USING SAS/QC SOFTWARE WITHIN AN INFORMATION SYSTEM FOR QUALITY DATA MANAGEMENT. Thomas Schunk, Bayer AG

USING SAS/QC SOFTWARE WITHIN AN INFORMATION SYSTEM FOR QUALITY DATA MANAGEMENT

Thomas Schunk, Bayer AG

1. Abstract

The information system LDV (Labordatenverwaltung = laboratory data management) is used at Bayer-as a-tool for quality data man-agement, as well as for the documentation and long-term monitor-ing of quality data. It runs on the IBM host usmonitor-ing the IMS data-base system and has an interface to TSO. The system incorporates facilities for calling up control charts as a graphic means in order to illustrate product quality. These charts are generated with SAS/QC.

The following facilities are available:

1) Individual and moving range charts for the study of averages and variations in quality data

2) Cumulative sum charts for the rapid detection of slight deviations from a fixed target value

3) Process capability analyses and histograms for the ex-amination of frequency distributions

2. Introduction

The term "product quality" is generally used today to denote the conformity of specific product properties with requirements, such as those imposed by customers or public authorities, for example. Product quality and proof of product quality are becoming more and more important for the manufacturing industry. This is due not only to the increasing requirements that customers are plac-ing on their suppliers and to the ever fiercer worldwidecompeti-tion but also to new product liability legislaworldwidecompeti-tion and interna-tional standards. Quality has thus become a decisive factor of competition.

A large company wishing to survive on the world market must therefore have a quality assurance system at its disposal. The chief tasks to be performed by such a system include the realiza-tion of the company~s quality strategy, the preparation and im-plementation of measures to achieve and permanently maintain a prescribed level of quality and finally, the documentation and long-term observation of product quality.

In view of the vast quantities of information that need to be processed, i t is only sensible to have recourse to an information system in this task. Bayer employs a number of different systems to this end. One of these is LDV.

3. TheLDV Information System

LDV is installed on Bayer~s IBM Host and uses IMS as database system. It supports the management and documentation of lot-based quality data worldwide. Its chief functions include:

1) Maintenance of product specifications (e.g. customer requirements)

2) compilation of analysis certificates in different languages

3) Management of samples for incoming-lot control and final product testing with intersystem communication

4) Long-term evaluation and documentation of test results 5) Accounting of completed samples

One of the chief targets is that of furnishing proof of product quality to customers. This is why LDV incorporates a submodule which will evaluate quality data in tabular (certificates) and graphic form (control charts). Graphic presentation, in particu-lar, has become increasingly important of late. .

4. Graphical Quality Documentation

So-called statistical quality control relates to the investiga-tion of variainvestiga-tions in the data observed in a bid to establish their causes and eliminate them where necessary and feasible. Of the many different methods that can be employed here, the follow-ing ones have emerged as the chief methods suitable for a company in the chemicals sector, and these are also available within the LDV system:

1) Individual and moving range charts for the study of means and dispersions

2) Cumulative sum charts (Cusum charts) for the rapid detec-tion of slight deviadetec-tions from a fixed target value and for the detection of long-term trends

3) Process capability analyses and histograms for the ex-amination of frequency distributions (in particular, his-tograms) and for checking how far specifications can be met; also the performance of statistical tests and the calculation of process capability indexes

These functions are called up via a two stage panel structure. In the first stage (Fig. 1), the user is required to enter different items of data for purposes of selecting the specific products and tests to be presented. In addition to this, the output device can be specified. Finally, the required chart type must be selected, and, following this, the second stage is called up. Here, the user has the opportunity to change specific details of the analy-sis and to determine the outward appearance of the chart. Fig. 2 gives an example of a histogram, Fig. 3 a Cusum chart and. Fig. 4 an individual and moving range chart. SAS code is then generated with the aid of these inputs and this, in turn, produces the graphics (Fig. 5 - 7). The control charts can be printed out im-mediately and are also stored in a catalog from where they can be output on different printers and plotters. This makes i t possible to trace back and compare quality data over a period of at least a year. Bayer AG Thomas Schunk AV-IM-AM 5090 Leverkusen 1 Tel. 0214/3072684 22

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...

-- Graphical Quality Documentation ---Quality Control Charts

COMMAND

===>

Lab Account

Product No.: . . . . Version: Country: .. .

Test: . . . Sequence: Text line: Color: . . . . .

Period: . . . to . . . . Lots: . . . to . . . .

Annotate lots by lot number or by date

Sort lots by lot number or by date (lid) ? (lid) ?

Type of control chart: . Choose between: h c i

h histogram c Cusum chart

i individual and moving range chart)

TermlPtr type: ...• Printer No.: . . . Quantity:

Valid colors: RED BLUE GREEN Valid types: 3192 3287 4224

Please enter initial and final dates as 'DDMMYY'

Fig. 1 Product and chart selection panel

Graphical Quality Documentation ---Process Capability Analyses

COMMAND

===>

Process capability investigations can be performed here. These divide up into three parts: a) the drawing of a histogram, b) superposition of a normal density curve of equal area on the histogram and c) tabular output of statistical data.

Specify midpoints of the histogram intervals? If "yes", enter: Lower limit: . . . . • . . . Upper limit: • . . . Class width: . . . . Draw in specifications which deviate from the default?

If "yes", enter values: . . . (lower) . . . . • . . . (upper)

Add fitted curve to histogram (Yin) ?

If parameter selection is required, specify values: Mean value: . . . Standard deviation: • . . .

Print table on statistics and process capability (Yin)?

Table for the adjusting of histogram and fitted curve (Yin) ?

Fig. 2 Histogram selection panel

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Graphical Quality Documentation

---Cusum Charts

COMMAND

===>

This type of evaluation can be used to detect level shifts in

processes which display statistical scatter. The cumulative

deviation of the measurements from a (specified) target value is

presented in a char:t. In conjunction with the so-called V mask,

it is possible to establish the point in time at which a specific

(statistically significant) shift from this target value occurs

(i. e. the shift which is to be prevented). The curve profile

itself supplies information on the level at which the process has

operated by comparison to the target value.

Target:

Standard deviation of the process (for V mask):

Shift (multiple of standard deviation):

Parameter h (Mask "window": values between 0 and 10):

Parameter k (Mask "gradient": values between 0 and 2):

Normalisation of graph using the standard deviation

(Yin)

?

Fig. 3 Cusum chart selection panel

__________________ Graphical Quality Documentation

---Individual and Moving Range' Charts

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COMMAND

===>

X and R charts are generated for individual values. The R charts

are based on so...,.called "moving ranges". The upper 'and lower

con-trol limits can be "estimated" in two different ways: via the

standard deviation

(5 ':

method for estimating process capability)

or moving range (r ,: method for maintaining quality'co,ntrol

charts). The UCL and LCL are drawn in at a

di~t~nce.of 3~s~gma

from the mean value. The upper and lower

spec~f~cat~on l~m~ts

may

be entered as required or read in from the quality database

(where available). Two run tests can be performed for the X

charts.

Method for establishing control limits

(rls)

?

Draw in specifications?

(Yin)

?

Change standard specifications? ••.•••.. (upper) ...•.... (lower)

Perform run test No. 2 or No. 3

(yy/nn)

?

How many values to calculate, the moving ranges?

No. of decimal places to ,be indicated?

output X and R charts on two separate pages

(Yin)

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Fig. 4 Individual and moving range chart selection panel

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ANALY~ICAL RESULT STATISTI~S

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PERIOD: 01/01/88 - 01/12/88

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RESULT

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TEST:

NO. ERASED WORDS/MINUTE

SPECIFICATION: 94.0 . ··-.·.~"'T~~.\"oJ.:4< -.' 87.3 .' j.-.-. "~' " ' --,. DATE: 88/01/2 TIME: 08:157 87.7 88.1

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ANALYTICAL RESULT STATISTIOS PRODUCT: RUBBER 2000 PERIOD: 01/01/88 - 01/12/88 DATE: 89/01/2 TIME: 09:02 -12.5-1, I i ( I I ,I 6 1 1 16 21 26 31 36 41 46 51 Subgroup Sizes: X "-1 Parameters: ~0-95.9337 Delta-1 «-0.0027 TEST:

NO. ERASED WORDS/MIWUTE

SPECIFICATION: 94.0

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ANALYTICAL RESULT STATISTICS PRODUCT: RUBBER 2000 PERIOD: 01/01/66 - 01/12/66 DATE: 69/01/2 TIME: 09:02 3(7' Limit. For n .. 2: UCL-97.1 S 96

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Subgroup Index (CHARGE) TEST:

NO. ERASED WORDS/MINUTE

SPECIFICATION: 94.0

Fig. 7 Individual and moving range chart