SSCM 336 Case Study
The “Case study: the use of statistical process control in fish product packaging” by Grigg et al.
(1998) demonstrates how SPC systems can be established and operated in a simple yet effective
manner to reduce quality errors and improve customer confidence. Please carefully read through
the case and make appropriate analysis to prepare a case report.
The case report is individual assignment.
The written report should be around 4 -6 pages with the 12 Times New Roman font and
double-space paragraph.
The report should demonstrate an in-depth written analysis of the case. The case report is
expected to:
o Describe the background and rationale of why to use SPC in the case company
o Explain the steps used to establish the SPC system in the case company
o Discuss the outcome and benefits of using SPC in the case company
o Critique the use of SPC in the case company, such as whether SPC was applied
properly, whether there are any potential problems in this SPC system, and what
suggestions you can give to improve the SPC system in the packing process. Try
to link the concepts and methods discussed in class (can be beyond SPC and
include other quality management tools) with your analysis.
The report should be written in paragraphs and include a proper introduction, proper
transitions between sections (include section titles if needed), proper conclusion/closing
statement, and proper citations of APA style in a reference list when applicable.
Please refer to the case report grading rubric for guidance.
Reference:
Grigg, N.P., Daly, J., and Stewart. M. (1998). Case study: the use of statistical process control in
fish product packaging. Food Control, 9(5), 289-297.
GRADING RUBRIC FOR CASE STUDY REPORTS
Objective/Criteria
Unacceptable
(0 points)
Poor
(1 points)
Average
(2 points)
Excellent
(3 points)
Identification of Main
Issues/Problems
Applied if there is
no report
Identifies and describes an
acceptable understanding of
some problems in the case
study
Identifies and describes a
comprehensive and accurate
understanding of most
problems in the case study
Identifies and describes a
comprehensive and accurate
understanding of the main
issues in the case study
Analysis and Evaluation
of Issues/Problems
Applied if there is
no report
Provides an incomplete
analysis of some of the
identified problems
Provides a thorough analysis
of most of identified
problems
Provides a thorough analysis
of all identified problems
Recommendations on
Effective
Solutions/Strategies
No suggestions or
applied if there is
no report
Gives out little actions and
suggestions
Gives out a somewhat one-
sided view and supports
diagnosis/opinions with
limited details and evidence
Gives out a critical view and
supports diagnosis/opinions
with well documented
evidence
Links to Course Readings
and Additional Research
No connection or
applied if there is
no report
Makes inappropriate or little
connection between issues
identified and the concepts
studied in the class
Makes appropriate but
somewhat vague connection
between issues identified
and the concepts studied in
the class
Makes appropriate and
powerful connection
between issues identified
and the concepts studied in
the class
Writing Mechanics and
Formatting Guidelines
Applied if there is
no report
Writing is unfocused with
unclear expression and
incomplete sentences; lack
of organization of ideas;
contains serious spelling and
grammar errors
Generally well-written with
complete sentences and
ordinary wording;
organization is a bit unclear;
there are a few spelling and
grammar errors.
Well-written with complete
sentences and appropriate
wording; Display a clear
organization of ideas;
contains no spelling and
grammar errors
ELSEVIER PII: SO956-7135(98)00018-S
Food Control, Vol. 9, No. 5, 289-297, 1998 PP.
0 1998 Elsevicr Science Ltd
All rights reserved. Printed in Great Britain
0956-7135198 $lY.OO+O.OO
PAPER
Case study: the use of
statistical process control in
fish product packaging
Nigel P. Grigg,“’ Jeannette Daly*
and Marjorie Stewart?
The DTI publication ‘Code of Practical Guidance for Packers and Importers’
provides detailed advice on the establishment of effective statistical process control
(SPC) systems to ensure ejficient and effective compliance with the requirements
of the Average System for food and drink organizations. Since this document is
non-mandatory, however; many organizations opt not to adopt such systems, and
rely instead upon their checkweigher as a last line of defence. In many cases, this
is because of the apparent complexity of the systems to the non-statistically
trained. This paper presents a case study which demonstrates that simple, manual
SPC systems involve very little statistical knowledge to establish and operate. Such
systems, it is argued, can reduce unnecessary checkweigher rejections and product
giveaway, assist with Trading Standards inspections and improve customer confi-
dence. 0 1998 Elsevier Science Ltd. All rights reserved
Keywords: The Average System; statistical process control; fish product manufacture
NOMENCLATURE
DTI Department of Trade and Industry.
Non-Standard package
Any food package having a weight below the absolute
tolerance limit, T,.
Inadequate package
Any food package having a weight below the toler-
ance limit, T,.
Qn Nominal (declared) Quantity (or weight)
under the Average System. The quantity
marked on a container of package.
Qt Target Quantity (or weight). The average
quantity to which a packing or filling line
operation is intended to produce.
SD Text abbreviation for standard deviation.
*Department of Consumer Studies, Glasgow Caledonian
University, Park Campus, 1 Park Drive, Glasgow, U.K.
G3 6LP and +Swankie Food Products Ltd, Baden-
Powell Road, Kirkton Ind. Est., Arbroath, U.K.
DDll 3LS. *Corresponding author. Tel: (0141) 337 4000;
Fax: (0141) 337 4420; e-mail: [email protected]
(5
s
so
SP
SPC
TNE
TSO
T
T2
Population standard deviation.
Sample standard deviation.
Short-term standard deviation.
Medium-term standard deviation.
Statistical process control.
Tolerable Negative Error. The negative
error in relation to a particular nominal
quantity, as defined by the Weights and
Measures Act, 1979.
Trading Standards Officer.
Tolerance Limit. A defined quantity below
which, according to the requirements of
the Average System, no more than 2.5% of
package weights may legally fall. It’s value
is equal to the nominal quantity minus the
tolerable negative error: ie Q, – 1TNE.
Absolute Tolerance Limit. A defined
quantity below which, according to the
requirements of the Average System, no
package weight may legally fall. It’s value
is equal to the nominal quantity minus
twice the tolerable negative error: ie
Q. – 2TNE.
Food Control 1998 Volume 9 Number 5 289
Case study: N. P. Grigg et al.
INTRODUCTION
Quality control and statistical process control (SPC)
have, for several decades, been synonymous in most
high volume manufacturing environments, yet rela-
tively little is written on the successful application of
SPC within the food industry. This is surprising, given
the benefits which SPC can provide in terms of
economic, predictive and systematically documented
process control (eg Gaafar and Keats, 1992; Xie and
Goh, 1993; Wu, 1994; Cartwright, 1995; Holmes,
1996). In a recent Food Control paper, Hayes et al.
(1997) highlighted the role which SPC can play in
relation to food safety. This paper aims to highlight
the role of SPC in packaging control.
Typically, weights and measures (W&M) control
has less of a high media profile than food safety, but
the underfilling of packages is also a significant issue
in consumer protection, since the consumer takes it
on trust that purchased food products are of the
stated weight. In addition, for the food producer,
there are potentially significant financial considera-
tions. Quality costs can be considered under three
main headings, namely, prevention, appraisal and
failure (Crosby, 1979; BS6143, 1992; Oakland, 1997).
Weight control falls into two of these categories:
appraisal, in terms of monitoring product and compo-
nent weights during process control, and failure, in
respect of product giveaway and underweight
products. Examples of failure costs would be the cost
per unit of giveaway, the cost of any packaging
material discarded in a rejection/repack situation (eg
the bursting out of sachets of dehydrated quick soups,
in an attempt to salvage the contents where inade-
quate packages have been manufactured), or the cost
of any litigation under the Weights and Measures
Act, 1985.
Whilst failure costs can, in some cases, be traded
off against the costs of establishing and maintaining
effective control systems (prevention costs), there are
other, less tangible, benefits associated with SPC
which make the investment worthwhile. Such benefits
include the systematic recording of quality data, the
possibility of predictive control, allowing corrective
action to be taken proactively, and the provision of
confidence to customers (eg large retailers) that an
effective system is in place.
In recognition of the benefits which SPC systems
can accrue to food packing environments, the DTI
outlines the application of such systems in it’s Codes
of Practice for packers (DTI, 1979a) and inspectors
(DTI, 1979b). These recommendations are designed
to enable the packer of food products to routinely,
systematically and demonstrably meet the require-
ments of the Average System. In spite of this,
however, many packers do not make use of the tech-
niques described, many relying instead upon check-
weighers to detect underweight items. In a recent
pilot survey of 200 U.K. food manufacturers, under-
taken at Glasgow Caledonian University, out of 71
responding organizations representing a variety of
product types, 46 (65%) had a formal documented
quality system covering W&M control, but only 24
(34%) made use of SPC in this area.
This paper presents a case study of a medium-
sized fish product producer based in Scotland,
Swankie Food Products Ltd. The organization uses a
pre-checkweigher SPC system, designed around the
guidelines presented in the DTI (1979a) Codes of
Practice. The system is manual, and is simple and
effective, requiring neither specialist statistical know-
ledge nor any computer software. Only the equivalent
input of one member of the staff is required to
operate the system, but there are quantifiable cost
benefits in terms of the control of product overfill
and the less easily quantified costs of the conse-
quences of product underfill. This paper aims to
demonstrate the practical simplicity of the system
used, and present the benefits that such systems may
thus accrue, regardless of the complexity or sophisti-
cation of the system. It is hoped that this might
encourage more organizations to consider adopting
such a system.
LEGISLATION AND ENFORCEMENT
In the U.K., weights of packaged goods are
controlled according to the Average System, the
requirements of which are encapsulated under the
‘three rules for packers’, which state that for any
given sample of packages taken for testing by a
Trading Standards Officer (TSO):
Rule 1
Rule 2
Rule 3
The actual contents of the packages shall
not be less, on average, than the declared
nominal quantity.
Not more than 2.5% of the packages may
be non-standard, ie have negative errors
larger than the Tolerable Negative Error
(TNE)* specified for the nominal quantity
<Q.>.
No package may be inadequate, ie have a
negative error larger than twice the speci-
fied TNE.
Figure I is a schematic representation of the
average system, showing the relationship between Qn
and the tolerance limits T, and T2 for a Normally
Distributed filling or packing process*. Table 1 shows
the TNE values for associated Q. values.
*TNE values for given nominal or declared quantities are
tabulated in DTI (1979a).
*Note that in the parent distribution of packages, there is a
limit of one package in 10,000 applied to the permitted
number of inadequate packages, as opposed to none at all,
as in the three rules. This is to compensate for the theo-
retically infinite tails of the normal distribution and to
allow for pragmatic enforcement.
290 Food Control 1998 Volume 9 Number 5
Case study: N. P. Grigg et a/.
Non-standard
14
Inadequate items
b
2TNE
Q” Nominal
Quantity
(max 0.01%)
Figure 1 Relationship between package weight distribution and Average System tolerance limits
THE CODE OF PRACTICE
This document (DTI, 1979a) sets out the require-
ments of the Average System, and the attendant
responsibilities of packers and importers. Appendix C
of the Code is a detailed exposition on the applica-
tion of effective SPC on packaging operations. The
system and theoretical background are rigorous, and
can be daunting to those without prior training in
statistical theory. In recognition of this, the document
also presents an alternative ‘Off-the-Peg Control
System’, designed to be straightforward with a
minimum of theory, and applicable to ‘widely varying
products and processes’ (Appendix F, DTI, 1979a).
The following case describes the practical operation
of this system, and the benefits which have resulted
for the organization.
CASE STUDY: W&M CONTROL IN FISH
PRODUCT MANUFACTURING
The sequence of activities required to establish the
off-the-peg system come under the following broad
headings:
(1) Collection of data.
(2) Assessment of process characteristics.
(3) Calculation of target fill quantity.
Table 1 Tolerable negative errors (DTI, 1979a,b)
Nominal quantity Tolerable negative error (TNE)
(Q.1 g or ml
as % of Q. gorml
5-50 9 –
50-100 4.5
100-200 -4.5 –
200-300 9
300-500 -3 –
500-1000 – 15
1000-10000 1.5
10000-15000 150
above 15000 1
Reproduced with the permission of the controller of Her Majesty’s
Stationery Office.
(4) Construction of control charts.
(5) On-line control using charts.
Collection of data
The first stage in establishing this or any other such
system is to accurately determine the average fill level
and the inherent variability of the packing process
under current manufacturing conditions.* This stage
is normally referred to in statistical quality control as
an initial study. The data for this Case Study are
shown in Table 2. A minimum of 200 packages are
required, to be collected in subgroups of size 3 or 5.
The subgroup size and sampling rate are left to the
packer to select on the basis of production levels.
Swankie opted to use subgroups of size 5, taken at
half-hourly intervals, making a total of 40 subgroups.
As shown, for each subgroup (i), the mean (denoted
X), the standard deviation (hereafter abbreviated to
SD and denoted by the symbol ‘s’), and the range
(the difference between the largest and smallest
subgroup weights, and denoted ‘R’) have been
obtained.
Calculation of process characteristics
The overall mean fill is the average =of the 40
subgroup means. This value is denoted x (mean of
means), and its value for this data is 363.54 g. The
individual subgroup means will be required later for
plotting on the X chart.
The variability of a process is normally measured
using SD. In the detailed system (Appendix C of
DTI, 1979a), two measures are required; short-term
SD (denoted s,,) and medium-term SD (denoted sp).
The value for s,, represents normal piece-to-piece
*Although an accurate assessment is sought, the data
should be taken under conditions as ‘ideal’ as possible. It is
not recommended to set up control charts on the basis of
out-of-control data, as this will render the process charac-
teristic estimates inaccurate for ‘normal’ production.
Food Control 1998 Volume 9 Number 5 291
Case study: N. P. Grigg et al.
variation, whereas s, incorporates the effects of any
additional shift-to-shift fluctuations. In the off-the-
peg system, for practical simplicity, one measure of
variability suffices. All necessary calculations can be
carried out using either average subgroup SD (S), or
average subgroup range (R). For the case study
dataset (Table 2) it can be seen that the two values
are respectively 5.06 g and 12.80 g. Since in this
system process variability can be estimated using R,
then it is not always necessary to calculate SD at all.
For some manual systems, this will come as good
news to the process controller, since this is not a
trivial calculation, and can lead to errors (such as
simple miscalculations or failure to apply the
correction for small samples).
The process parameter estimates thus obtained are
required for the construction of control charts, and
the determination of the target quantity described in
the next section. All remaining calculations are there-
fore based upon these measures. The appropriate
calculations will depend upon which process varia-
bility measure (s or R) has been calculated. For the
purposes of this article, calculations using both
measures shall be used, to give the reader an indica-
tion of the degree of error involved between the two
methods.
Determination of minimum target quantity (Q,)
The minimum ‘target quantity’, denoted Qt, is the
minimum level at which the filling or packing process
should be set in order to consistently meet the
requirements of the Average System regulations (ie
the three rules for packers). As shown in Figures 2
and 3, the value of Qt will depend upon the process
SD. For any given Qn, there is an associated ideal
value of SD which will exactly meet the requirements
for non-standards and inadequates. The ideal distri-
bution with this SD is referred to in the diagrams as
the ‘W&M requirements distribution’. As shown in
Figure 2, for any actual process with a SD at or below
this value, Qt can be set equal to Qn (its lowest
possible value).
As Figure 3 shows, however, the larger the process
SD, the more packages will cross the lower limits of
T, and T,. In this case, Q, must be set larger than Q.,
Table 2 Net weight data set (Q. = 350 g, all weights in g)
Batch Sample
i
Mean Std Dev
ii si
Range
Ri
364 361 360 361 360
362 368 361 364
364 358 361 361
362 361 367 358
365 361 369 361
364 362 360 359
359 360 361 365
351 362 368 364
361 365 361 363
363 358 362 360
369 360 363 362
358 363 362 362
364 361 362 365
371 363 362 362
358 359 364 357
380 361 348 364
354 377 362 353
365 351 367 363
361 365 363 363
371 364 367 365
361 360 372 366
374 370 367 372
377 374 364 371
375 374 310 362
362 370 367 367
316 377 362 382
367 358 366 350
356 352 360 357
360 351 353 359
361 355 365 357
364 362 356 360
365 377 372 351
371 365 364 313
366 370 365 367
370 360 364 365
361 365 361 357
357 360 370 363
382 354 361 373
361 358 372 377
358 356 358 360
361 1.6
3.2
2.8
3.4
6.7
3.0
4.0
4.1
3.0
2.0
3.5
3.2
3.1
4
8 2 360
3 357
363
360 1
364 362 9
18
8
9
11
8
5
9
9
8
9
351
356
361
360 1 6
358 362
363
361
361
364
362
8 365
9 357
10 362
11
12
366
367
357
369
360
1
2 13 362
14
15
365 4.3
360 2.7
16 367
17 352
18 376
19 366
20 369
21 354
22 374
23 376
24 365
25 364
26 377
27 354
28 363
29 369
30 369
31 361
364 11.5 32
360 10.5 25
364 9.0 25
364
367
363
1.9
2.9
6.8
3.0
1s
371
372 5.2 13
369 5.6 13 3
366 3.1 8
375
359
7.5
1.4
20
17
358
358
361
361
367
367
367
363
362
362
365
368
361
: = 363.54
4.2 I1
7.1 18
5.7 14
30.0 8
26
13
5
13
IO
5 32 371
5 33 360
10.0
5.3
34 365
357
358
358
357
370
371
2.1
5.0
4.3
5
5
35
36
37
38
39
40
5.2 13
11.8 28 5
5
5
7.9 19
6.0 15
s = 5.06 R=l 2.80
292 Food Control 1998 Volume 9 Number 5
Case study: N. P. Grigg eta/.
Figure 2 Q,, = Q,: 3 Rules Met
Qn= Q, /’
/
b = W&M requirements distribution
and its lowest possible value must be mathematically
obtained.
Theoretically, based upon the requirements of the
packers rules, for a known medium term SD,* Q, is
equal to the largest of the values resulting from the
following expressions:
Q,# (1)
T,+I .96s,, (2)
T2+3.72s,, (3)
Expression (1) ensures that the target value is at
least equal to the nominal quantity. (2) ensures that
*The medium term SD figure s, is used in this instance
because it will allow for shift-to-shift variation.
no more than 2.5% of packages will fall below T,, and
(3) ensures that no more than 1 in 10,000 packages
will fall below T,, for any given s,. The multiplying
factors used (1.96 and 3.72) are fixed constants under
the normal distribution, representing the number of
SDS limiting 2.5% and 0.01% of the curve. To aid
clarity, the situation is represented schematically in
Figure 4.
In the off-the-peg system, because practical simpli-
city is sought, the multipliers involved in formulae
l-3 are changed so that S or l? can be used to obtain
Qt, instead of s,, which is harder to obtain. The new
multipliers are given below in Table 3. For the sample
data, Q. is 35Og, and therefore (from Table 2) the
TNE is 3% of 350 g = 10.5 g. Hence T, = 339.5 g, and
Qn – Q,?
a = Filling Distribution
b = W&M requirements distribution
Figure 3 Q, > Q,,: Q, to be obtained
Food Control 1998 Volume 9 Number 5 293
Case study: N. P. Grigg et al.
Ts,-W
Target
I
3.72 sp
b
~ Quantity
(Unknown)
Figure 4 Finding Q, from T, and T,
Tz = 329.0 g. Table 3 shows the resulting values for Q,
calculated using both S and R.
The optimum value for (2, is therefore 352.76 g
(based upon S), or 353.07 g (based upon R). Once the
optimum target quantity is thus established, this then
becomes the optimum value for the filling process,
and is used as the basis for the centreline of the
mean control chart.
Use of on-line process control charts
mean and range or SD (eg Gaafar and Keats, 1992;
Xie and Goh, 1993; Wu, 1994). ‘Mean’ charts are
used to control the average value of a key process
variable such as package weight, and ‘Range’ or ‘S’
charts to control the variability of the same measure-
ment. The charts (of which Figures 5 and 6 are
examples) consist of a centreline, relating to the
overall average value of the sample statistic, and
between one and four control limits, representing
(broadly) minimum and maximum allowable values
for that measurement.
The Code of practical guidance describes three types
of on-line control chart which can be used by the
packer or filler. These are:
(1) Original value plots;
(2) Mean and Range Control Charts;
(3) Cusum Charts.
This paper will discuss only mean and range
control charts. The reader is referred to the Code of
Practice for a discussion of the other methods.
These charts, standard in much of industry, are
used to monitor and control sample statistics such as
For average weight charts, lower limits control
underweight items and upper limits control overfill.
As stated in the introduction, although underweights
are of primary legal importance, overfill is to be
avoided in food packing for economic reasons. It is
therefore of benefit to the packer to use both upper
and lower limits for this chart.
Table3 Formulae for obtaining Q, from s or R (adapted from
DTI, 1979a)
Using & Using J7
Rule 1
Formulae Q,,+0.49 S Q,,+O.2 l?
Value = 35og+o.49 (5.06g) =35Og+O.2 (12.8Og)
= 352.48 g = 352.56 g
Rule 2
Formulae T,+2.62 S T,+1.06 f?
Value = 339.5 g+2.62 (5.06 g) = 339.5 g+1.06 (12.80 g)
= 352.76 g = 353.07g
Rule 3
Formulae T2+4.45 9 Tz+1.8 R
Value = 329 g+4.45 (5.06g) =329g+1.8(12.8Og)
= 351.52 g = 352.04 g
For weight variability charts, upper limits are of
more relevance, since the variability of values should
be kept from becoming too great, but low values are
to be welcomed. Weight variability is dependent on a
number of factors and can be extremely product
specific. The most common factors are those of
equipment capability and specific product character-
istics, eg sauce viscosity, the presence or absence of
discrete particulate materials and the number of
discrete components within the finished product.
Both types of chart are normally necessary in prac-
tice, because, whilst average weight might be under
control and stable, process variability could be simul-
taneously increasing, resulting in increasing overfill
and underfill, which would be undetectable from the
mean chart alone. To exemplify this situation,
consider two samples of size 3, the first consists of the
values 249 g, 250 g and 251 g, and the second consists
of the values 240 g, 250 g and 260 g. Both samples
294 Food Control 1998 Volume 9 Number 5
Case study: N. P. Grigg et al.
380
345
Figure 5 .? chart for net weight data set
Centreline
Upper/Lower Control Limits -ae-v–*-_
clearly have the same mean, which if treated in isola-
tion fails to account for the underlying variation.
Apart from alerting the packer to actual violations
of control limits, the other main function of these
charts is predictive. They can be used to reveal trends
in subsequent samples, which alert the controller to
potential violations, and non-random variation. W.
Edwards Deming, the pioneer of such methods, splits
variation into common causes and special causes
(Deming, 1986). Common causes are random and
35
30
25
20
15
10
5
0
unavoidable, whereas special causes are due to
correctable faults such as machine drift or shift-
to-shift variation. Using control charts, acceptable
limits can be established on common causes, and
special causes can be identified for corrective action.
Construction of charts
In the off-the-peg system, charts are constructed
using only one control limit. On mean charts, this is a
Centreline
Upper Control Limit -c——-
Figure 6 Range chart for net weight data set
Food Control 1998 Volume 9 Number 5 295
Case study: N. P. Grigg et al.
Table 4 Control limit multipliers and values (adapted from DTI,
1979a)
Using 5
Upper/lower control limit for mean (x) chart
Formulae Q,,,.,., 2 1.43 3
Value for dataset = 352.76 g + 1.43
(5.06 8)
Upper limit = 360.00 g
Lower limit = 345.52 g
Upper control limit for range (R) chart
Formulae 2.29 S
Value for dataset = 2.29 (5.06 g)
Limit = 11.59 g
Using li
Q,,.,,., kO.58 ti
= 353.07 g + 0.58
(12.80 g)
= 360.99 g
= 345.65 g
2.36 I?
= 2.3h (12.80 g)
= 30.2 g
lower limit to detect underfill, and on variability
charts, this is an upper limit to detect high variation
only. The authors of this article recommend using
both upper and lower limits on the mean chart, and
so both are used here.
Table 4 shows the formulae used to derive the
limits, and the associated values, from the dataset.
Again, the value for s or l? is merely substituted into
the appropriate equation to obtain these limits,
depending upon which statistic has been calculated.
The reader will observe that the limits for the
mean chart do not differ significantly, regardless of
which measure of variability is used. The difference
in the range chart limits are merely due to the fact
that the range is a larger value than the SD. This,
therefore, requires a higher limit for its control.
The final stage of the analysis is the setting up of
the control charts, using the limits obtained above,
and the sample data in Table 2. The sample data are
plotted on the appropriate chart, as shown in Figures
5 and 6. In the charts shown, R has been used to
obtain the limits.
Comments on charts
The mean chart shows that a high level of giveaway is
present in this product. Since the product is battered
fish, it is the less expensive batter which is being
oversupplied. Under normal circumstances, the
packer might decide to adjust the mean level,
resample and recalculate all the values described
above before beginning real-time control with the
charts. The chart also shows an apparent shift varia-
tion from sample 21, after which means fluctuate
more than previously. Such non-random or unusual
variation should be investigated before continuing to
use the chart, in case non-standard data has been
included.
The reader will note the presence of an out-of-
control value on the Range chart (Figure 6). This
value relates to sample number 16 (Table 2), which
shows a SD of 11.5 g, and range of 32 g. The lowest
value in the sample is 348 g, which does not represent
a non-standard nor inadequate item, but there is a
high value of 380 g, representing 30 g of giveaway.
This is followed by two further high values, suggesting
that it is unlikely to be a purely random occurrence.
This should represent a significant concern to the
producer of a relatively expensive product like fish.
Normal procedure under such conditions would be to
investigate the non-conforming sample in order to
ascertain the reason for non-conformance, to then
remove the sample from the data set, and finally, to
repeat all stages and calculations of the initial study.
The reason for this is that a sample with a mean or
range value beyond normal production values will
tend to influence the calculated values for overall
mean and SD or average range. This will then influ-
ence all subsequent calculations of target value and
control or limits. A recalculation has not been carried
out for this article but would, in normal, circum-
stances be recommended strongly.
Use of charts
With the charts established, sample data are now
taken on an on-line basis until limits require to be
recalculated. The organization has to decide upon
what action should be taken in the event of an out of
control signal being detected. It is not sufficient to
merely record non-conforming sample values. Such
action should be written into a documented
procedure as part of the organization’s quality
system.
Benefits of the system
The benefits which Swankie Food Products Ltd
experience as a result of these activities include
reduced giveaway and unnecessary rejections at the
checkweigher stage, since package weights are
controlled prior to this stage. The systematic and
explanatory records which are kept help to facilitate
Trading Standards inspections, and can be shown to
customers as evidence of Good Manufacturing
Practice.
CONCLUSION
The case study in this article is designed to demon-
strate the simplicity of application of the off-the-peg
system. It is hoped that food organizations not
presently applying SPC may by reading this article
and become aware of the ease with which they might
apply a working system, regardless of their particular
specialism, through reference to the Code of Practice.
By establishing and using such an SPC system, the
food packer can provide confidence to their
customers that they are unlikely to violate any of the
packers rules, fail the reference test, and thus be
liable to prosecution under the Act. The organisation
can also assist TSOs with routine inspections through
the provision of high quality records (datasheets,
calculations and control charts). Such a system can
also reduce giveaway and help the packer to maintain
the lowest possible legal target quantity.
296 Food Control 1998 Volume 9 Number 5
Case study: N. P. Grigg et al.
ACKNOWLEDGEMENTS DTI, (1979a) Code of Guidance for Puckers and Importers. Depart-
ment of Trade and Industry, HMSO, London
The authors gratefully acknowledge the comments
and suggestions received from the two reviewers of
this paper.
DTI, (1979b) Manual of Practical Guidance for Inspectors. Depart-
ment of Trade and Industry, HMSO, London
Gaafar, L. and Keats, B. (1992) Statistical process control: A guide
for implementation. International Journal of Quality and Relia-
bility Management 9(4), 9-20
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Food Control 1998 Volume 9 Number 5 297
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