AI readiness for manufacturing companiesYour line measures everything. Your ERP doesn’t know half of it.
Manufacturers measure the same thing the same way, every shift. The trouble is that the shop floor, engineering and the ERP grew up separately. Planning, pricing and automation plays need the part, the order and the cost to mean the same thing on every side, and nobody has checked that it does.
Starts with a 30-minute call. Commits you to nothing.
The AI on the table
The AI plays proposed in manufacturing.
What vendors and plans put forward in manufacturing, what each play needs from the data, and the gap that holds it back. In a real Read, each call rests on your Truth Score™ (a score out of 100 for how far your data can be trusted) and the records behind it. The calls here are an illustrative read, not a benchmark.
- Needs work
- Needs
- Order history with one part number throughout
- Held back by
- Parts renumbered or duplicated across plants
-
Quote and price optimisation
“Price quotes from real cost and win history”
What pricing and rebate optimisation needsBlocked- Needs
- Costs that reflect real routings and scrap
- Held back by
- Standard costs years out of date
-
Purchase-to-pay automation
“Match and pay supplier invoices automatically”
What process automation needsNeeds work- Needs
- Orders, receipts and invoices that match three ways
- Held back by
- Duplicate suppliers and unmatched goods receipts
-
Supplier document extraction
“Read certificates and invoices automatically”
What document intelligence needsNeeds work- Needs
- Documents that match supplier and part records
- Held back by
- Supplier names that match no vendor record
-
Predictive maintenance
“Predict failures before they stop the line”
Blocked- Needs
- Sensor and maintenance history tied to the right asset
- Held back by
- Shop-floor asset IDs that don’t match the asset register
-
Month-end close automation
“Close faster with automated reconciliations”
What financial close and FP&A needsNeeds work- Needs
- Inventory and work in progress that tie to the ledger
- Held back by
- Stock valuation that differs between plants
Illustrative example, not a client. Your calls depend on your records.
The systems estate
The systems behind the plays.
Written for discrete and process manufacturers and engineered-products businesses, single or multi-plant, running an ERP beside shop-floor and quality systems, engineering data and a CRM. Each system is built to be right about one thing. None was built to agree with the others, and the plays above all run across more than one.
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ERP
Orders, parts, stock, costs
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Shop floor
Production orders, machine data
MES and machine historians
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Quality
Inspections, non-conformances
Quality management system
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Engineering
Bills of materials, revisions
PLM and drawings
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CRM
Customers, quotes, distributors
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HR and payroll
Shifts, labour, cost centres
Typical, not exhaustive. Anything that exports to CSV or Excel can be included.
The gaps
Every system looks clean on its own. The truth is in the gaps between them.
One record traced across your MES, the ERP and the quality system: each system right about its own part, and the three still disagree. Then the gaps that hold the plays back. Which of them exist in your data is what a Truth Score Read finds out.
Where the plays sink
-
Shop floor ↔ ERP
Production orders finished on the floor, still open in the ERP
SinksPlanning and close automation
-
Engineering ↔ ERP
Bills of materials that differ between PLM and the ERP
SinksCosting and quote optimisation
-
Plant ↔ Plant
The same part under different numbers at each plant
SinksDemand planning and purchasing
-
Maintenance ↔ ERP
Assets in the maintenance log that match no ERP asset
SinksPredictive maintenance
Gaps to test in the sector, not client findings.
Regulation
The rules that shape which plays you can run.
Kept short and honest. This is not legal advice: we evidence what your data can support; your counsel decides what the rules require.
The EU AI Act and machinery
AI that acts as a safety component of machinery is high-risk. The plays on this page are commercial and planning; safety-related AI needs its own conformity path.
The EU Data Act
Users of connected products gain rights to the data those products generate. If you sell connected equipment, that affects how you, and your customers, can use machine data for AI.
Traceability duties
Automotive, aerospace and medical supply chains carry part and batch traceability obligations. Automating anything that touches them puts the weight on those records being right.
Your plan, tested
Got a plan that names these plays?
Send it with your exports. A Truth Score Read tests the AI in it against your own records, within 5 working days of complete exports. Go further with an AI Investment Review: every bet ranked, your plan tested play by play, and a roadmap to hand to your own team.
- 01
Send the plan, if there is one
The business plan, growth strategy, value creation plan or the vendor proposal. We pull out the AI plays in it and the targets that lean on them.
- 02
Send exports from two or more systems
Usually ERP and shop floor to start. Spreadsheets count. Under NDA, using the field list we send.
- 03
Test each play against the records
Each play mapped to the data it needs. Our engine matches the customers, people, deals and invoices across your exports to see whether that data is there and agrees, and every red flag is checked by hand against sample records.
- 04
Get the verdict, play by play
A signed memo: your Truth Score, a Leverability read marking each play Deploy now, Needs work or Blocked with the reason in your own records, and a verdict: sign, fix first or walk away.
What you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Not a software company? The sample is one, but the method doesn’t change; only the systems do. For a manufacturing business the same match runs across your MES, the ERP and the quality system, and every red flag is checked by hand the same way.
Questions
AI in manufacturing, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callCan you assess shop-floor and machine data?
Where it exports, yes, alongside the ERP. Machine and sensor data is large and specialised, so for predictive maintenance we agree the scope with your team first: which assets, which signals, and how they map to the asset register.
We run several plants on different ERPs. Is that common?
Common in groups built by acquisition, and a common reason group-wide planning AI stalls. Cross-system matching shows where parts, suppliers and customers diverge between plants.
Which play is usually closest to ready?
There is no general answer; it depends on your records. Plays that live inside one well-run ERP face a lower bar than plays that cross the shop floor, engineering and finance.
Do you recommend MES or ERP vendors?
No. We sell no software and take no referral fees. We tell you what your data can support today and what has to change first.
Before manufacturing AI gets funded, find out what your data can carry.
Exports from two or more systems, plus the plan or proposal if there is one. A signed verdict, play by play, within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.