What Data Do You Need to Use AI in Manufacturing?

AI readiness is not a separate data project. It starts with the factory records that already decide work: item masters, BOMs, work orders, stock, purchase orders, inspections, dispatch and machine signals.

Optiwise connects these records in one operating system, so AI questions can use real context rather than isolated spreadsheets.

Direct answer

AI needs the same records managers use to run the factory: clean items, BOMs, orders, stock, purchase, inspection, production and machine signals where needed.

Factory example

A planner asks AI why an order is late. The answer needs sales order date, BOM demand, stock availability, open purchase orders, work-order status, quality holds and dispatch readiness. Missing any one of those records weakens the answer.

Worksheet for the decision

Area	What to check	Why it matters
Data area	Required record	Common gap
Production	Work orders, routes, output and delay reasons	Open jobs not closed
Inventory	Item, batch, location, reservation and ageing	Manual adjustments without reason
Purchase	Indent, RFQ, PO, receipt and supplier history	Partial receipts not reconciled
Quality	Inspection result, hold and disposition	Pass/fail with no parameter
Dispatch	Packing, document and shipment status	Manual follow-up outside system
Machine	Runtime, stop, load or cycle signals	No source timestamp or stale flag

How Optiwise supports this workflow

• Use item masters, variants, multi-UOM, BOM and routing records as the base.
• Connect purchase, production, inventory and quality workflows before asking AI for recommendations.
• Add IoT data through OptiEdge where machine signals are required.

Use the related Optiwise workflows for AI agents, production, inventory, purchasing, quality and industrial IoT.

Implementation notes

Data readiness should be checked by workflow, not by department. For material readiness, BOM, stock, reservations, open POs and work orders matter most. For quality review, inspection parameters, batch links and disposition history matter more. This keeps cleanup practical because teams fix the records that support the first decision instead of trying to clean everything at once.

Source and control checks

For AI risk and governance language, compare your project controls with the NIST AI Risk Management Framework. For connected device access and security planning, also review CISA cybersecurity performance goals where applicable.

Before using the recommendation, confirm the record owner, source timestamp, approval rule and missing-data handling. Keep customer results, savings numbers, safety certification and regulatory claims out of the page unless there is separate evidence for that exact claim.

Buyer review before rollout

Before rollout, review one finished transaction from start to end. Check the source record, the recommended action, the approval step, the person who accepted or rejected it and the operational result. This review catches weak data, vague ownership and unsafe automation before the workflow reaches more users. Keep the pilot narrow until the team can explain the recommendation in plain factory language.

FAQs

Do we need perfect data before using AI?

No. You need trustworthy data for the selected decision. Start with one workflow and fix the fields that decision needs.

Which data should be cleaned first?

Clean item masters, BOMs, stock locations, open orders and inspection rules before complex AI use cases.