What Data Do I Need to Use AI in Manufacturing?
Learn what data manufacturers need for AI, including production, inventory, quality, maintenance, purchase, dispatch, finance, and machine data.
What Data Do I Need to Use AI in Manufacturing?
The data you need for AI in manufacturing depends on the problem you want to solve. AI for SOP writing needs documents and process notes. AI for inventory needs stock and consumption data. AI for predictive maintenance needs machine or maintenance data.
Start with the use case, then identify the data.
Production Data
Work orders, output, WIP, downtime, machine usage, shift data, and production delays help AI support planning and performance analysis.
Inventory Data
Stock movement, inward, issue, consumption, adjustments, ageing, reorder levels, and locations help AI improve inventory decisions.
Quality Data
Inspection results, rejection reasons, customer complaints, corrective actions, batches, and supplier links help AI identify defect patterns.
Maintenance Data
Downtime logs, breakdown history, spare usage, sensor readings, and maintenance schedules support predictive maintenance.
Purchase and Dispatch Data
Vendor performance, lead times, pending purchase, dispatch status, and customer commitments help AI highlight operational risks.
Where AICAN Optiwise Fits
AICAN Optiwise connects these data areas inside one manufacturing operating system, giving AI stronger context across ERP workflows.
FAQ
Do I need all data before starting AI?
No. You need the data relevant to the selected use case.
Is spreadsheet data enough?
It can be enough for basic analysis, but ERP data is more reliable for ongoing AI.
What if my data is messy?
Start cleaning the most important fields and use a narrow pilot.
Final Thought
AI is only as useful as the data behind it. Choose the problem first, then build the data foundation needed to solve it.
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