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.
Evidence from the field
Where this has actually worked, and where it has not.
Published results from other organisations, cited so you can check them. None of these are our clients and none of these numbers are ours.
Where the gains are real
Two things are well evidenced: inline quality inspection using vision and sensor data, and predictive maintenance. Both work because the ground truth is unambiguous. A part either passes or it does not, and a machine either failed or it did not.
What the evidence does not support
Demand forecasting evidence is much weaker than vendors imply. Gains depend almost entirely on order-history quality and on whether planners trust the output enough to stop maintaining a parallel spreadsheet.
BMW Group
Germany, and every BMW plant worldwide
AIQX, an inline quality platform using conveyor-mounted cameras and sensors, with AI analysing the data in real time and pushing feedback to the operator on the line.
Now standard across every BMW Group plant globally, covering variant identification, completeness checking and anomaly detection. The company is assessing making it available to suppliers.
What it does not prove: BMW publishes the deployment scope but not a headline defect-rate figure. Worldwide rollout by a manufacturer of this size is the substantive signal here, not a percentage.
United States Department of Energy, Federal Energy Management Program
United States
Established the baseline effect of moving from scheduled to predictive maintenance, which is the honest comparison for any AI maintenance business case.
A properly functioning predictive maintenance programme returned 8 to 12 per cent savings over preventive maintenance alone, rising past 30 to 40 per cent where a facility had been relying on reactive maintenance.
What it does not prove: Predates modern machine learning, and the two ranges are doing different work. The 8 to 12 per cent is the gain against an already competent programme. The 30 to 40 per cent is the gain against neglect. Which one a business is entitled to expect depends entirely on where it starts.
US Department of Energy, O and M Best Practices Guide Release 3.0, 2010
Stanford HAI, AI Index
Global
Tracked where measured productivity gains actually landed across business functions.
Gains fall as the work requires more reasoning. Structured, high-volume, verifiable tasks moved most, which is exactly the shape of inspection and maintenance work.
Toyota Motor Corporation
Japan
An in-house AI Platform that lets production line staff, rather than specialist AI engineers, build and deploy their own machine learning models against manufacturing line data.
Nearly 1,200 employees use the platform across all 10 of Toyota's car and unit manufacturing factories, with more than 400 staff going through in-house training each year. Toyota reports over 10,000 man-hours saved per year, 10,000 models created in 2024 (up from 8,000 in 2023), and a 20% reduction in model creation time after a container image streaming change.
What it does not prove: Written by Toyota's own AI group and published by its cloud vendor. The hours figure is an internal estimate and the post does not state how it was measured or against what baseline. Model count is an activity measure, not a value measure.
Google Cloud blog, authored by Toyota Motor Corporation, 2024
Toyota Industries Corporation
Japan, Nagakusa Plant
Paint shop process data unified into a semantic layer on Azure with Sight Machine, analysing close to 400 process variables to find the drivers of paint defects on bumper production, replacing manual sight-based inspection judgement.
A 25% reduction in seeding-related paint defects during the pilot, analysis cycles cut from 5 days to under 4 hours, and about 80% less preparation time for daily standup meetings.
What it does not prove: A vendor-published customer story, not an independent evaluation. The 25% figure covers one defect type at one plant during a pilot phase, and the 18% carbon reduction quoted in the same story is an expectation rather than a measured result.
Siemens
China, Nanjing
More than 50 artificial intelligence applications deployed alongside digital twins, modular automation and manufacturing operations management at Siemens' Nanjing Digital Industries factory.
Measured against a 2022 baseline, Siemens reports lead times down 78%, time to market down 33%, productivity up 14% by 2024, field failures down 46%, and direct and energy-related carbon emissions down 28%. The site was named a World Economic Forum Global Lighthouse.
What it does not prove: Siemens' own figures for its own plant. The AI applications went in alongside a wider rebuild of the factory, so the improvements cannot be attributed to AI on its own. The Lighthouse designation is a recognition programme, not an audit of the numbers.
Schneider Electric
Indonesia, Batam
Industrial IoT deployment at the Batam plant combining smart sensors, alarm prediction management, augmented reality and planning and scheduling tools, with the site used as a test bed for machine learning and predictive maintenance.
A 44% reduction in machine downtime in one year and a 40% improvement in on-time delivery.
What it does not prove: Company-published and not independently verified. Machine learning was one of several technologies introduced at the same time, so the downtime figure belongs to the whole programme rather than to AI. The release also describes the plant as a test bed for AI, which suggests the AI element was less mature than the sensing and scheduling work.
McElheran, Li, Brynjolfsson, Kroff, Dinlersoz, Foster and Zolas, using the US Census Bureau Annual Business Survey
United States, peer-reviewed working paper
Analysis of AI use in production as reported by 850,000 US firms in the 2018 Annual Business Survey, the largest firm-level collection of its kind.
Fewer than 6% of US firms used any of the measured AI technologies, though employment-weighted adoption was just over 18%. Manufacturing was one of the two leading sectors at roughly 12% adoption, while construction and retail trade lagged at roughly 4%.
What it does not prove: The survey year is 2018, before generative AI, so this describes the starting point rather than today's adoption. It measures use, not benefit: the paper does not establish that adopting firms performed better.
Sector figures are useful for deciding what to try first, and almost useless for forecasting your own result. The organisations above had the data, the systems access and the governance in place before they measured anything. The full global picture covers 13 industries, sets out what separated the organisations that got a result from the ones that got nothing, and carries the randomised trials that found no gain at all.
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, with a modelled layer underneath it that the next AI or forecasting project can stand on.
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.