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BS 5703-4:2003

BS 5703-4:2003

$189.07

Guide to data analysis and quality control using cusum techniques – Cusum methods for discrete (count/classified) data

Published By Publication Date Number of Pages
BSI 2003 58
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PDF Catalog

PDF Pages PDF Title
1 BRITISH STANDARD
2 Committees responsible for this British Standard
3 Contents
7 Introduction
1 Scope
2 Normative references
3 Terms, definitions, abbreviations and symbols
3.1 General
3.2 Additional terms, definitions, abbreviations and symbols
8 3.3 Symbols
9 4 Discrete data: cusum basics
4.1 Data classification
4.2 Classified data
11 Table 1 – Nonconforming spot weld production figures and cusum table
Figure 1 – Basic cusum plot of spot weld data
12 Figure 2 – Standard shewhart
4.3 Countable data
14 Table 2 – Printing errors and basic cusum tabulation for magazine issues
Figure 3 – Cusum plot of magazine printing errors by issue number
15 Figure 4 – Standard Shewhart
5 Cusum decision schemes for discrete data
5.1 General
18 5.2 Truncated V-mask scheme
Figure 5 – Outline features of one-sided V-mask for discrete data
19 5.3 Decision interval (DI) scheme
5.4 Fast initial response (FIR) cusum
6 Cusum decision schemes for count data
6.1 Step-by-step design approach using the British Standard table method
20 6.2 Case study of cusum design using the British Standard�table method
21 Table 3 – Table for cusum stock-out plot with V-mask
22 Figure 6 – Cusum chart with V-mask for stock-outs (
23 Table 4 – Cusum DI (decision interval) tabulation: (
24 6.3 Step-by-step design approach using a software routine
25 6.4 Case study of cusum design using the software approach
27 Table 5 – Warp knitting plant data: cusum tabulation
28 Figure 7 – Cusum plot of fabric data with “up” and “down” truncated V-masks
Figure 8 – Decision interval plot of fabric data with upper and lower control limits
29 7 Cusum decision schemes for classified data
7.1 Design approach using the British Standard table method
30 Figure 9 – Cusum plot of spot weld data with V-mask having
Table 6 – Decision interval (DI) tabulation of spot weld data
31 Table 7 – ARL performance for a binomial cusum scheme with a sample size = 50 and target value,
7.2 Design approach using a software routine
33 Table 8 – Audited product cusum
34 Figure 10 – Audited product cusum plot with asymmetrical truncated V-mask for up and down movement
35 Figure 11 – Cusum plot of audited product with upper and lower control limits
8 Design of cusum schemes for low rates of occurrence of events
8.1 Introduction
36 8.2 Case studies
Table 9 – ARLs for 1 % and 4 % proportions in terms of
Table 10 – ARL performance of two
37 Table 11 – Cusum schemes in terms of selected ARLs and in- and out-of-control probabilities
Table 12 – ARL performance of two
41 Annex A (normative) Tests for validity of Poisson or binomial distribution assumptions
A.1 General
A.2 Test by comparison of observed and theoretical variances
42 Table A.1 – Critical values of variance ratio for testing binomial or Poisson distribution assumptions
43 A.3 Successive difference test
44 Annex B (normative) Cusum design schemes for count (Poisson) data
Table B.1 – C1 and C2 Schemes for count (Poisson) data in terms of
45 Table B.2 – ARL characteristics for cusum schemes in terms of
46 Table B.3 – ARLs for low rate schemes with
47 Table B.4 – ARLs for schemes with
48 Table B.5 – ARL data for schemes with
49 Annex C (informative) Tabular cusum design schemes for binomial data
Table C.1 – C1 and C2 schemes for binomial data
50 Table C.2 – ARL data for binomial variables design scheme
Table C.2a) – C1 design schemes with
51 Table C.2b) – C2 design schemes with
Table C.2c) – C1 design schemes with
52 Table C.2d) – C2 design schemes with
Table C.2e) – C1 design schemes with
53 Table C.2f) – C2 design schemes with
Table C.2g) – C1 design schemes with
54 Table C.2h) – C2 design schemes with
Table C.2i) – C1 design schemes with
55 Table C.2j) – C2 design schemes with
Table C.3 – ARLs for low rate schemes with

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BS 5703-4:2003
$189.07