BS 5700:1984

Foreword
Committees responsible
Guide
Section one. General
1.0 Introduction
1.0.1 Description
1.0.2 Objectives
1.0.3 Assignable and unassignable causes of variation
1.0.4 Decision rules and average run lengths
1.0.5 Sampling
1.1 Scope
1.2 Definitions and symbols
Section two. The choice of British Standard for
               control charting
2.1 General
2.2 Measured variables or counted attributes
2.3 Traditional or cusum?
Section three. How a control chart works: charts for
               number defective
3.1 Introduction
3.1.1 General
3.1.2 Example
3.1.3 Applications
3.1.4 Procedure
3.1.5 Sampling risks
3.2 The probability models used in BS 5701
3.2.1 General
3.2.2 The binomial and Poisson distributions
3.2.3 The binomial distribution
3.2.4 The Poisson distribution
3.2.5 Calculation of the probabilities for a range of
       outcomes
3.2.6 Application to BS 5701
3.3 Basis for decision rules
3.3.1 Basic principle of control charts for number
       defective
3.3.2 Application to control limits
3.3.3 The choice of sample size and type II risks
3.3.4 Example
3.4 Using attributes charts for controlling measured
       variables
3.4.1 General
3.4.2 Control of more than one dimension or property
3.4.3 Control of a single dimension or property (small
       proportion outside limits acceptable)
3.4.4 Compressed limits (for control of a single
       dimension or property: defective items not
       produced or not acceptable)
3.5 Control charts for defects
3.5.1 General
3.5.2 Stability of m
3.5.3 Example
3.6 Cumulative sum charts for attributes
3.6.1 General
3.6.2 Cusum and traditional similarities
3.6.3 Procedure
3.6.4 Plotting
3.6.5 Decision procedures
Section four. How a control chart work: charts for
               variables
4.1 Introduction
4.1.1 General
4.1.2 Example
4.1.3 Applications
4.1.4 Procedure
4.1.5 Sampling risks
4.2 The probability models used in BS 5702
4.2.1 General
4.2.2 Measures of location
4.2.3 Measures of dispersion
4.2.4 Distributions to describe manufacturing processes
4.3 Relative capability
4.4 Sampling and sample statistics
4.4.1 General
4.4.2 Sample averages
4.4.3 Sample range
4.4.4 Sample variance
4.5 The basis for decision rules
4.5.1 Control charts for process average (low and
       medium relative capability processes)
4.5.2 'Modified limits' control charts for process
       average (high relative capability processes)
4.5.3 Control charts for sample medians
4.5.4 Control charts for process dispersion
4.5.5 The combined average and range chart
4.6 Establishment of the 'in control' state: process
       capability studies
4.6.1 General
4.6.2 Assumptions
4.6.3 Basic procedure for process capability study
4.6.4 Relative capability
4.6.5 Further aspects of process capability studies
4.6.6 Example
4.7 Cumulative sum ('cusum') charts for controlling
       measured variables
4.7.1 General
4.7.2 Cusum charts for sample averages
4.7.3 Summary of advantages of cusum chart for sample
       averages
4.7.4 Cusum charts for sample range
Section five. Average run length curves and run length
               distribution
5.1 Traditional charts
5.1.1 General
5.1.2 Assignable cause of variation not present
5.1.3 Assignable cause of variation present
5.1.4 Example showing calculation of ARL curve for
       traditional chart with action and warning lines
5.2 ARL curves for cusum charts for sample averages
5.3 Relation of ARL curves to given processes
5.4 ARL curves for control charts for number
       defective
5.4.1 BS 5701 control charts
5.4.2 Cusum charts for attributes
5.5 Run length distributions
Tables
1 Critical test statistics for step 5
2 Example of cumulative sum tabulation for averages
3 Example of cumulative sum tabulation for ranges
4 Average run length curve
Figures
1 Control chart for number defective
2 The binomial probability distribution
3 Comparison of binomial and approximating Poisson
       distributions
4 Curves showing theoretical probability that x or
       more defectives will be found in a sample
5 Example of control chart for defectives (binomial
       data)
6 One-way and two-way charts for two-sided
       specification limits
7 Compressed limits
8 Data with cusum plot (ma = 2.5)
9 Example of cusum plot for binomial data in
       3.6.3(a), showing suitable V-masks
10 Example of cusum plot for Poisson data in
       3.6.3(b), showing suitable V-masks
11 Single-sided cusum chart for attributes (with
       V-mask)
12 Traditional control chart for a measured variable
13 Probability distribution for a continuous
       variable
14 The mean of a continuous distribution
15 The median of a continuous distribution
16 The normal distribution
17 The log-normal distribution
18 Relative capability
19 Position of action and warning lines
20 Control chart for sample averages (for medium
       relative capability processes)
21 Average chart with modified limits (for high
       relative capability processes)
22 Control chart for sample median (sample size = 5)
23 Effect of increase in process dispersion
24 Probability distribution of sample range
25 Control chart for sample range
26 Combined average and range chart
27 Relative capability
28 Probability plot to test for normal distribution
29 'Provisional' control charts for feasibility data
30 Cusum chart for sample average
31 Truncated V-mask
32 Decision procedure for V-mask
33 Generalized truncated V-mask
34 Cusum chart for production data
35 Traditional chart for production data
36 V-mask for cusum chart for sample range
37 Cusum range plot for data in table 2
38 Ideal and attainable ARL curves
39 Example on ARL calculation
40 ARL curves for control charts for sample means
41 Typical run-length probability distribution

Provides guidance on the selection of quality control chart methods for process control, complementary to BS 5701 and 5703. Explains how control charts work and gives a procedure for the determination of process capability. Also provides a basic reference standard for both industrial training and technical educational purposes.