

  • Fewer failure points in data handovers between systems and teams

  • A documented process your teams can run — and show to customers and auditors

  • A data estate ready for AI and machine learning, without rework

We work with manufacturing and production organizations, distributors, and enterprises where planning and reporting depend on data spread across multiple systems — typically alongside supply chain, operations, finance, and IT teams.

Selected work

Engagements that show the pattern

A representative sample of the manufacturing, production, and data engagements we've delivered — anonymized where clients have asked us to keep specifics confidential.

Apparel Manufacturing

Leading apparel manufacturer

Challenge

Production planning ran on informal, tribal-knowledge practices — sales commitments and shop-floor capacity were reconciled manually, with little visibility into work in progress.

Approach

Assessed current planning practices against modern production standards, then introduced a structured production planning process with clear KPIs and training for the planning team.

Outcome

Planning decisions now draw from a single, trusted set of numbers, with the improved process documented for internal review and customer audits.

Apparel Vendor — US

International apparel vendor — US

Challenge

Demand planning was spread across disconnected spreadsheets that took days to refresh, leaving the team unable to respond quickly to shifting orders.

Approach

Rebuilt the demand planning workflow around live, refreshable Power Query and Power BI models, replacing manual re-keying with connected reporting.

Outcome

Manual reporting effort dropped sharply, and the planning team can now refresh forecasts on demand instead of waiting on a manual rebuild.

Medical Accessories

Leading medical accessories producer

Challenge

Production, quality, and ERP data lived in separate systems with no single source of truth, making customer and auditor reporting slow and error-prone.

Approach

Reviewed data flows end-to-end, designed a consolidated data architecture, and implemented governance and access controls around it.

Outcome

Fewer failure points in data handovers between systems, and a documented, auditable process the team runs independently.

AI Readiness

AI consulting engagement

Challenge

The data estate was not prepared for AI initiatives — inconsistent structure, unclear ownership, and no governance framework to build on safely.

Approach

Defined a data strategy aligned to best practice, prepared datasets and pipelines for AI workloads, and advised on integration options including LLM-based tooling.

Outcome

A data estate ready for AI and machine learning use cases, without the rework that typically follows a rushed rollout.

Get in touch

Tell us where the greatest challenge sits

We will outline what a solution would involve in a no-obligation initial conversation. Send a short description of your planning or reporting setup, and we will respond with an initial view.

Manufacturing & production organizationsDistributors with complex data environmentsEnterprises planning a move toward AI-ready data