Learn about the key changes and concepts in the new AIAG & VDA SPC Manual

How and why did this AIAG & VDA Statistical Process Control (SPC) version evolve? The previous AIAG SPC 2^nd Edition (SPC2) was published in 2005, so it has been more than 20 years since this core tool manual was revised. As with the AIAG-VDA FMEA 1^st Ed., released in 2019, this revision harmonizes the U.S. based Automotive Industry Action Group (AIAG) and European based German Association of the Automotive Industry (VDA) approaches.  In addition, this new manual aligns with the related ISO statistical standards, several of which are also being updated in a similar timeframe (more on this later). 

There is a philosophical shift in this manual as well, away from SPC as a standalone tool and toward implementing statistical process management/SPC based on understanding the process, risk (FMEA and special characteristics), and linking it to the APQP process, in addition to continual improvement. Whereas SPC2 focuses on the classic Shewhart control charts (i.e., control charts primarily intended to distinguish between variation due to random causes versus special causes), the new manual gives more weight to other control chart types, depending on the process.

Much more emphasis is placed on the proper implementation of a statistical management process, and less on the mechanics of constructing control charts and on the rules for interpreting them.

The AIAG SPC2 was termed a “reference manual,” and the AIAG & VDA SPC Manual Foreword says it is “non-mandatory”; however, the tone is more prescriptive.  The new SPC Manual contains eleven (11) “shall” s.  There are fifty-one (51) “musts” in SPC2, but one hundred thirty-three (133) in the SPC Manual.

Alignment with the ISO Statistical standards

The AIAG & VDA SPC Manual published on July 1, 2026, is designed to be fully aligned with the ISO Statistical standards but is more prescriptive due to the needs of automotive customers. The following ISO statistical standards are referenced in the SPC Manual:

• ISO 7870 (Control Charts) – global standard for control charts.
• ISO 22514 (Process Capability & Performance) – global standard for capability indices and process performance.
• ISO 11462 – Guidelines for the implementation of statistical process control.
• ISO 3534 (Statistics Vocabulary & Symbols) – standardizes statistical terminology.

The ISO statistical standards provide mathematical rules, statistical definitions, chart families, capability formulas, and vocabulary. The SPC Manual content borrows much from these ISO standards.

In line with ISO 11462, the new manual treats SPC as part of an overall statistical process management methodology (rather than just a standalone quality tool) that integrates process stability, performance, and capability into a multi-level Plan-Do-Check-Act control loop.  It also eliminates some of the inconsistencies between the VDA and AIAG approaches.

The Objectives of Statistical Process Control and Requirements for Proper Application

In both ISO 11462 and the new SPC manual, the objectives of Statistical Process Control (SPC) are to:

• increase knowledge of the process,
• control or steer the process,
• reduce variation,
• improve capability/performance,
• and improve economics/productivity.

The new SPC manual also outlines several requirements for the proper application of SPC:

• Clear product specifications (characteristic tolerances must be clearly defined in terms of function, production and inspection; risk assessment of product characteristics and their tolerances; special characteristics)
• Valid and capable inspection systems (e.g., MSA – IATF 16949® clause 7.1.5.1.1)
• Process characterization (determine relationship between process parameters and product characteristics – e.g., DOE)
• Planning production (how to make the design) and inspection planning (how to measure and inspect)
• Out-Of-Control Action Plan (OCAP) – guidelines for operators to make an adjustment when a control rule is violated (i.e., out-of-control (OOC) condition. Includes adjustment matrix, responsibility, process log, and training.
• Sampling Plan (subgroup size, subgroup frequency, conditions)
• Control Chart Selection (refer to Chapter 10)
• Control or Process Documentation
• Control Plan data collection, storage, and retrieval
• Roles and competencies in SPC
• Periodic audits of the SPC system

Process Performance and Capability Indices

There are some new concepts for process performance and capability indices as well.  One is a maturity progression for capability studies from Machine Performance (Pm, Pmk), to Process Performance (Pp, Ppk), and then, after a stable process (either statistically or controlled stable) is achieved, Process Capability (Cp, Cpk). Pp and Ppk are used both for preliminary process performance (to release production processes) and, after the process has been in production long enough to experience all sources of variation, as measures of process performance.  

At this point, stability is assessed.  If not stable, Pp and Ppk can still be used as a process performance measure to monitor production processes. However, a key concept in the new SPC Manual is the requirement that capability (Cpk) cannot be calculated until the process is stable; however, the definition of stable includes both “in statistical control” (that is, the process is subject only to random causes of variation, no special causes) and “in control” (or a “controlled stable process”). In control (controlled stable) is defined in ISO 22514-2; it means that the product characteristic is in control since changes over time only in a known, modeled or bounded way, making the process predictable, like tool wear.

Note that this is different from classical SPC which generally recognized that being “in statistical control” is a precondition for calculating valid capability indices; however, even SPC2 acknowledged that “reasonable approaches have been developed to assess the capability of processes” with “systematic special causes” for such things as tool wear that act systemically on a process in a way that can be predictable (SPC2, p. 127).

Greater Emphasis on Machine Performance

This manual includes detailed guidance on how to conduct Machine-only studies (Chapter 7), as well as discussions of both the General Geometric Model (used by VDA and the ISO series of standards and does not require normality) and the Z-Score method (primary method used in SPC-2, although the quantile method is discussed on pp. 142-143) for calculating process performance and capability indices (both are acceptable for normal distributions).

Process behavior over time must be considered to determine the correct SPC approach. Preliminary process performance studies (Chapter 8) are used to determine the appropriate distribution model for the process and to determine process stability.  Data collection and sampling strategies are outlined and section 8.4 goes into detail on the eight different types (cases) of time-dependent distribution models (per ISO 22514-2) that reflect both the subgroup distribution of the characteristic and the changes in its location, variation and shape during the time interval of observation (e.g., 25 subgroups of n=5). These models are classified into four groups:

• Model A – both the location and variations are constant over time; A1 is Normal, A2 is non-normal, but unimodal; both can be in statistical control.
• Model B – location is constant, but variation is systemic or random; not in statistical control
• Model C – location is not constant (4 variants, C1 – C4), but variation is constant; not in statistical control
• Model D – location and variation not constant

Statistically Stable Process vs. Controlled Stable Process

While only two cases, A1 and A2, represent processes that are in statistical control, it is possible that the other cases might be in control (controlled stable); for this, a process performance or capability study must show that all values stay within control limits, sufficiently within tolerance limits to make out-of-tolerance values exceedingly unlikely. Further, there must be procedures in place to monitor the process and take action to maintain it within the control limits.  In this situation, the SPC Manual states that Cp and Cpk can be used to measure capability, indicating that these characteristics are “in control” as in “Controlled stable”. While it is better to have a statistically stable process than one that is just “controlled stable”, it is often less costly to operate a controlled stable process in real situations.

Expanded Control Chart Guidance

The SPC Manual covers a wide variety of control chart types, with guidance on chart selection (for both Shewhart and non-Shewhart charts) and detailed descriptions of each chart type, including its description, visualization, and formulas.

Control charts can be used for retrospective analysis of past performance or for real-time, continuous process control.  The retrospective analysis chart is used to evaluate post-process stability, process performance, and capability, and to study sources of variation. SPC Charts used for reactive process control can be either process-based to minimize process spread (e.g., Shewhart and Pearson charts) or tolerance-based to best utilize tolerance (e.g., Acceptance Control Charts, Pre-Control Charts). The main difference between process-related and tolerance-related control charts is that the former are used to improve the process (and are considered preventive), while the latter are used to ensure compliance with tolerances (detection).

Other topics in the manual include the application of SPC software, documentation and reporting, including report formats, and archiving and traceability requirements.

In Summary

The new AIAG & VDA SPC Manual is a major departure from the AIAG SPC Manual (2^nd Edition) approach, although the basic concepts from that version are still valid. It is closely aligned to the ISO Statistical standards, such as ISO 11462 – Guidelines for the implementation of statistical process control. Like this ISO standard, the new manual uses SPC as a structured system to improve process knowledge, stability, variation control, and capability. The process performance and capability indices are more clearly defined and comprehended as a maturity progression with stability as a prerequisite for the capability index.  

This collaboration establishes a uniform foundation for the application of statistical methods in quality management, providing companies with a practical and internationally aligned methodology for the approval, monitoring and control of production processes. The AIAG & VDA harmonization of the core tools continues with this new manual, and Measurement Systems Analysis (MSA) will be the next core tool due for a harmonized revision.

This article was written by Paul Gambino, a simpleQuE ISO 9001 and IATF 16949® consultant and auditor with more than 20 years’ experience implementing, maintaining and improving automotive quality management systems and business processes. He shares his IATF 16949® expertise through various articles and training to help those in the automotive supplier industry understand this complex standard and its requirements.

SimpleQuE offers a full line-up of IATF 16949® training courses which includes AIAG and VDA Core Tools, Root Cause Analysis and Problem Solving, Requirements and Implementation.  With IATF also putting a major focus on internal auditor competency, it is essential to have IATF 16949® Internal Auditor Training. Contact us to learn more about the customized services offered to match your certification and training needs.

SimpleQuE is not associated with the IATF®, IAOB, ANAB®, IAQG®, and is not a certification body. SimpleQuE is an independent consulting, training, and second-party auditing service provider that assists a company on a path to obtain and maintain certification through accredited certification bodies.