Manufacturing and Distribution
Plan against what is actually happening on the floor.
Demand and inventory intelligence, production analytics and supplier automation built on data your planners already trust.
Operational context
How the sector actually runs.
The plan and the floor diverge within hours. An MRP run produces a schedule overnight, then a machine goes down, a supplier short-ships and an urgent order jumps the queue, and by mid-morning the schedule is a document nobody is following.
The ERP is often the oldest system in the business and the one nobody will touch. It holds the bills of materials, the routings and the stock positions, it runs on infrastructure two upgrades behind, and any change to it requires the one person who understands the customisations.
Inventory decisions are made on averages that hide the problem. Reorder points set years ago against smooth demand fail against lumpy ordering, long supplier lead times and seasonality, so the business carries too much of the wrong stock and stocks out of the right stock.
Quality and production data exists but is trapped. Machine counters, non-conformance reports, downtime logs and scrap records live in separate systems or on paper, so nobody can answer why last quarter's yield dropped.
Supplier communication is manual and high-volume. Purchase order confirmations, lead-time changes and advance shipping notices arrive as email and PDF, and a buyer keys them into the ERP one at a time.
High-value problems
Where the money and the risk actually sit.
Our forecast is a spreadsheet somebody maintains by feel
We build a demand model on actual order history, seasonality and known drivers, and run it alongside the existing spreadsheet so the planner can see where the two disagree and why. The planner keeps the final call on the number that goes into the plan.
We are carrying too much stock and still running out of the fast movers
Inventory analytics segment items by demand variability and lead time rather than value alone, and recommend reorder points per segment. Recommendations are reviewed and applied by the planner, not written directly to the ERP.
Nobody can tell me why yield dropped last quarter
We consolidate machine data, downtime logs, non-conformance reports and scrap records into one model with a consistent time base, so a yield movement can be traced to a shift, a line, a product or a material batch.
Our buyers spend their week retyping supplier emails into the ERP
Document intelligence reads order confirmations and advance shipping notices, extracts the quantities and dates, and stages them against the matching purchase order. The buyer approves or corrects the match rather than transcribing it.
We cannot touch the ERP without breaking something
We work alongside it rather than through it. An integration layer reads from the ERP on a schedule the platform can sustain and writes back only through supported interfaces, with changes staged for approval so no automated process posts directly to production data.
Use cases
What we build in this sector.
Each is tagged with the disciplines involved, because most useful work crosses more than one.
Demand forecasting
Statistical and machine-learning forecasts on real order history, presented next to the planner's own numbers with the variance explained.
- AI
- Data
Inventory segmentation and reorder policy
Segment by demand variability and supplier lead time, then recommend reorder points and safety stock per segment for planner approval.
- Data
- AI
Production and OEE analytics
Consolidate machine counters, downtime reasons, scrap and non-conformance data into one model with a consistent time base.
- Data
Supplier document automation
Extract quantities, prices and dates from order confirmations and shipping notices, match them to open purchase orders and stage the update.
- AI
- Automation
ERP integration layer
A supported, monitored interface between the ERP and everything else, so analytics and automation never depend on direct database access.
- Cloud
- Data
Real-time floor dashboards
Line and shift dashboards that update through the day rather than reporting yesterday, built for a screen on the floor as well as a desk.
- Data
- Cloud
Operational technology network separation
Segment plant networks from corporate IT, control remote access to machine systems and monitor the boundary between them.
- Security
- Cloud
Human oversight
Where a person still decides.
Automation proposes. A named person approves. These are the points we design the workflow to stop at.
- The demand planner approves the forecast that enters the production plan. A model output is a recommendation, never an automatic input to MRP.
- A supply chain manager approves any change to reorder points or safety stock before it is applied in the ERP.
- The buyer confirms each matched supplier document before the purchase order is updated. Unmatched or ambiguous documents are queued, not guessed.
- A production manager approves any automated reschedule before it reaches the floor.
- An engineering or OT lead approves any change affecting plant network access, separately from corporate IT change control.
Delivery options
On Azure, on AWS, and afterwards.
The platform is chosen for the workload, not for us. Both paths end in the same place: someone still owns it after go-live.
- 01
Delivered on Microsoft Azure
Suits manufacturers running Dynamics or a Microsoft-centric back office, where the finance and planning teams already work in Excel and Power BI.
- Microsoft Fabric consolidates ERP extracts, machine data and quality records into OneLake with a governed semantic model.
- Fabric Real-Time Intelligence handles high-frequency machine and sensor data for floor dashboards.
- Azure Machine Learning trains and versions the demand models, with performance tracked against held-back periods.
- Azure AI Document Intelligence reads supplier order confirmations and advance shipping notices.
- Azure Arc brings on-premises ERP and plant servers under consistent policy and monitoring without migrating them.
- Power BI delivers planner and floor reporting, with row-level security by site and product line.
- 02
Delivered on AWS
Suits manufacturers with existing AWS workloads, bespoke MES or shop-floor systems, or a mixed ERP estate that does not sit naturally in the Microsoft stack.
- AWS Glue and Amazon Redshift build the consolidated planning and production model from ERP and machine sources.
- Amazon Kinesis ingests high-frequency machine telemetry for near-real-time floor reporting.
- Amazon SageMaker AI trains, evaluates and versions demand and yield models with reproducible pipelines.
- Amazon Bedrock and Amazon Textract handle supplier document extraction and matching.
- AWS Database Migration Service moves ageing on-premises databases with a rehearsed cutover.
- Amazon Quick delivers planner and shift-level reporting against the governed model.
- 03
Operated after handover
Forecast quality decays quietly. Somebody has to notice that the model is drifting before the planner stops trusting it and goes back to the spreadsheet.
- Forecast accuracy tracked per product segment each cycle, with degradation reported and investigated.
- Data pipeline monitoring for ERP extracts and machine feeds, with alerting on missing or late loads.
- Model retraining on an agreed cadence, with results reviewed before promotion.
- Document extraction match rates monitored, and new supplier formats onboarded as they appear.
- Cloud cost optimisation across storage and compute, reported monthly against production volume.
- Quarterly review of planner overrides, which is the most reliable signal of where the model is wrong.
Related services
The capabilities behind this work.
Data and Analytics
Reporting your executives trust, produced once, governed properly and ready for the AI work that comes next.
Artificial Intelligence
AI that is chosen for a reason, costed before it is built and governed once it is live.
Cloud Modernisation
Ageing systems become a cloud platform your team can change safely, recover predictably and account for line by line.
Managed Services
Your platform keeps earning its business case after go-live, with cost, security posture, reliability and adoption reviewed on an agreed cycle rather than left to drift.
Free discovery workshop
Start with one manufacturing challenge.
Bring a process that costs more than it should. We will map the opportunity, the readiness gaps and a recommended next step.