Automotive
SPC: Statistical Process Control
Separating normal variation from real change, so processes are adjusted only when they should be.
Reference noteThis page is an independent plain-language summary written by QUALITAS TR. It does not reproduce the standard. Always work from the current official edition, available from AIAG, the Automotive Industry Action Group (FMEA jointly with VDA) (official page). QUALITAS TR supports organisations against this standard; it does not certify them.
01Purpose
SPC uses statistical methods, mainly control charts, to monitor process behaviour and distinguish common cause from special cause variation. Capability studies compare process spread with specification limits.
02Scope
Applied to key product and process characteristics, especially special characteristics in automotive control plans.
03Main requirements
- Selection of characteristics and rational subgrouping
- Control chart types (X-bar R, X-bar s, individuals, p, np, c, u)
- Control limits calculated from process data, not specification
- Run rules for detecting special causes
- Reaction plans when signals occur
- Capability indices Cp, Cpk and performance indices Pp, Ppk
04Typical applications and industries
Series production of machined, moulded and stamped parts, and process parameters such as temperatures and pressures. See the Cp/Cpk calculator.
05Audit and inspection considerations
- Are charts maintained and acted on in real time?
- Are control limits calculated correctly?
- Do reaction plans exist and get used?
06Common nonconformities
- Control limits replaced by specification limits
- Charts filled in after the fact
- Signals with no recorded reaction
- Capability calculated from unstable data
07How QUALITAS TR can support
We review SPC practice in process audits and explain it in our capability guide.