Statistical Process Control 1 STATISTICAL PROCESS CONTROL by XXXXXXXX Student ID: 2XXXXXXX University of Northampton (Amity Global Institute Pte Ltd, Singapore) Managing Operations and The Supply Chain Dr. Melvin Goh BSOM046 BSOM046-SUM-1920-ES1-Statistical Process Control 18 Oct XXXX Word Count: 1600 (± 50) Statistical Process Control 2 Table of Content 1. Introduction………………………………………………………………….3 2. Literature Review……………………………………………………………3 3. Methodology…………………………………………………………………5 4. Case Study Analysis…………………………………………………………9 5. Recommendation…………………………………………………………….15 6. Conclusion…………………………………………………………………...17 7. References……………………………………………………………………18 8. Appendix……………………………………………………………………..22 Statistical Process Control 3 STATISTICAL PROCESS CONTROL INTRODUCTION This report will provide a literature review of the concept and relevance of statistical process control (SPC) from its inception until the present day. A case study of Waterside’s Leather Limited (WLL) using the temperature data of its combined effluent discharge over one hundred and twenty days will be conducted, and a recommendation will also be proposed. LITERATURE REVIEW Man has always tried to imitate and better his competitors to develop a better and cheaper product or service. This idea was as crucial for the hunter-gatherer as it is for the manufacturing industry after many millennia. This awareness led to the requirement of apprentices having to follow in the footsteps of the master craftsmen for many years until they could become masters in their craft. However, this was not a scientifically tabulated and monitored process. Bradford and Miranti (2019) state that “it was in 1924 that Walter A. Shewhart introduced the use of control charts to evaluate data distribution patterns to determine whether manufacturing processes remain under control at Bell Telephone Laboratories”. He also introduced the terms of variation in the process which comprises of common cause and special cause variation (Subhabrata and Marien, 2019). SPC is a technique for controlling processes to distinguish causes of variation and signal for corrective action (Chen 2005 cited in Avakh and Nasari 2016). While some say that “SPC is the use of statistically based tools and techniques principally for the management and Statistical Process Control 4 improvement of processes” (Stapenhurrst, 2005), others say that “SPC is not really about statistics or control, it is about competitiveness” (Oakland and Oakland, 2018). Figure 1: A typical Control Chart (Graph from https://learning.oreilly.com/library/view/nonparametric-statistical-process/9781118456033/c02.xhtml#head-2- 18) The USA War Department used these methods to enhance the quality of products during World War II. W.E Deming used Shewhart’s cycle in his quality training in Japan in 1950 but made a new version stress.

1. Statistical Process Control 1
STATISTICAL PROCESS CONTROL
by XXXXXXXX
Student ID: 2XXXXXXX
University of Northampton
(Amity Global Institute Pte Ltd, Singapore)
Managing Operations and The Supply Chain
Dr. Melvin Goh
BSOM046
BSOM046-SUM-1920-ES1-Statistical Process Control
18 Oct XXXX
Word Count: 1600 (± 50)

2. Statistical Process Control 2
Table of Content
1.
Introduction…………………………………………………………
……….3
2. Literature
Review……………………………………………………………3
3.
Methodology…………………………………………………………
………5
4. Case Study
Analysis…………………………………………………………9
5.
Recommendation……………………………………………………
……….15
6.
Conclusion……………………………………………………………
……...17
7.
References……………………………………………………………
………18
8.
Appendix………………………………………………………………
……..22

3. Statistical Process Control 3
STATISTICAL PROCESS CONTROL
INTRODUCTION
This report will provide a literature review of the concept and
relevance of statistical process
control (SPC) from its inception until the present day. A case
study of Waterside’s Leather
Limited (WLL) using the temperature data of its combined
effluent discharge over one hundred
and twenty days will be conducted, and a recommendation will
also be proposed.
LITERATURE REVIEW
Man has always tried to imitate and better his competitors to
develop a better and cheaper
product or service. This idea was as crucial for the hunter-
gatherer as it is for the manufacturing
industry after many millennia. This awareness led to the
requirement of apprentices having to

4. follow in the footsteps of the master craftsmen for many years
until they could become masters
in their craft. However, this was not a scientifically tabulated
and monitored process.
Bradford and Miranti (2019) state that “it was in 1924 that
Walter A. Shewhart introduced the
use of control charts to evaluate data distribution patterns to
determine whether manufacturing
processes remain under control at Bell Telephone Laboratories”.
He also introduced the terms
of variation in the process which comprises of common cause
and special cause variation
(Subhabrata and Marien, 2019).
SPC is a technique for controlling processes to distinguish
causes of variation and signal for
corrective action (Chen 2005 cited in Avakh and Nasari 2016).
While some say that “SPC is
the use of statistically based tools and techniques principally
for the management and
Statistical Process Control 4
improvement of processes” (Stapenhurrst, 2005), others say that
“SPC is not really about

5. statistics or control, it is about competitiveness” (Oakland and
Oakland, 2018).
Figure 1: A typical Control Chart
(Graph from
https://learning.oreilly.com/library/view/nonparametric-
statistical-process/9781118456033/c02.xhtml#head-2-
18)
The USA War Department used these methods to enhance the
quality of products during World
War II. W.E Deming used Shewhart’s cycle in his quality
training in Japan in 1950 but made
a new version stressing the permanent interaction of design,
production, sales and research
(Saier, 2017). According to Brownstein et al. (2019), “Expert
knowledge is information; to
ignore it or fail to obtain it incurs a considerable opportunity
cost. Judgements should be as
objective as possible and based on data when available.
Anything less is unscientific. Yet,
deciding what data are relevant always involves degrees in
judgement”. The Japanese fully
incorporated SPC into their workspace and manufacturing,
which led to enhanced production

6. and quality of the products from Japan.
SPC, however, cannot be imitated from one company to another
as it requires an in-depth study
and analysis of the process of the individual organisation and
this led to the development of the
Six Sigma program by Bill Smith from Motorola in 1986. The
Six Sigma technique become
Statistical Process Control 5
famous under Jack Welch from General Motors, who
incorporated the program into General
Motors and famously saved it 12 billion dollars within five
years after its implementation.
METHODOLOGY
Process capability is a measure of acceptability of variation of
the process. Cp signifies the
capability of the process within the specified limits.
Cp = (USL-LSL)/ 6σ
where USL = Upper Specification Limit
LSL = Lower Specification Limit

7. σ = Standard deviation
Cp is the number of times the process spread fits into the
specification limits; hence, the process
is more capable with a higher value of Cp. Hence, if Cp>1 then
the process is said to be capable,
whereas if Cp < 1 then the process is not capable. However, Cp
will only tell us if the data fits
within the UCL and LCL.
Figure 2: Examples of Cp (graph from
https://statisticalprocesscontrol.info/glossary.html)
Statistical Process Control 6
Process capability analysis has attracted the interest of
industrial practitioners to enhance the
quality and productivity according to the specification limits
(Aslam et al. 2014). The process
capability index, Cpk = min (Cpu, Cpl)
where Cpu = UCL-µ/ 3σ
Cpl = µ -LCL/ 3σ
µ = process mean

8. Hence, a larger value of the Cpk is better, with an industry-
standard of 1.33 or higher being
acceptable to most customers, as it is more likely that all items
will fall within the specification
limits and conversely a lower value of Cpk will mean that a lot
more items will fall outside the
specification range.
Figure 3: Relationship between Cp & Cpk
(graph from https://statisticalprocesscontrol.info/glossary.html)
Statistical Process Control 7
n
Not Capable Centred- Does Not Fit Fits but Not Centred
Capable
Cp>1, Cpk<1 Cp<1, Cpk≥1 Cp≥1, Cpk<1 Cp≥1,
Cpk≥1
Figure 4: Relationship between Cp & Cpk

9. (Graphs from https://www.qimacros.com/process-capability-
analysis/)
Control charts are used in SPC to represent and interpret the
data visually. Some of the control
charts typically used are scatter charts, histograms, Pareto
charts and x-bar & range charts.
Figure 5: Relationship between UCL, LCL and Process Mean
(Graph from https://arrizabalagauriarte.com/en/concepto-de-
calidad-primero-debemos-entender-esto-antes-de-aprender-
6sigma/)
Statistical Process Control 8
Figure 6: Normal Relationship between UCL, LCL, USL and
LSL
(Graphs from https://www.six-sigma-material.com/Voice-of-
the-Customer.html)
Over the years, SPC has been transformed from a method to
analyse historical data to one where
it can provide real-time analysis based on current data using
advanced software like InfinityQS
and Predisys Analytical Suite.

10. https://www.six-sigma-material.com/Voice-of-the-
Customer.html
Statistical Process Control 9
CASE STUDY ANALYSIS
WLL’s discharge temperatures were measured for the one
hundred and twenty days. Peña-
Rodríguez (2018) mentions that “sampling is an easy and cost-
effective way of monitoring a
process, but it does not provide much information about the
quality level of the process”. Jeang
(2010) also states that “it is clear that passively controlling a
process by its output is far less
effective than preventively managing the root causes among the
inputs”.
Table 1: Summary
Data (n) 120
Lowest Temperature Reading 16.2033
Highest Temperature Reading 46.4899
Mean Temperature 31.7121

11. UCL (Upper Control Limit) (Temperature) 50.4247
LCL (Lower Control limit) (Temperature) 12.9995
USL (Upper Specification Limit) (Temperature) 40.0000
LSL (Lower Specification limit) (Temperature) 25.0000
Standard Deviation (σ) 6.2375
Cp (Capability Index) 0.4008
Cpu (Capability Index upper limit) 0.4429
Cpl (Capability Index lower limit) 0.3587
Cpk= Min (CPU, Cpl) 0.3587
Temperatures are typically controlled within the range 25oC to
35oC, with the maximum
permitted temperature being 40oC; hence, the USL and LSL
have been calculated at 40 and 25.
The UCL and LCL are calculated at 50.4247 and 12.9995,
respectively.
Both the Cp (Cp = 0.4008) is less than 1; hence, the process is
inadequate and not capable. Cpk
(Cpk = 0.3587) is less than 1, which is far below the industry
standards of 1.33.

12. Statistical Process Control 10
Table 2: Temperature Frequency Interval
Temperature
Intervals
Frequency Total
16.2033 19.2320 Xxx 3
19.2320 22.2607 Xxx 3
22.2607 25.2894 Xxxxxxxxxxx 12
25.2894 28.3181 xxxxxxxxxxxxxxxxxx 19
28.3181 31.3468 xxxxxxxxxxxxxxxxxxxxxxx 23
31.3468 34.3755 xxxxxxxxxxxxxxxxxxxxxxx 19
34.3755 37.4042 xxxxxxxxxxxxxxxxx 17
37.4042 40.4329 Xxxxxxxxxxxx 12
40.4329 43.4616 Xxxxxxxx 8

13. 43.4616 46.4903 Xxxx 4
0
10
20
30
40
50
0 20 40 60 80 100 120 140
T
em
p
er
at
u
re
Days
Scatter Chart
Temperature vs Days

14. (with trendline)
Statistical Process Control 11
The temperature frequency interval and the scatter chart give us
an idea of the temperature
readings distribution. The histogram does not show a normal
distribution as the distribution is
skewered towards the right, which indicates that there are more
temperature readings on the
higher side of the specification limits, than on the lower side.
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 101112 131415161718 192021222324
252627282930

15. T
em
p
er
at
u
re
Day
Month 1
Temperature Mean (CL) UCL
LCL USL LSL
Statistical Process Control 12
0
10
20
30
40

16. 50
60
1 2 3 4 5 6 7 8 9 101112 131415161718 192021222324
252627282930
T
em
p
er
at
u
re
Day
Month 2
Temperature Mean (CL) UCL
LCL USL LSL
0
10
20
30
40
50

17. 60
1 2 3 4 5 6 7 8 9 101112 131415161718 192021222324
252627282930
T
em
p
er
at
u
re
Day
Month 3
Temperature Mean (CL) UCL
LCL USL LSL
Statistical Process Control 13
The temperatures are erratic, remaining mainly within the
specification limits, for the first and
second month, while remaining high for most of the third month
and then steadily decreasing

18. to below specification limits towards in the fourth month, as
denoted in red. Hence, it can be
said that variations in the first and second months are mainly
due to common cause variations
while in the third and fourth months are due to special cause
variations.
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 101112 131415161718 192021222324
252627282930
T
em
p
er
at
u
re

19. Day
Month 4
Temperature Mean (CL) UCL
LCL USL LSL
0
10
20
30
40
50
60
1 5 9
1
3
1
7
2
1
2
5

20. 2
9
3
3
3
7
4
1
4
5
4
9
5
3
5
7
6
1
6
5
6
9
7
3

21. 7
7
8
1
8
5
8
9
9
3
9
7
1
0
1
1
0
5
1
0
9
1
1
3
1

22. 1
7
T
em
p
er
at
u
re
Days
X-Bar Chart
Temperature Mean (CL) UCL
LCL USL LSL
Statistical Process Control 14
In the X-Bar chart, there is a high process variation with a
significant portion of the readings
falling between UCL and LCL. A majority of readings are also
within WLL’s acceptable USL
and LSL range, which is one standard deviation from the mean.
However, it also shows a
number of the readings falling outside this specification range,

23. which is represented in red. The
following also requires an investigation into root causes of the
out of control process, especially
in the third and fourth month;
• two points are near the specification limit in Day 27-28, Day
59-73, Day 98-99 and
Day 114-120
• alternating behaviour is observed
• the apparent trend in one direction with an increasing
tendency from the middle of
the second month and then decreasing tendency from the
beginning of the third
month till the end of the fourth month
• five points on one side of centreline at multiple areas with
from Day 114-120 falling
below lower specification limit
Hence, the process is out of control and therefore needs to be
investigated. Ignoring this
information would lead to a Type II error. However, we need to
note that the LCL and UCL
are usually calculated at three deviations from the mean, and, in
this case, the LSL and USL

24. are calculated at approximate one deviation as per WLL
requirements, which can lead to an
increased chance in Type I error of 32 percent.
Statistical Process Control 15
Figure 7: Abnormal Relationship between UCL, LCL, USL and
LSL, where USL & LSL is lower
than UCL & LCL, in Waterside Leather Limited
(Graphs from https://www.six-sigma-material.com/Voice-of-
the-Customer.html)
The MR chart shows the variation between two consecutive
temperature readings, and though
the spread is generally erratically spread, we can notice special
cause violations in days 1, 5,
77 and 78, highlighted in red.
RECOMMENDATION
WLL’s plant has not performed well as the process is not
capable. Hence, it can be said that
either there is no Standard Operating Procedure (SOP) for the
recording of temperature or that

25. the plant machinery is not performing as per industry standards.
In many situations, production
0.0000
2.0000
4.0000
6.0000
8.0000
10.0000
12.0000
14.0000
1 6
1
1
1
6
2
1
2
6
3
1

26. 3
6
4
1
4
6
5
1
5
6
6
1
6
6
7
1
7
6
8
1
8
6
9
1

27. 9
6
1
0
1
1
0
6
1
1
1
1
1
6
M
o
v
in
g
R
an
g
e
Days
Moving Range Chart

28. MR MR Bar MR UCL
https://www.six-sigma-material.com/Voice-of-the-
Customer.html
Statistical Process Control 16
systems are subject to degradation, which is the leading cause
of the production of defective
units (Bahria et al. 2019). Cp ≠ Cpk, hence, the process is
inadequate, and new process
parameters need to be developed. However, it is doubtful that
WLL would be able to change
its acceptable temperature specification limits to three
deviations from the mean temperature
as this would result in a non-acceptable value of the pH of the
effluent.
If the pH of the effluent not maintained as per the requirement
of WLL and government
regulations, it could lead to health and environment issues.
Islam et al. (2019) found that the
presence of chromium from tannery effluents can suppress the
immune function in humans and
animals. The releasing of high levels of organic matter,
nitrogen, total suspended solids, total

29. dissolved solids and ammonia into the waste system can cause
pollution and health problems
for the people around the tannery and contamination of the
groundwater.
Therefore, the recommendation is that WLL prioritises
developing a proper SOP for the
recording of temperature and conducts regular inspections of its
machinery. Dutoit and Riviere
(2018) stresses that maintenance and quality are intimately
linked, and the product of poorly
maintained equipment will inevitably see the quality of its
products degrade. Pickett (2108)
highlights the progress in SPC that allows real-time data
collection and analysis using a single
software that can be run concurrently in different branches,
while Rasay et al. (2018) emphasise
on the benefits of implementing an integrated maintenance
management (MM) and SPC
system. Similarly, Zasadzien and Midor (2018) say that “the
production process should be
conducted by identification of problems, finding the root causes
of the problems and
development and implementation of measures that will
eliminate the problems”. Abdul et al.

30. (2015) also state the benefit of having an Out-of-Control-
Action-Plan (OCAP) to provide
Statistical Process Control 17
guidelines for process adjustment. In addition to this
implementation of a DMAIC (define,
measure, analyse, improve, control) would also be
recommended.
CONCLUSION
Through the literature review and methodology of SPC along
with a case study of WLL, it is
seen that SPC is a highly reliable method of analysing a process
by use of statistical data.
Implementation of the SOP, regular inspections of machinery,
an integrated MM and real-time
SPC system, OCAP, and DMAIC will make the WLL’s process
capable with a higher Cp and
Cpk. If no remedial action is taken to develop a proper
balancing system to precisely neutralise
the pH of the effluent, it will result in environmental damage
and legal complication arising
from it.

31. Statistical Process Control 18
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Statistical Process Control 19
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Statistical Process Control 20
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Statistical Process Control 21
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Statistical Process Control 22
APPENDIX
Table 3: Historical Temperature Data from WLL, with CL,
UCL, LCL, USL, LSL
Day Temperature Mean
(CL)
UCL LCL USL LSL
1 28.7812 31.7713 50.4247 12.9995 40 25
2 34.4632 31.7713 50.4247 12.9995 40 25
3 31.3381 31.7713 50.4247 12.9995 40 25
4 31.2834 31.7713 50.4247 12.9995 40 25
5 28.9207 31.7713 50.4247 12.9995 40 25
6 33.7596 31.7713 50.4247 12.9995 40 25
7 25.3969 31.7713 50.4247 12.9995 40 25

38. 8 27.7849 31.7713 50.4247 12.9995 40 25
9 35.2479 31.7713 50.4247 12.9995 40 25
10 27.1159 31.7713 50.4247 12.9995 40 25
11 32.8717 31.7713 50.4247 12.9995 40 25
12 29.2171 31.7713 50.4247 12.9995 40 25
13 36.0253 31.7713 50.4247 12.9995 40 25
14 32.3371 31.7713 50.4247 12.9995 40 25
15 34.5249 31.7713 50.4247 12.9995 40 25
16 32.8717 31.7713 50.4247 12.9995 40 25
17 34.1173 31.7713 50.4247 12.9995 40 25
18 26.5235 31.7713 50.4247 12.9995 40 25
19 27.6623 31.7713 50.4247 12.9995 40 25
20 25.7744 31.7713 50.4247 12.9995 40 25
21 29.2701 31.7713 50.4247 12.9995 40 25
22 30.7326 31.7713 50.4247 12.9995 40 25
23 29.5054 31.7713 50.4247 12.9995 40 25
24 33.0292 31.7713 50.4247 12.9995 40 25
25 25.0401 31.7713 50.4247 12.9995 40 25

39. 26 28.9167 31.7713 50.4247 12.9995 40 25
27 24.3437 31.7713 50.4247 12.9995 40 25
28 26.1203 31.7713 50.4247 12.9995 40 25
29 25.0293 31.7713 50.4247 12.9995 40 25
30 26.6311 31.7713 50.4247 12.9995 40 25
31 35.6541 31.7713 50.4247 12.9995 40 25
32 28.4353 31.7713 50.4247 12.9995 40 25
33 29.1495 31.7713 50.4247 12.9995 40 25
34 28.1584 31.7713 50.4247 12.9995 40 25
35 26.1927 31.7713 50.4247 12.9995 40 25
Statistical Process Control 23
36 33.3182 31.7713 50.4247 12.9995 40 25
37 34.9424 31.7713 50.4247 12.9995 40 25
38 28.7812 31.7713 50.4247 12.9995 40 25
39 25.0293 31.7713 50.4247 12.9995 40 25
40 35.6541 31.7713 50.4247 12.9995 40 25
41 25.7283 31.7713 50.4247 12.9995 40 25

40. 42 32.1247 31.7713 50.4247 12.9995 40 25
43 31.0652 31.7713 50.4247 12.9995 40 25
44 38.0343 31.7713 50.4247 12.9995 40 25
45 32.5144 31.7713 50.4247 12.9995 40 25
46 28.1677 31.7713 50.4247 12.9995 40 25
47 37.7284 31.7713 50.4247 12.9995 40 25
48 34.8157 31.7713 50.4247 12.9995 40 25
49 37.8175 31.7713 50.4247 12.9995 40 25
50 33.1018 31.7713 50.4247 12.9995 40 25
51 34.2708 31.7713 50.4247 12.9995 40 25
52 31.1627 31.7713 50.4247 12.9995 40 25
53 31.7993 31.7713 50.4247 12.9995 40 25
54 39.5554 31.7713 50.4247 12.9995 40 25
55 33.7153 31.7713 50.4247 12.9995 40 25
56 37.4093 31.7713 50.4247 12.9995 40 25
57 33.8995 31.7713 50.4247 12.9995 40 25
58 36.4312 31.7713 50.4247 12.9995 40 25
59 42.0032 31.7713 50.4247 12.9995 40 25

41. 60 39.5672 31.7713 50.4247 12.9995 40 25
61 44.4895 31.7713 50.4247 12.9995 40 25
62 45.1121 31.7713 50.4247 12.9995 40 25
63 41.8079 31.7713 50.4247 12.9995 40 25
64 39.3991 31.7713 50.4247 12.9995 40 25
65 46.4899 31.7713 50.4247 12.9995 40 25
66 37.8993 31.7713 50.4247 12.9995 40 25
67 46.1963 31.7713 50.4247 12.9995 40 25
68 40.8314 31.7713 50.4247 12.9995 40 25
69 41.9645 31.7713 50.4247 12.9995 40 25
70 41.0937 31.7713 50.4247 12.9995 40 25
71 39.7727 31.7713 50.4247 12.9995 40 25
72 41.5375 31.7713 50.4247 12.9995 40 25
73 33.6594 31.7713 50.4247 12.9995 40 25
74 41.4132 31.7713 50.4247 12.9995 40 25
75 37.3876 31.7713 50.4247 12.9995 40 25
76 37.8315 31.7713 50.4247 12.9995 40 25
77 38.4347 31.7713 50.4247 12.9995 40 25

42. 78 42.4413 31.7713 50.4247 12.9995 40 25
79 35.2391 31.7713 50.4247 12.9995 40 25
80 32.7223 31.7713 50.4247 12.9995 40 25
Statistical Process Control 24
81 30.3257 31.7713 50.4247 12.9995 40 25
82 34.5093 31.7713 50.4247 12.9995 40 25
83 30.2113 31.7713 50.4247 12.9995 40 25
84 29.6383 31.7713 50.4247 12.9995 40 25
85 34.8458 31.7713 50.4247 12.9995 40 25
86 32.2237 31.7713 50.4247 12.9995 40 25
87 35.9531 31.7713 50.4247 12.9995 40 25
88 37.8416 31.7713 50.4247 12.9995 40 25
89 36.0366 31.7713 50.4247 12.9995 40 25
90 29.9267 31.7713 50.4247 12.9995 40 25
91 31.4899 31.7713 50.4247 12.9995 40 25
92 35.5593 31.7713 50.4247 12.9995 40 25

43. 93 34.4278 31.7713 50.4247 12.9995 40 25
94 28.3744 31.7713 50.4247 12.9995 40 25
95 30.2776 31.7713 50.4247 12.9995 40 25
96 26.8801 31.7713 50.4247 12.9995 40 25
97 33.4373 31.7713 50.4247 12.9995 40 25
98 23.7654 31.7713 50.4247 12.9995 40 25
99 22.4613 31.7713 50.4247 12.9995 40 25
100 31.0189 31.7713 50.4247 12.9995 40 25
101 30.7144 31.7713 50.4247 12.9995 40 25
102 27.0994 31.7713 50.4247 12.9995 40 25
103 26.3009 31.7713 50.4247 12.9995 40 25
104 27.9834 31.7713 50.4247 12.9995 40 25
105 20.6646 31.7713 50.4247 12.9995 40 25
106 23.5196 31.7713 50.4247 12.9995 40 25
107 26.6281 31.7713 50.4247 12.9995 40 25
108 19.8736 31.7713 50.4247 12.9995 40 25
109 24.9148 31.7713 50.4247 12.9995 40 25
110 26.8165 31.7713 50.4247 12.9995 40 25

44. 111 28.8493 31.7713 50.4247 12.9995 40 25
112 24.6247 31.7713 50.4247 12.9995 40 25
113 25.5761 31.7713 50.4247 12.9995 40 25
114 25.0189 31.7713 50.4247 12.9995 40 25
115 19.1047 31.7713 50.4247 12.9995 40 25
116 22.5525 31.7713 50.4247 12.9995 40 25
117 18.8148 31.7713 50.4247 12.9995 40 25
118 19.7404 31.7713 50.4247 12.9995 40 25
119 16.2033 31.7713 50.4247 12.9995 40 25
120 24.6646 31.7713 50.4247 12.9995 40 25
Statistical Process Control 25
Table 4: Historical Temperature Data from
WLL, with Moving Range (MR), MR Bar and
MR UCL
Day Temperature MR MR
Bar

45. MR UCL
1 28.7812 0.0000 3.9401 12.8724
2 34.4632 5.6820 3.9401 12.8724
3 31.3381 3.1251 3.9401 12.8724
4 31.2834 0.0547 3.9401 12.8724
5 28.9207 2.3627 3.9401 12.8724
6 33.7596 4.8389 3.9401 12.8724
7 25.3969 8.3627 3.9401 12.8724
8 27.7849 2.3880 3.9401 12.8724
9 35.2479 7.4630 3.9401 12.8724
10 27.1159 8.1320 3.9401 12.8724
11 32.8717 5.7558 3.9401 12.8724
12 29.2171 3.6546 3.9401 12.8724
13 36.0253 6.8082 3.9401 12.8724
14 32.3371 3.6882 3.9401 12.8724
15 34.5249 2.1878 3.9401 12.8724
16 32.8717 1.6532 3.9401 12.8724
17 34.1173 1.2456 3.9401 12.8724

46. 18 26.5235 7.5938 3.9401 12.8724
19 27.6623 1.1388 3.9401 12.8724
20 25.7744 1.8879 3.9401 12.8724
21 29.2701 3.4957 3.9401 12.8724
22 30.7326 1.4625 3.9401 12.8724
23 29.5054 1.2272 3.9401 12.8724
24 33.0292 3.5238 3.9401 12.8724
25 25.0401 7.9891 3.9401 12.8724
26 28.9167 3.8766 3.9401 12.8724
27 24.3437 4.5730 3.9401 12.8724
28 26.1203 1.7766 3.9401 12.8724
29 25.0293 1.0910 3.9401 12.8724
30 26.6311 1.6018 3.9401 12.8724
31 35.6541 9.0230 3.9401 12.8724
32 28.4353 7.2188 3.9401 12.8724
33 29.1495 0.7142 3.9401 12.8724
34 28.1584 0.9911 3.9401 12.8724
35 26.1927 1.9657 3.9401 12.8724

47. 36 33.3182 7.1255 3.9401 12.8724
37 34.9424 1.6242 3.9401 12.8724
38 28.7812 6.1612 3.9401 12.8724
Statistical Process Control 26
39 25.0293 3.7519 3.9401 12.8724
40 35.6541 10.6248 3.9401 12.8724
41 25.7283 9.9258 3.9401 12.8724
42 32.1247 6.3964 3.9401 12.8724
43 31.0652 1.0595 3.9401 12.8724
44 38.0343 6.9691 3.9401 12.8724
45 32.5144 5.5199 3.9401 12.8724
46 28.1677 4.3467 3.9401 12.8724
47 37.7284 9.5607 3.9401 12.8724
48 34.8157 2.9127 3.9401 12.8724
49 37.8175 3.0018 3.9401 12.8724
50 33.1018 4.7157 3.9401 12.8724

48. 51 34.2708 1.1690 3.9401 12.8724
52 31.1627 3.1081 3.9401 12.8724
53 31.7993 0.6366 3.9401 12.8724
54 39.5554 7.7561 3.9401 12.8724
55 33.7153 5.8401 3.9401 12.8724
56 37.4093 3.6940 3.9401 12.8724
57 33.8995 3.5098 3.9401 12.8724
58 36.4312 2.5317 3.9401 12.8724
59 42.0032 5.5720 3.9401 12.8724
60 39.5672 2.4360 3.9401 12.8724
61 44.4895 4.9223 3.9401 12.8724
62 45.1121 0.6226 3.9401 12.8724
63 41.8079 3.3042 3.9401 12.8724
64 39.3991 2.4088 3.9401 12.8724
65 46.4899 7.0908 3.9401 12.8724
66 37.8993 8.5906 3.9401 12.8724
67 46.1963 8.2970 3.9401 12.8724
68 40.8314 5.3649 3.9401 12.8724

49. 69 41.9645 1.1331 3.9401 12.8724
70 41.0937 0.8708 3.9401 12.8724
71 39.7727 1.3210 3.9401 12.8724
72 41.5375 1.7648 3.9401 12.8724
73 33.6594 7.8781 3.9401 12.8724
74 41.4132 7.7538 3.9401 12.8724
75 37.3876 4.0256 3.9401 12.8724
76 37.8315 0.4439 3.9401 12.8724
77 38.4347 0.6032 3.9401 12.8724
78 42.4413 4.0066 3.9401 12.8724
79 35.2391 7.2022 3.9401 12.8724
80 32.7223 2.5168 3.9401 12.8724
81 30.3257 2.3966 3.9401 12.8724
82 34.5093 4.1836 3.9401 12.8724
Statistical Process Control 27
83 30.2113 4.2980 3.9401 12.8724
84 29.6383 0.5730 3.9401 12.8724

50. 85 34.8458 5.2075 3.9401 12.8724
86 32.2237 2.6221 3.9401 12.8724
87 35.9531 3.7294 3.9401 12.8724
88 37.8416 1.8885 3.9401 12.8724
89 36.0366 1.8050 3.9401 12.8724
90 29.9267 6.1099 3.9401 12.8724
91 31.4899 1.5632 3.9401 12.8724
92 35.5593 4.0694 3.9401 12.8724
93 34.4278 1.1315 3.9401 12.8724
94 28.3744 6.0534 3.9401 12.8724
95 30.2776 1.9032 3.9401 12.8724
96 26.8801 3.3975 3.9401 12.8724
97 33.4373 6.5572 3.9401 12.8724
98 23.7654 9.6719 3.9401 12.8724
99 22.4613 1.3041 3.9401 12.8724
100 31.0189 8.5576 3.9401 12.8724
101 30.7144 0.3045 3.9401 12.8724
102 27.0994 3.6150 3.9401 12.8724

51. 103 26.3009 0.7985 3.9401 12.8724
104 27.9834 1.6825 3.9401 12.8724
105 20.6646 7.3188 3.9401 12.8724
106 23.5196 2.8550 3.9401 12.8724
107 26.6281 3.1085 3.9401 12.8724
108 19.8736 6.7545 3.9401 12.8724
109 24.9148 5.0412 3.9401 12.8724
110 26.8165 1.9017 3.9401 12.8724
111 28.8493 2.0328 3.9401 12.8724
112 24.6247 4.2246 3.9401 12.8724
113 25.5761 0.9514 3.9401 12.8724
114 25.0189 0.5572 3.9401 12.8724
115 19.1047 5.9142 3.9401 12.8724
116 22.5525 3.4478 3.9401 12.8724
117 18.8148 3.7377 3.9401 12.8724
118 19.7404 0.9256 3.9401 12.8724
119 16.2033 3.5371 3.9401 12.8724
120 24.6646 8.4613 3.9401 12.8724

52. Module Code: BSOM046
Assignment: ES1 – Statistical Process Control
Local Module Tutor: Mr. Melvin Goh
STATISTICAL
PROCESS
CONTROL
By: XXXXXX
Student ID: XXXXXXXX
FACULTY OF BUSINESS & LAW // MBA
Contents
1. Introduction
...............................................................................................
.................. 1

53. 2. Literature review
...............................................................................................
.......... 1
2.1. Control charts for data
types.................................................................................. 2
2.2. Process capability
...............................................................................................
... 5
3. Statistical analysis of Waterside Leather Limited
.................................................... 6
3.1. Capability test
...............................................................................................
......... 8
4. Recommendations
...............................................................................................
....... 9
5. Conclusion
...............................................................................................
................. 10
References
...............................................................................................
........................ 12
Appendix A: WLL’s historic data with control limits and
moving range average ...... 14
Appendix B: WLL’s historic temperature recordings (X-bar
Chart) ............................ 18

54. Appendix C: WLL’s process capability results
............................................................. 19
1
1. Introduction
All organisations, whichever nature, compete on quality,
delivery and price—all of which
requiring processes to facilitate the transformation of inputs
into outputs in the form of
products, information, and services (Oakland and Oakland,
2019). Every task that’s to be
carried out within an organisation involves processes. While
some are easily identified (e.g.
filling bottles with soda), others are less conspicuous (e.g. a
personal assistant preparing a
report for her boss).
To be successful in today’s climate, organisations will need to
commit to continuous
improvement and to be equipped with know-how in proper

55. process management to ensure
quality. But operators and managers often mistook quality
management as post-production
detection when it is essentially about managing quality at the
point of production or
manufacture. To prevent customer dissatisfaction and to reduce
waste incurred from poor
quality and the manufacturing of defective products, cost-
effective quality control measures
must therefore be incorporated into processes. And to do so, it
is highly recommended to
incorporate Statistical Process Control (SPC) which—succinctly
defined—is to help
organisations achieve total quality management through process
control and management.
2. Literature review
The objective of SPC lies in controlling and monitoring
processes to help organisations
achieve competitive advantage. In order to manage processes
effectively, it’s crucial to
understand that processes have variations, need proper control,
have a capability and will
require improvements (Oakland and Oakland, 2019). It entails
proper documentation of

56. procedures, includes the collection of reliable data about
processes and data analysis, and
enables action to be taken to prevent failure or non-
conformance with the desired
requirements—improving processes and leading to quality
assurance as a result (Oakland
and Oakland, 2019). In essence, to continuously improve the
quality of a process or product,
the role of SPC is to continuously reduce variations around a
target (Ravichandran, 2017;
Abbas et al., 2018).
SPC however cannot operate independently. At its most
fundamental level, it requires data
from sources the likes of operators, machines, and the Internet
of Things (IoT), and must
harness the ability to present findings and notifications to the
right stakeholders, at the
appropriate time and place so as to make effective, immediate
decisions (Seland, 2019).
2
Therefore, to commission SPC, managers must identify the

57. objectives they wish to achieve
before they can identify the right kind of parameters the SPC
process will need.
2.1. Control charts for data types
Where it’s common to have variations in processes (e.g.
common cause variations that are
considered inherent), SPC provides insights on whether
processes are behaving as
specified and helps detect irregularities either through control
by variables or control by
attributes through the use of control charts. Widely used to
provide enhanced efficiency in
production, to reduce defects, improve profitability and reduce
costs, control charts
henceforth play major roles in achieving the goal of sticking to
the target with minimum
variation, leading to quality improvement (Ravichandran, 2017).
There are several control charts used to monitor variables and
attributes (Kounis, 2018). In
control by variables, process monitoring schemes such as the X-
bar (X
̄ ) control chart is used
to monitor the mean of a normally distributed variables

58. concurrently, where variable data
are collected at regular intervals from a process. Compiling
collected data, it presents the
process mean (x̄ ) where coupled with set control limits (i.e.
upper control limit and lower
control limit), sheds light on whether a process is “in-
statistical-control” or “out-of-control”.
Whilst it’s natural to have random variables within control
limits that results in only minor
variations, assignable causes that are responsible for non-
conformities in an “out-of-control”
process must be quickly identified and corrected. In doing so,
corrective action can be
undertaken before non-conforming units are manufactured,
preventing the incurrence of
undesired costs (Mahesh and Prabhuswamy, 2010).
Figure 2.1 Example of X-bar Chart (Adapted from Oakland and
Oakland, 2019)
3
An example of an X-bar control chart is illustrated in Figure 2.1
where it shares the dynamic

59. performance of a process over a period of time. Control charts
with limits are used for
interpretation of data where results are plotted and fall into
three zones: the stable zone
where no action is required (only common causes are present),
the warning zone which
suggests more data is to be obtained, and the action zone which
requires action to be taken
(assignable causes are present) (Oakland and Oakland, 2019).
According to the empirical
rule, if a process is stable, 99.73% of the points plotted should
fall within the 3 sigma limits
with half of the points above the centre-line and half below;
95% of them should fall within
the 2 sigma limits and 68% within the 1 sigma limit (Hayes,
2020). Table 2.1 illustrates the
basic rules for interpreting control charts and states the possible
causes for each pattern.
RULE RULE NAME PATTERN PATTERN
DESCRIPTION
POSSIBLE CAUSES
1 Beyond
Limits

60. One or more points
beyond the control
limits Large shifts
from the
average
• New operator doing the job
• Wrong setup
• Error in measurement
• Process step skipped
• Incomplete process step
• Power failure
• Equipment breakdown
2 Zone A
(Action Zone)
2 out of 3 consecutive
points in Zone A or
beyond
3 Zone B
(Warning
Zone)
4 out of 5 consecutive
points in Zone B or
beyond

61. Small shifts
from the
average
• Change in raw material
• Change in operating instructions
• Different measurement device
and/or calibration
• Change of shifts
• Operator becomes better at the
job
• Change in maintenance
programme
• Change in setup procedure
4 Zone C
(Stable
Zone)
7 or more consecutive
points on one side of
the average (in Zone
C or beyond)
5 Trend 7 consecutive points
trending up or down
• Effects of temperature change
(cool or hot)

62. • Wear (and tear)
6 Mixture 8 consecutive point in
Zone C
More than one process present (e.g.
machines, raw materials, shifts)
7 Stratification 15 consecutive points
in Zone C
More than one process present (e.g.
machines, raw materials, shifts)
8 Over-control 14 consecutive points
in alternating up and
down
• Tampering by operator
• Alternating raw materials
Table 2.1. Basic control chart rules and possible causes behind
patterns (Adapted from McNeese,
2016)
4

63. Figure 2.2. Types of control charts for data types (Adapted from
Gygi et al., 2012)
The most common control charts are illustrated in Figure 2.2.
The p and u charts are used
to monitor attributes—p chart for defectives (defective items in
a sample) as a percentage
or proportion of total outcomes, and u chart for counts of
defects or events with a precise
outcome that is negative or inferior where they may be given as
a percentage or proportion
(Cheung et al., 2012). On the other hand, variables control
charts such as X-bar-R and X-
bar-S charts plot continuous measurement process data such as
temperature or length in a
chronological sequence. These two types of charts go hand-in-
hand when monitoring
variables as they measure two critical parameters: central
tendency and variability
(Majumdar et al., 2013). Lastly, the X-MR chart is used when
only one observation is taken
where process variability needs to be determined. It is used to
assess process stability by
visualising the difference between individual values and that
between ranges of values

64. (Cheung et al., 2012).
Evidently, there are different control charts for different
process monitoring and control.
Basic factors to help in the selection of the right control chart to
use include the type of data
charted (whether it’s a variable or attribute), sample size, and
cost and ease of sampling
(Cheung et al., 2012). All of which, will require a different
control chart to use and analysis
methodology.
5
2.2. Process capability
A stable process may not necessarily be a capable process.
Besides measuring how
conformed products or processes are to quality specifications,
understanding how capable
the process is of manufacturing conforming products is equally
important. A convenient way
to measure process capability is through the use of process

65. capability indices (PCI)—a
powerful means to determine if the process is capable of
meeting tolerance specifications
(Chen et al., 2011). It compares the distribution of the process
specifications with the
distribution of the product specification limits (Mahapatra et
al., 2020). As illustrated in Figure
2.3, a capable process has a narrower distribution as compared
to one that’s incapable.
Figure 2.3. Process capability based on spread of process
distribution across specification (Adapted from
Mahapatra et al., 2020)
Using sigma value determined from either the Moving Range
(MR), R or S chart, Cp and Cpk
are two measurements of process capability for a process that is
under statistical control.
Cp Cpk
Shows how well the Six Sigma range fits into
the specification range
Shows the relationship of the Six Sigma
spread to the specification limits

66. Typically, if Cp=Cpk, the process is centred at the midpoint of
the specifications, and when
Cpk is less than Cp, the process is off-centred (Mahesh and
Prabhusw, 2010). In other words,
when both values are similar, the mean is close to the middle of
the specification limits,
demonstrating how the process is capable of meeting its target.
When Cp is greater than
Cpk, the mean is nearer to either one of the specification limits.
The simplest way to measure
capability is as shown below, using the ratios of specification
range to the natural variation
of the process that is ±3 standard deviation.
6
�� =
��� − ���
6�
UTL = upper tolerance limit; LTL = lower tolerance limit; s =
standard deviation of process variability.

67. ��� =
���(��� − �, � − ���)
3�
USL = upper specification limit; LSL = lower specification
limit
In general, assuming distribution is normal, if the Cp of a
process is greater than 1, the
process can be considered as ‘capable’ whereas if it’s less than
1, the process can be
considered as ‘incapable’ (Slack et al., 2016).
3. Statistical analysis of Waterside Leather Limited
Using the 120-day historic data collected at Waterside Leather
Limited (WLL), the average
value (mean), UCL and LCL were found (refer to Appendix A),
and used to chart an X-bar
chart to analyse its effluent’s temperature stability as shown in
Figure 3.1 (also see Appendix
B). Clearly, the temperature was kept rather stable for the first
quarter where majority of the
temperature readings were close to the average line suggesting
common variables, and the

68. process appeared to be in statistical control. However, it’s
evident that there are non-
conforming special causes which must be addressed. According
to Table 2.1 where possible
rules of control patterns were stated, upward/downward trends
and erroneous patterns
occurring within Zone A and B were detected.
Figure 3.1 X-bar of 120-day temperature recordings at
Waterside Leather Limited (WLL)
7
Using historic data of the past 120 days, the UCL was found to
be 50.4°C. However, the
tannery needs to keep its temperature at 40°C maximum.
Therefore, using 40°C as UCL,
another X-bar chart was plotted with revised LCL in Figure 3.2.
In this revised chart, it was
evident that the temperature of the effluent has gone out-of-
control on several days. The
average value hit maximum limit toward the middle of period,

69. strongly suggesting process
instability. Shown in Table 2.1, this kind of issues can arise due
to equipment malfunctions
or change in maintenance programme. As regular maintenance is
performed on the
balancing system on a monthly basis, the balancing system
could have been tampered with
or had parts replaced during its maintenance, hence resulting in
the upward trend on Day
31, the out-of-statistical-control on Day 61, and the downward
trend on Day 91.
Figure 3.2 Revised X-bar chart using UCL 40°C (top) and MR
chart (bottom).
In the same Figure 3.2, a Moving Range (MR) chart was plotted
to show variability between
data points. The MR chart is also used to monitor the effects of
process improvement. At
first glance, data points appear to be in-statistical-control.
However, upon closer look, it is
evident that there’s out-of-control deviation recording 2 out of 3
consecutive points in Zone
A.

70. 8
3.1. Capability test
Typically, capability tests are for control charts that appear to
be in-statistical-control with no
special causes or variations. Given that special variations were
present in the control charts
plotted above, normal distribution curve and histogram are
henceforth used to test WLL’s
process capability.
Again, using 40°C as upper specification limit (USL) which is
stated as the maximum
temperature limit, histogram generated in Figure 3.3 shows that
majority of the data is below
limit and is close to target. And because the supposedly
specification range is between 25°C
to 35°C, it appears that majority of the distribution fell under
the acceptable range. However,
it’s critical to emphasise that there are also readings that are
off-target. Though with

71. elimination of process shifts and drifts, there remains potential
for the tannery to fine-tune
the balancing system.
Figure 3.3 Capability histogram and distribution curve
Conclusively, it’s clear that WLL’s process is incapable as the
upper control limit based on
the 120-day record is at 50.42°C when the upper specification
limit is supposed to be 40°C.
Calculations show that its process’s capability measures at
Cp=0.4013 and Cpk=0.359 (see
Appendix C) which both are below 1, rendering it incapable
(Slack et al., 2016).
9
According to Table 2.1, causes for the instability include poor
maintenance work, poor
equipment calibration and a change in its external temperature.
That said, WLL must look
into possible causes and identify the right adjustments to make
so as to render a capable

72. process. Otherwise, it will run the risks of producing
unbalanced effluent of high pH values
and cause damage to surrounding water bodies and land surfaces
including aquatic life
(Chowdhury et al., 2015).
4. Recommendations
Figure 4.1 The Six Sigma “DMAIC” Improvement Process
(Adapted from Terry, 2019)
It’s crucial to recognise that process improvement is a
continuous cycle characterised by
repeatedly monitoring and questioning the workings of each
process (Slack et al., 2016). Using
Six Sigma “DMAIC” cycle, a five-step approach is illustrated in
Figure 4.1. Numerous studies have
proven the effectiveness of this methodology, which when
applied, has led to energy efficiency
that has resulted in quality management and cost-savings
(Falcón et al., 2012; Saravanan et al.,
2012; Srinivasan et al., 2014). It is henceforth suggested that
WLL utilises the “DMAIC”
framework to improve its current process.

73. Starting with Defining the issues, the goals and deliverables
must be identified. In the case of
WLL, its core objective would be to control the temperature of
its effluent within its specified
range of 25°C-35°C. Next, in the Measure stage, WLL must
validate the issues to ensure
that they are indeed worth solving through the use of data to
refine the problem and identify
10
what is exactly happening. It is perhaps worthwhile to take two
readings per day to understand
if the patterns are similar so as to improve the quality of
measurement.
Once these measurements have been identified, they can be
Analysed to develop
hypotheses of the root causes of problems, and to validate if
these hypotheses are indeed
true. In WLL’s case, the inferred causes were due to the
monthly maintenance being carried
out where upward and downward trends were observed at these
points of time, thus strongly
hinting this to be the root cause. Was it parts replacement that

74. had caused the existing
calibration to be thwarted? Was it due to the service
technician’s lack of experience or skills
that had resulted in the special variations? Once the causes are
identified, work can then be
initiated on Improving the process using ideas and solutions that
are to be tested for their
effectiveness. Lastly, with results measured and refinements
incorporated, the improved
process must be continually monitored and Controlled to check
for sustainability of this
enhanced level of performance.
5. Conclusion
Ever since its founding in the early 1920s, SPC has been an
indispensable component
amongst numerous industries and functions within businesses.
While it has proven to be
reliable and effective, SPC is not without its shortcomings. SPC
adoption has been found to
be a time-consuming and costly endeavour where it requires the
buy-in and contribution
from stakeholders such as production personnel who might see
it as an additional task to

75. perform and refuse to cooperate (Gordon, 2013). However, as
SPC approaches its century
mark, its effectiveness and reliability remain uncontested due to
its relevance in controlling
quality—where it serves as a control process to ensure that
products and services are
produced to customers’ requirements.
In the case of WLL, it was evident that its process was
statistically out-of-control which if
unmanned, could lead to major repercussions such as the release
of harmful effluent to its
environment that could be in violation with its state
environmental laws, leading to the
incurrence of hefty fines. Therefore, it’s recommended that
WLL uses the “DMAIC”
framework to improve the process and that they must continue
to do so, to ensure continual
improvement which confidently, will lead to totally quality
management and cost-savings as
prior studies have shown.

76. 11
12
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factory-part-one
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dmaic/
14
Appendix A: WLL’s historic data with control limits and
moving range average
Mean 31.71212 UCL 50.42469876
Std Dev. 6.237526 LCL 12.99954124
Range 3.940133
68% 95% 99.7%%
Day
Temperature
℃ Range Mean 1σ (1σ) 2σ (2σ) UCL (3 σ) LCL (3 σ)
1 28.7812 0 31.71212 37.94964625 25.47459375 44.18717251
19.23706749 50.42469876 12.99954124
2 34.4632 5.682 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
3 31.3381 3.1251 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
4 31.2834 0.0547 31.71212 37.94964625 25.47459375

82. 44.18717251 19.23706749 50.42469876 12.99954124
5 28.9207 2.3627 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
6 33.7596 4.8389 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
7 25.3969 8.3627 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
8 27.7849 2.388 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
9 35.2479 7.463 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
10 27.1159 8.132 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
11 32.8717 5.7558 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
12 29.2171 3.6546 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
13 36.0253 6.8082 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
14 32.3371 3.6882 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
15 34.5249 2.1878 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
16 32.8717 1.6532 31.71212 37.94964625 25.47459375

83. 44.18717251 19.23706749 50.42469876 12.99954124
17 34.1173 1.2456 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
18 26.5235 7.5938 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
19 27.6623 1.1388 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
20 25.7744 1.8879 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
21 29.2701 3.4957 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
22 30.7326 1.4625 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
23 29.5054 1.2272 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
24 33.0292 3.5238 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
25 25.0401 7.9891 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
26 28.9167 3.8766 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
15

84. 27 24.3437 4.573 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
28 26.1203 1.7766 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
29 25.0293 1.091 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
30 26.6311 1.6018 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
31 35.6541 9.023 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
32 28.4353 7.2188 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
33 29.1495 0.7142 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
34 28.1584 0.9911 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
35 26.1927 1.9657 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
36 33.3182 7.1255 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
37 34.9424 1.6242 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
38 28.7812 6.1612 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124

85. 39 25.0293 3.7519 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
40 35.6541 10.6248 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
41 25.7283 9.9258 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
42 32.1247 6.3964 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
43 31.0652 1.0595 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
44 38.0343 6.9691 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
45 32.5144 5.5199 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
46 28.1677 4.3467 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
47 37.7284 9.5607 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
48 34.8157 2.9127 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
49 37.8175 3.0018 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
50 33.1018 4.7157 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124

86. 51 34.2708 1.169 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
52 31.1627 3.1081 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
53 31.7993 0.6366 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
54 39.5554 7.7561 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
55 33.7153 5.8401 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
56 37.4093 3.694 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
57 33.8995 3.5098 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
58 36.4312 2.5317 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
59 42.0032 5.572 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
60 39.5672 2.436 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
61 44.4895 4.9223 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
16

87. 62 45.1121 0.6226 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
63 41.8079 3.3042 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
64 39.3991 2.4088 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
65 46.4899 7.0908 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
66 37.8993 8.5906 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
67 46.1963 8.297 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
68 40.8314 5.3649 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
69 41.9645 1.1331 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
70 41.0937 0.8708 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
71 39.7727 1.321 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
72 41.5375 1.7648 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
73 33.6594 7.8781 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124

88. 74 41.4132 7.7538 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
75 37.3876 4.0256 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
76 37.8315 0.4439 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
77 38.4347 0.6032 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
78 42.4413 4.0066 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
79 35.2391 7.2022 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
80 32.7223 2.5168 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
81 30.3257 2.3966 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
82 34.5093 4.1836 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
83 30.2113 4.298 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
84 29.6383 0.573 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
85 34.8458 5.2075 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124

89. 86 32.2237 2.6221 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
87 35.9531 3.7294 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
88 37.8416 1.8885 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
89 36.0366 1.805 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
90 29.9267 6.1099 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
91 31.4899 1.5632 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
92 35.5593 4.0694 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
93 34.4278 1.1315 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
94 28.3744 6.0534 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
95 30.2776 1.9032 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
96 26.8801 3.3975 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124

90. 17
97 33.4373 6.5572 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
98 23.7654 9.6719 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
99 22.4613 1.3041 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
100 31.0189 8.5576 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
101 30.7144 0.3045 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
102 27.0994 3.615 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
103 26.3009 0.7985 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
104 27.9834 1.6825 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
105 20.6646 7.3188 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
106 23.5196 2.855 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
107 26.6281 3.1085 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
108 19.8736 6.7545 31.71212 37.94964625 25.47459375

91. 44.18717251 19.23706749 50.42469876 12.99954124
109 24.9148 5.0412 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
110 26.8165 1.9017 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
111 28.8493 2.0328 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
112 24.6247 4.2246 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
113 25.5761 0.9514 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
114 25.0189 0.5572 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
115 19.1047 5.9142 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
116 22.5525 3.4478 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
117 18.8148 3.7377 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
118 19.7404 0.9256 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
119 16.2033 3.5371 31.71212 37.94964625 25.47459375
44.18717251 19.23706749 50.42469876 12.99954124
120 24.6646 8.4613 31.71212 37.94964625 25.47459375

92. 44.18717251 19.23706749 50.42469876 12.99954124
18
Appendix B: WLL’s historic temperature recordings (X-bar
Chart)
Mean
UCL (3 σ)
LCL (3 σ)
0
10
20
30
40
50
60
1 3 5 7 9
1

93. 1
1
3
1
5
1
7
1
9
2
1
2
3
2
5
2
7
2
9
3
1
3
3
3

94. 5
3
7
3
9
4
1
4
3
4
5
4
7
4
9
5
1
5
3
5
5
5
7
5

95. 9
6
1
6
3
6
5
6
7
6
9
7
1
7
3
7
5
7
7
7
9
8
1
8

96. 3
8
5
8
7
8
9
9
1
9
3
9
5
9
7
9
9
1
0
1
1
0
3

97. 1
0
5
1
0
7
1
0
9
1
1
1
1
1
3
1
1
5
1
1
7
1

98. 1
9
Waterside Leather Limited Temperature Recordings (Day One
through Day 120)
19
Appendix C: WLL’s process capability results
Assessment Brief
Module Code
Module Name
Managing Operations and the Supply Chain
Level
7
Module Leader
Andrew Gough
Module Code
BSOM046
Assessment title:

99. AS2: Statistical Process Control
Weighting:
60%
Submission dates:
13 January 2023
Feedback and Grades due:
12 February 2023
Please read the whole assessment brief before starting work on
the Assessment Task.
Assessment Task
You will conduct a review of the academic literature on the
subject of statistical process control.
Following your review, you are to analyse a given set of data to
evaluate the performance of a
fictional brewery in a given scenario.
You will be expected to illustrate your discussion with
examples from academic journals, the trade press and other
authoritative sources.
The word count should be 2000 words ±10% (
tables, diagrams and appendices are excluded from the
count).
Assessment Breakdown
1. Prepare a
literature review on the subject of

100. Statistical Process Control, covering the
concept from its
inception up to the
present day.
Ensure that you include references to at least
10 peer-reviewed articles, no more than ten years old.
You may also acknowledge older works, providing they are of
sufficient importance in
charting the development of SPC.
(50% of word count)
2. The supplied spreadsheet contains
historic data recording the
temperature of c
ombined effluent discharged by a fictional brewery,
Waterside Lager Limited (WLL). The data comprises
temperatures recorded
four times a day over the month of September 2022.
The brewery’s discharges are normally controlled within the
range
25oC to 35oC. The maximum legally permitted
temperature is
40oC.
Regular maintenance is performed on the balancing system
(which neutralises the
pH of the effluent at the expense of
heating the
discharge in the process), normally on a weekly basis.
Use the data to
visualise the performance of the

101. effluent control process, describing your
analytical approach in detail. Include
any graphs generated.
In your view, how
well has the plant performed?
What
priorities for quality improvements should the plant
management set?
(50% of word count)Learning Outcomes
On successful completion of this assessment, you will be able
to:
c) Critically discuss the managerial relevance of topics in
business operations and supply management, analysing their
benefits and implementation challenges to organisations and
their supply chains.
d) Apply managerial concepts, theoretical frameworks and
approaches to solve specific operations and supply chain
problems in a range of business case scenarios, including
related implementation challenges.
f) Produce and justify appropriate informed decisions in the
context by elaborating pros and cons arguments concerning
application of relevant concepts and managerial frameworks.
Your grade will depend on how well you meet these learning
outcomes in the way relevant for this assessment.
Please see the final page of this document for further
details of the criteria against which you will be assessed.
Assessment Support
Specific support sessions for this assessment will be provided
by the module team and notified through NILE. You can also

102. access individual support and guidance for your assessments
from Library and Learning Services. Visit the
Skills Hub to access this support and to discover the
online support also available for assessments and academic
skills.
Academic Integrity and Misconduct
Unless this is a group assessment,
the work you produce must be your own, with work
taken from any other source properly referenced and attributed.
This means that it is an infringement of academic integrity and,
therefore, academic misconduct to ask someone else to carry out
all or some of the work for you, whether paid or unpaid, or to
use the work of another student whether current or previously
submitted.
For further guidance on what constitutes plagiarism, contract
cheating or collusion, or any other infringement of academic
integrity, please read the University’s
Academic Integrity and Misconduct Policy. Other
useful resources to help with understanding academic integrity
are available from
UNPAC - the University of Northampton’s Plagiarism
Avoidance Course.
N.B. The penalties for academic misconduct are severe and
include failing the assessment, failing the module and even
expulsion from the university.Assessment Submission
To submit your work, please go to the ‘Assessment and
Submission’ area on the NILE site and use the relevant
submission point to upload the assignment deliverable. The
deadline for this is 11.59pm (UK local time) on the date of
submission. Please note that essays and text-based reports
should be submitted as word documents and not PDFs or Mac
files.

103. Written work submitted to TURNITIN will be subject to anti-
plagiarism detection software. Turnitin checks student work for
possible textual matches against internet available resources and
its own proprietary database.
When you upload your work correctly to TURNITIN you will
receive a receipt which is your record and proof of submission.
If your assessment is not submitted to TURNITIN, rather than a
receipt, you will see a green banner at the top of the screen that
denotes successful submission.
N.B Work emailed directly to your tutor will not be marked.
Late submission of work
For
first sits, if an item of assessment is submitted late and
an extension has not been granted, the following will apply:
· Within one week of the original deadline – work will be
marked and returned with full feedback and awarded a
maximum bare pass grade.
· More than one week from original deadline – grade achievable
LG (L indicating late).
For
resits there are no allowances for work submitted late
and it will be treated as a non-submission.
Please see the
Assessment and Feedback Policy for full information on
the processes related to assessment, grading and feedback,

104. including anonymous grading. You will also find
Guidance on grades and resit opportunities from the
main University website. Also explained there are the meanings
of the various G grades at the bottom of the grading scale
including LG mentioned above.
Extensions
The University of Northampton’s general policy about
extensions is to be supportive of students who have genuine
difficulties in meeting an assessment deadline. It is not intended
for use where pressures of work could have reasonably been
anticipated.
For full details please refer to the
Extensions Policy. Extensions are only available for
first sits – they are not available for resits.
Mitigating Circumstances
For full guidance on Mitigating circumstances please go to
Mitigating Circumstances where you will find
information on the policy as well as guidance and the form for
making an application. Please also see
Extensions & Mitigating Circumstances guide 22_23
that compares your options.
Please note, however, that an application to defer an assessment
on the grounds of mitigating circumstances should normally be
made in advance of the submission deadline or examination
date.
Feedback and Grades
These can be accessed through clicking on the “Gradebook” on
NILE. Feedback will be provided by a rubric with summary
comments.

105. 3
2
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