Shop floor data you can act on today

Standards and compliance on the factory floor

Manufacturing software often has to produce evidence for audits. ISO 9001 expects controlled documents and quality records, automotive suppliers working to IATF 16949 need traceability and control of nonconforming parts, and pharmaceutical and medical device makers serving the US must meet FDA 21 CFR Part 11 for electronic records and signatures, with EU GMP Annex 11 playing a similar role in Europe.

Architecture standards help too. The ISA-95 model describes how enterprise systems such as ERP and plant systems such as MES and SCADA should divide responsibilities and exchange data, and the ISA/IEC 62443 series addresses industrial cybersecurity. This is general information, not legal or certification advice; quality and regulatory teams should confirm requirements for each product and market.

In practice, compliance features should be part of the design rather than added before an audit. Record who did what and when for every critical step, prevent edits to approved records except through controlled corrections, and make reports easy to export in the format auditors request.

• Keep audit trails for every change to production and quality records.
• Use electronic signatures with identity checks where regulations require them.
• Link each batch or serial number to materials, machines and operators.
• Control document versions for work instructions and specifications.
• Segment plant networks and restrict remote access.

How to choose a manufacturing software partner

Factory software succeeds when it fits real shift work. A strong partner spends time on the shop floor, talks to operators and supervisors as well as managers, understands industrial protocols such as OPC UA and Modbus, and designs screens for gloves, noise and bright lighting. They should be comfortable working alongside your ERP vendor and maintenance team rather than replacing everything.

Ask for a pilot with measurable goals on one line before plant-wide rollout, and for clarity on who supports the system during night shifts. Plants cannot wait until morning when a production terminal stops working, so support hours, spare devices and offline behavior must be agreed upfront.

• Which machine protocols and PLC brands have you connected?
• How will the system behave if the network drops mid-shift?
• What does success look like for the pilot line?
• How do you integrate with our ERP without duplicating data?

Where AI fits in manufacturing

Proven uses include predictive maintenance from vibration, temperature and current data, camera-based quality inspection that catches defects consistently across shifts, process parameter optimization that improves yield, and demand forecasting that feeds production planning. Maintenance assistants grounded in equipment manuals and past work orders help technicians diagnose faults faster.

Data readiness decides success. Many plants first need reliable machine data collection, consistent downtime reason codes and labeled defect images before models can learn anything useful. Start where the cost of the problem is clear, such as a bottleneck machine or a high scrap rate, and compare model recommendations against experienced engineers before automating decisions.