ISO 10017-2021 PDF

St ISO 10017-2021

Name in English:
St ISO 10017-2021

Name in Russian:
Ст ISO 10017-2021

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Original standard ISO 10017-2021 in PDF full version. Additional info + preview on request

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Оригинальный стандарт ISO 10017-2021 в PDF полная версия. Дополнительная инфо + превью по запросу
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Full title and description

ISO 10017:2021 — Quality management — Guidance on statistical techniques for ISO 9001:2015. This International Standard provides guidance to help organizations select appropriate statistical techniques that support the development, implementation, maintenance and continual improvement of a quality management system in conformity with ISO 9001:2015. The document is advisory and does not teach how to perform or compute the statistical techniques described.

Abstract

Guidance on selecting statistical techniques that are useful for organizations of any size or complexity when implementing or improving a QMS aligned with ISO 9001:2015. The standard maps families of statistical methods to QMS needs, highlights benefits and limitations, and gives examples of typical applications; it does not provide step‑by‑step instruction on how to apply the techniques.

General information

  • Status: Published.
  • Publication date: July 2021 (effective/published 9 July 2021).
  • Publisher: International Organization for Standardization (ISO).
  • ICS / categories: 03.120.10 (Quality management and quality assurance); 03.120.30 (Application of statistical methods).
  • Edition / version: Edition 1 (2021).
  • Number of pages: 30 pages (official ISO listing).

Scope

ISO 10017:2021 gives organisations guidance for identifying and selecting statistical techniques that can be applied to satisfy requirements of ISO 9001:2015 where quantitative methods and evidence are useful. It is intended to help determine which techniques are appropriate for particular QMS needs (e.g., measurement uncertainty, process monitoring, capability assessment, sampling, experimental design) but does not teach the mathematical or procedural steps for performing those techniques.

Key topics and requirements

  • Overview of the role of statistics in quality management and evidence‑based decision making.
  • Mapping QMS needs to families of statistical techniques and guidance on applicability (benefits, limitations and typical use cases).
  • Description and guidance (high level) for major techniques, including: descriptive statistics; design of experiments (DoE); hypothesis testing; measurement system analysis (MSA); process capability analysis; regression analysis; reliability analysis; sampling strategies; simulation and time‑series analysis; statistical process control (SPC); and statistical tolerancing.
  • Guidance on when statistical support is likely to add value to QMS activities (e.g., risk‑based decision making, performance monitoring, supplier/product acceptance).
  • Notes on limitations, prerequisites (such as data quality and measurement system adequacy), and cautions about misapplication of techniques.

Typical use and users

Used by organisations implementing or maintaining ISO 9001:2015 to help select appropriate statistical approaches. Typical users include quality managers, process engineers, data analysts, reliability engineers, internal auditors, conformity assessment bodies, consultants and trainers who need to match statistical methods to QMS requirements without receiving detailed instruction on how to perform each method. The guidance is intended for organisations of all sizes and sectors.

Related standards

ISO 10017:2021 replaces/revises the earlier technical report ISO/TR 10017 (2003). It is intended to be used alongside ISO 9001:2015 and other ISO 9000 family documents (e.g., ISO 9000, ISO 9004) and complements standards and sector specifications that reference statistical methods for quality and process control. National adoptions (e.g., AS/NZS, NBN, BS adoptions) exist that are identical or equivalent to ISO 10017:2021.

Keywords

statistical techniques; quality management; ISO 9001:2015; descriptive statistics; SPC; process capability; measurement system analysis; sampling; design of experiments; regression; hypothesis testing; reliability; simulation.

FAQ

Q: What is this standard?

A: ISO 10017:2021 is an ISO technical standard that provides guidance on selecting statistical techniques to support the implementation and continual improvement of a quality management system in line with ISO 9001:2015. It is advisory, not normative.

Q: What does it cover?

A: It covers high‑level descriptions of many statistical families (for example descriptive statistics, DoE, hypothesis testing, MSA, process capability, regression, reliability, sampling, simulation, SPC and statistical tolerancing), and explains where each family can be useful in QMS activities, together with benefits, limitations and typical applications. It does not provide procedural or computational instructions.

Q: Who typically uses it?

A: Quality professionals, process owners, engineers, data analysts, auditors and consultants who need to match statistical methods to QMS requirements and communicate those choices inside an organization. It is intended for organisations of any size and sector seeking to apply statistical thinking in support of ISO 9001:2015.

Q: Is it current or superseded?

A: ISO 10017:2021 is the current published edition (first edition, published July 2021). It supersedes the earlier ISO/TR 10017:2003. Users should check for any national adoptions or later amendments, but as of its publication the 2021 edition is the authoritative document.

Q: Is it part of a series?

A: ISO 10017:2021 is a stand‑alone guidance standard aligned with the ISO 9000/9001 family; it is not a numbered “part” of ISO 9001 but complements the ISO 9000 series by providing statistical guidance relevant to ISO 9001:2015. It is commonly used together with ISO 9001:2015 and other QMS guidance documents.

Q: What are the key keywords?

A: Statistical techniques, quality management, ISO 9001, SPC, process capability, MSA, sampling, design of experiments, regression, hypothesis testing, reliability, simulation.