111
VEHICLE COLOR GLOSSINESS STUDY USING SPC IN AN AUTOMOTIVE
MANUFACTURING COMPANY
AKHTAR RAZUL BIN RAZALI
A thesis submitted in fulfillment of the
requirements for the award of the degree of
Master of Engineering
Faculty of Mechanical Engineering And Manufacturing
Kolej Universiti Teknologi Tun Hussien Onn
NOVEMBER 2004
viii
ABSTRAK
Tahap kekilatan wama pada panel badan kenderaan adalah salah satu faktor yang
sangat penting dalam menentukan kejayaan penjualan kenderaan tersebut.
Berdasarkan kepada kenyataan mi, satu lawatan ke Malaysian Truck and Bus Sdn.
Bhd (MTB) telah dijalankan. Lawatan mi bertujuan mengenalpasti masalah berkenaan
banyak skrap (keija semula) terhasil daripada proses mengecat. Menurut MTB,
sebanyak RM1100 sebulan atau RIVI 13200 setahun telah diperuntukkan untuk
membaik pulih kecacatan ke atas kualiti mengecat yang dikesan. Oleh yang demikian,
laporan projek mi adalah mengenai analisis kualiti ke atas mutu kekilatan cat
menggunakan kaedah Kawalan Proses Statistikal (SPC). SPC direka sebagai salah
satu langkah pencegahan terhadap variasi dalam proses pembuatan. Dengan
menggunakan kaedah SPC terutamanya carta x dan R dan kajian kapabiliti (indek C',,
dan Ck), variasi yang berlaku di dalam proses mengecat yang disebabkan oleh
'assignable cause' telah dikenal pasti. Manusia, Mesin, Bahan (material) dan
Metodologi adalah faktor yang menyumbang ke arah kewujudan 'assignable cause'.
Keseluruhannya, proses mengecat kenderaan Mitsubishi Storm warna metallic mi
berada pada situasi luar kawalan dimana terdapat banyak titik plot dalam sebaris
berada di bawah garisan tengah disamping titik yang berada diluar had kawalan
proses. Keadaan mi dikenalpasti sebagai paten tidak natural (unnatural pattern). Kesan
faktor 4M mi ke atas proses mengecat menyebabkan nilai-nilai indek kapabiliti G
dan Ck yang diperolehi adalah agak rendah daripada sepatutnya dimana nilai sebenar
Cp = 1.22 dan Ck = 0.94 dan nilai sepatutnya ialah Ci,, = 1.33 dan Cpk = 1.00.
Berdasarkan faktor-faktor 4M, suatu langkah pencegahan dan pembetulan dirangka
bagi mengembalikan nilai purata proses kepada menjadi hampir atau sama dengan
nilai purata spesifikasi dan beberapa kaedah penyelesaian telah diusulkan.
Ix
ABSTRACT
High quality glossiness color for exterior body panel is critical to the success of
automobile sales because of the impact on the purchasing decision is relying on the
coating. Based on this statement, a visit to Malaysian Truck and Bus Sdn. Bhd.
(MTB) has been conducted. This visit regarding many reworks is produced in coating
process on the metallic color Mitsubishi Storm model vehicle. The reworks occurred
when the process failed to meet designated specification limit. As calculated, there is
about RM1 100 a month or RM13200 annum is spent for bad quality coating.
Therefore, this project has undertaken analysis and report on the statistical process
control (SPC) in the company to address this issue. SPC is invented as a prevention
tool to monitor manufacturing processes and becoming framework for preventive
action. By using SPC tools specifically the x and R chart and capability study which
is the C. and Cpk indices in this particular study, variation due to assignable causes in
the process has been identified where the 4M; Men, Machines, Methods and
Materials, causes become the main sources of variation. The C index shows how well
the process spread is compared to the specification spread and the Cpk index shows
whether the process is producing reject or not. Analysis has showed that the painting
process is in out-of-control situation. Even the C, index is 1.22 which is more than
1.00, the pattern on the control chart showing the unnatural ran process where many
points in a row fell below the centerline and some points located beyond the control
limits. The Ck index value is 0.94 and showing that rejects are produced in the
process. The variations cause the C and the Ck indices value are lessef than expected
value which are C = 1.33, and Ck = 1.00. Prevention action and countermeasures are
proposed in order to get the process back on its target based on the effect of the 4M.
x
TABLE OF CONTENT
CONTENT
PAGE
List of Tables
xiii
List of Figures
xvi
List of Symbols
xvii
List of Appendices
xix
CHAPTER 1— INTRODUCTION
1.1
Statistical Process Control (SPC)
1
1.2
Prevention versus Detection
3
1.3
The Need for SPC
5
1.4
Objectives
6
1.5
Scope of Study
7
CHAPTER 2— BACKGROUND
2.1
Applying SPC to an Existing Manufacturing Process
8
2.2
The Basic Tools for SPC
10
2.3
SPC Techniques
10
2.4
Defining the Problem
11
2.5
Determining the Quality Characteristic to be Measured
12
2.5.1
Concept of Variation
12
2.5.2
Causes for Variation in Manufacturing Processes
13
2.6
Making a Sampling Plan
13
2.7
Control Charts Method
14
2.8
The Average and Range (x and R) Chart
15
2.9
Establishing the Average and Range (x and R) Chart
17
2.10
Central Limit Theorem
18
2.11
Determining the Centerline for the x Chart
18
2.12
Determining the Control Limit for the x Chart
19
2.13
Determining the Control Limit for the R Chart
21
xi
2.14 Control Chart Interpretation
22
2.14.1 In-Control and Out-of-Control Processes
22
2.14.2 Analysis of Out-of-Control
24
2.15 Process Capability
27
2.15.1 Calculating the Capability Ratio
27
2.15.2 The Capability Indexes, C and Cpk
29
2.16 The MINITAB Software
30
2.17 Proposed Studied Company the Malaysia Truck & Bus
31
Sdn. Bhd. (MTB)
2.17.1 Malaysian Truck & Bus Sdn. Bhd. (MTB) Background
31
2.17.2 MTB's Quality Policies
32
2.18 Chapter Summary
34
CHAPTER 3— METHODOLOGY
3.1
Introduction
35
3.2
The Methodology
35
3.2.1
Literature Review
35
3.2.2 Product Selection
36
3.2.3
36
Determination of Quality Characteristic
3.2.4 Determination of Inspection Frequency/Sampling Size
Determination of Control Chart to be Used
37
3.2.6 Establishing Centerline & Control Limits
38
3.2.7 Process Capability and Performance Study
38
3.2.8
39
3.2.5
3.3
37
Conclusions
3.2.9 Recommendation and Future Works
39
Chapter Summary
39
CHAPTER 4— RESULTS AND DISCUSSIONS
4.1
Analysis on x and R Chart
42
4.2
Tests for Process Normality
42
4.3
x and R Chart Analysis
45
4.3.1 Manual x and R Chart Analysis
45
4.3.2 M1NITAB Analysis on x and R Chart
47
All
4.4
Interpretation of x and R Chart
48
4.5
Process Capability Analysis
48
4.5.1
Manual Analysis on C and Cpk
4.5.2 MINITAB Analysis on C, and Cpk
4.5.3
Example of Monetary Figure Calculation for the Loss
49
50
51
4.6
Causes of the Process Mean Shifted
53
4.7
The Cause-Effect of 4M
54
4.7.1
The Materials
54
4.7.2
Methods
55
4.7.3
Men
58
4.7.4 Machines
59
4.8
The Needs for Well-Controlled Painting Processes
61
4.9
Chapter Summary
61
CHAPTER 5— CONCLUSIONS
5.1
Conclusions
62
5.2
Future Works
63
REFERENCES
References
64
APPENDICES
Appendix A - Site Photos
66
Appendix B - Factory Layout
73
Appendix C - Other Related Documents
75
xlii
LIST OF TABLES
TABLE
DESCRIPTION
Table 1:
Factors for computing central lines and Ry control limits
PAGE
20
for x, s, and R charts [Smith, (2004)1.
Table 2:
MTB Plant Facilities [MTB Profile, (2004)].
Table 3:
Metallic color glossiness data taken from 4WD model
Mitsubishi Storm:
33
P43
xlv
LIST OF FIGURES
FIGURE
DESCRIPTION
Fig. 1:
The detection model [Smith, (2004)].
4
Fig. 2:
The prevention model [Smith, (2004)].
4
Fig. 3:
Average and range (x-bar and R) chart [Besterfield, (2002)].
16
Fig. 4:
Assignable-causes present operation zone [Besterfield, (2002)].
16
Fig. 5:
Relationship of limits, specifications and distributions
17
[Besterfield, (2002)].
PAGE
-
Fig. 6:
Three zones in in-control limits.
23
Fig. 7:
Some unnatural runs - process out of control [Besterfield,
24
(2002)].
Fig. 8:
Simplified rule for out-of-control process [Besterfield, (2002)].
24
Fig. 9:
Out-of-control pattern: change or jump in level [Besterfield,
25
(2002)].
Fig. 10:
Out-of-control pattern: trendy or steady change in level
25
[Besterfield, (2002)].
Fig. 11:
Out-of-control pattern: recurring cycle [Besterfield, (2002)].
26
Fig. 12:
Out-of-control pattern: two populations (mixture) [Besterfield,
26
(2002)].
Fig. 13:
Case 1, when 6 <usL-LSL [Besterfield,(2002)].
28
Fig. 14:
Case 2, when o = USL-LSL [Besterfield, (2002)].
28
Fig. 15:
Case 3, when & > USL-LSL [Besterfield, (2002)].
29
Fig. 1 6a:
The Overall Methodology Flowchart.
40
Fig. 16b:
Control Chart Construction Flowchart.
41
Fig. 17:
Glossy unit histogram.
Fig. 18:
Glossiness frequency histogram and normal curve.
44
AV
Fig. 19:
Plotted x chart for glossiness vs. subgroup no.
46
Fig. 20:
Plotted R chart.
46
Fig. 21:
MINITAB analysis on x and R charts.
47
Fig. 22:
Process capability analysis by MINTTAB.
52
Fig. 23:
Fishbone diagram of factors that affect mean to be shifted.
54
Fig. 24:
Spray gun patterns [Grundfor, (2004)].
56
Fig. 25:
Good spraying techniques [Grundfor, (2004)].
58
Fig. 26:
Manually preparation of metallic paint.
66
Fig. 27:
The paint is split into two bucket.
66
Fig. 28:
Dilution with thinner in the ratio of 2 part paint to 1 part thinner.
67
Fig. 29:
Dilution with thinner (continuation).
67
Fig. 30:
Viscosity measurement using Zahn cup.
68
Fig. 31:
Time taken for the diluted paint fully discharged from the Zahn
68
cup.
Fig. 32:
Paint spraying distribution equipment.
69
Fig. 33:
Vehicle in the baking oven.
69
Fig. 34:
Vehicle completely exits from baking oven.
70
Fig. 35:
Quality control checkpoint where glossiness measurement is
70
taken.
Fig. 36:
Glossiness meter PICOGLOSS Model 560 used to measure
71
body glossiness.
Fig. 37:
Quality Inspector measuring vehicle's glossiness rate.
71
Fig. 38:
Glossiness reading on PICOGLOSS Model 560 in glossiness
72
unit.
Fig. 39:
Studied vehicle, metallic color Mitsubishi Storm.
72
Fig. 40:
Overall MTB inter linkage processes between departments.
73
Fig. 41:
Overall MTB business process approach mapping.
74
Fig. 42:
Example of raw data sheet for color thickness and glossiness
on 28th April 2004.
75
Fig. 43:
Example of raw data sheet for color thickness and glossiness
on 29th April 2004.
76
Fig. 44:
Quality control inspection flowchart for painting process.
77
Fig. 45:
Vehicle's appearance zones.
78
AVI
Fig. 46:
Zone definition standard.
79
Fig. 47:
Specification of color thickness and glossiness.
80
Fig. 48:
Vehicle's inspection standard.
81
Fig. 49:
Standard of operation (SOP) for color glossiness measurement.
82
xvii
LIST OF SYMBOLS
X
-
Average of the subgroup averages
Xi
-
Average of ith subgroup
M
-
Number of subgroups
UCL
-
Upper control limit
LCL
-
Lower control limit
a-
-
Population standard deviation of the subgroup averages
R
-
Average of the ranges
R1
-
Individual range value for the sample
A2
-
Approximation factor used to calculate control limits
aR
-
Population standard deviation of the subgroup ranges
D3
-
Approximation factor used to calculate range chart
control limits
D4
-
Approximation factor used to calculate range chart
control limits
PCR
-
Process capability ratio
6s
-
6-sigma
CR
-
Capability ratio
USL
-
Upper specification limit
LSL
-
Lower specification limit
a
-
Standard deviation
CP
-
Capability index
Cpk-
Capability Index
CPU-
Single sided process spread capability index
-
Single sided process spread capability index
CPL
T
-
Tolerance (Specification tolerance)
d2
-
Approximation factor for calculating within subgroup
standard deviation
LIST OF APPENDICES
TITLE
APPENDIX
PAGE
A
Site Photos
B
Factory Layout
72
C
Other Related Documents
74
I
CHAPTER 1
INTRODUCTION
Statistical Process Control (SPC)
1.1
Today's automotive supplier industry competes in a global economy that imposes
rigorous and ever-increasing quality standards. Growing numbers of competitive
manufacturers are exposing quality. Dedication to constant improvement in quality
and productivity must be emphasized by each competing company. The quality of the
product has to be world class, as good as the best in the world, in order to compete.
Consumers are looking for the best combination of price and quality before buying.
The 'Made in Malaysia' or even the giant brand 'Made in the USA' labels are no
longer enough to sell a product. If Malaysian companies want to succeed in a global
market, their product must be competitive. Nowadays, each company employee must
be committed to the use of effective methods to achieve optimum efficiency,
productivity and quality to produce competitive goods. Due to each company
competing against each other to produce competitive goods, certainly, everyone has
felt the push from management and the pull from customers for improved quality.
Thus, the consistent, aggressive and committed use of Statistical Process Control
(SPC) to bring all processes under control, recognize and eliminate special causes of
variation, and identify the capability of all operations is a key requifement. SPC is
defined as prevention of defects by applying statistical methods to control the process
[Summers, (2003)]. Others define SPC as a procedure in which a data is collected,
organized, analyzed and interpreted so that a process can be maintained at its present
level of quality or improved to a higher level of quality [Smith, (2004)].
When a company produces a product or service, it utilizes many interrelated processes
and each process involves several to many steps to accomplish a specific task. There
may be several sources of data available as the task is undertaken and measurements
that have to be within specified limits or outcomes to be judged acceptable or notacceptable. All the different processes are combined to yield the final product or
service.
SPC can be applied whenever work is being done. Initially, it was applied to just
production processes, but is has evolved to the point where it is applied to any work
situation where data can be gathered. As companies work toward a total , quality goal,
SPC is used in more diverse situations.
SPC involves the use of statistical signals to identify sources of variation, to improve
performance and to maintain control of processes at higher quality levels. The
statistical concepts that are applied in SPC are very basic and can be learned by
everyone in the organization. All workers must know how SPC applies to their
specific jobs and how it can be used to improve their output.
SPC does not meant to workers only but supervisors also must acknowledge to the use
of SPC. Supervisors must be aware of the ways SPC can be used in their sections, be
prepared to help their production workers utilize SPC and be receptive to' suggestion
for improvements from the workers who are effectively using SPC. Manager also
must know how SPC can be used to improve quality and productivity simultaneously.
When supervisors recommend process changes based on SPC analysis and
interpretation, managers must understand the SPC' concepts in order to make
knowledgeable decisions. The company must create and maintain a management style
that emphasizes communication and cooperation between levels and between
departments. The goal must be set to develop a working atmosphere that maximizes
everyone's contribution to the production of competitive products.
Implementing SPC in manufacturing process is crucial as SPC will increase the
product quality and hence reduce the total rework and scrap costs. SPC is the best
prevention action where any out-of-control or unnatural process can be traced and
prevention countermeasure can be done in order to prevent the process producing
J
rejects. Hence, the purpose of implementing SPC in manufacturing process enables
the company to reduce reject costs and improve their product quality through low cost
monitoring process, the SPC.
1.2
Prevention versus Detection
One of the major problems in manufacturing today is that some companies' version of
quality control is simply to find defective items after they are made and remove them
before shipment to the customer. When mistakes are found in service industries,
documents are redone and corrected. If the mistakes involved the customer, apologies
must be made. These are examples of trying to achieve quality through the detection
method. The quality of the system has not improved, even though some inferior
products were weeded out or some mistakes were found and corrected. SPC on the
other hand leads to a system of prevention. SPC emphasizes the prevention of defects
rather than detection. Prevention refers to those activities designed to prevent defects,
defectives and nonconformance in products and services. The most significance
difference between prevention and inspection is that with prevention, the process
rather than solely the product is monitored, controlled and adjusted to ensure correct
performance.
Detection models, as Fig. 1 illustrates, usually rely on a corps of inspectors to check
the product at various stages of production and catch errors. This quality control
method is inadequate and wasteful. Money, time and materials are invested in
products or services that are not always usable or satisfactory. After the fact
inspection is uneconomical and unreliable. Inspection without analysis of the process
and subsequent action neither improves nor maintains product quality. Inspection
plans cannot find all the defective items, and the waste is appalling. The company
pays an employee to make the defective item and then pays the inspectors to try to
find it. If the inspectors find it, the company pays another employee to fix it. Also,
defective products that are not found lead to warranty costs, damaged reputation and
cancelled orders. Unless action is taken to correct the faulty process, the percentage of
the output that is defective will remain constant.
-t
Fig. 1: The detection model [Smith, (2004)].
The prevention model uses statistical signals at appropriate points in the process to
improve the procedure and to maintain control at the improved level. Statistical signal
provide an efficient method for analyzing a process to indicate where improvements
should be made to prevent the manufacture of defective items and to improve the
quality of the items produced. This is illustrated as in Fig. 2. Prevention avoids waste.
If the product is not flawless at the first time, fix the process so that the products will
be right again the next time. The process then monitored so that needed adjustments
can be made before quality suffers.
SPC is becoming the core for both quality improvement and quality maintenance.
Important decisions from optimum adjustment time decisions made at the shop floor
level to process change decisions made by management involve SPC. Statistical
method and techniques, such as control chart analysis of a process or its output, are
now being used extensively to make economically sound decisions. The process
analysis leads to appropriate actions for achieving and maintaining a state of statistical
control and for reducing process variability.
Output
Process
Improve
Inspect
An
Fig. 2: The prevention model [Smith, (2004)].
-7
1.3 The Need for SPC
sPc also seeks to limit the variation present in the item being produced or the service
being provided. There are two types of variation; common-cause and special-cause.
While it once was considered acceptable to produce part that fell somewhere between
the specification limits, SPC seeks to produce part as close to the nominal dimension
as possible and to provide services of consistent quality from customer to customer.
To relate loss to only those costs incurred when a product or service fails to meet
specifications is unrealistic. The losses may be due to reduced performance levels,
marginal customer service, a slightly shorter product life cycle, lower product
reliability, or a greater number of repairs. It ca be simplified that losses occur when
the customer has a less-than-optimal experience with the product or service. This is
because the larger the deviation from the desired value, the greater the losses. These
losses occurred regardless of whatever or not the specifications have been met.
Reducing process variation is important because any reduction in variation will lead
to a corresponding reduction in loss. SPC goals can be described as follows:
• Minimize production costs. This goal is accomplished by "make it right the
first time" program. This type of program can eliminate costs associated with
making, finding and repairing or scrapping substandard products.
• Attain a consistency of products and services that will meet production
specifications and customer expectations. Reduce product variability to a level
that is well within specifications so the process output will match the desired
design quality. This consistency leads to process predictability, which benefits
the company by helping management meet quantity targets.
• Create opportunities for all members of organization to contribute to quality
improvement.
• Help both management and employees make economically sound decisions
about actions affecting the process.
SPC goals also can be adopted by company as their quality statement mission. This is
due to currently almost all companies have such statement in their company's visions.
As stated earlier, SPC can be used by both management and production people
because it includes statistical methods that utilize the expertise of all employees of the
Company by problem solving. Management can use SPC as an effective tool for
reducing operating costs and improving quality by using its methods for organizing
and implementing the quality effort. The various processes can be charted and product
flow can be analyzed at any point of the process. Bottlenecks and quality problems
can be pinpointed quickly for management planning and solutions. The capability of
the processes to attain desired quality levels could be analyzed statistically for
management decisions regarding overhaul or replacement of machinery. Analysis of
control of charts can lead to training, retraining or reassignment decisions regarding
production workers. Management decision on recommendations for process changes
can be based on a combination of statistical analysis and cost analysis. The entire
process becomes predictable so managers can achieve better planning for quantity
targets. SPC creates a new management philosophy: Lines of communication are
opened among all employee levels for the betterment of the company and the product.
SPC also works for production employees. These employees can use SPC to develop
effective tools to work more efficiently (not necessarily harder but worm smarter and
effectively). When employees learn SPC, they worked more intelligently. They know
from their control charts when they are doing a good job. SPC gives them a good
opportunity to influence work operations and be responsible for their jobs. It can help
increase employees' pride in their work by allowing them input into the production
process. Production workers are often the most qualified employees to determine what
is wrong and what is right with their particular step in the process. As contributing
members of a process control team, they can help with quality improvement.
1.4
Objectives
Three specific objectives have been defined to simplify the main objective of the
project. There are:
i.
To carry-out a case study of glossiness quality of a vehicle.
ii.
To implement a quality control technique for a selected vehicle body panel.
iii.
To propose some improvement on coating process from the study.
Scope of Study
1.5
The scope defines the manufacturer background, boundary and population of the
process to be study. The scopes of study are:
1. A literature review has been conducted to determine the best quality tool.
2. A case study has been conducted on current coating process in Malaysian
Truck and Bus Sdn. Bhd.
3. To analyze the collected metallic color glossiness data from Mitsubishi Storm
model vehicle.
These definitions are essentially needed to narrow down the study scope from the
wide topic of SPC.
Manufacturer: MALAYSIAN TRUCK & BUS SDN. BHD.
Boundaries: SPC includes those tools used during production to eliminate
unexpected causes of variation. Type of taken data is metallic color glossy rate
on the right side of the studied vehicle's body.
Population: The study subject is production of Metallic Color Mitsubishi
Storm vehicle using brand NIPPON paint and lacquer.
Time period: One month data, taken from the production of studied subject.
CHAPTER 2
BACKGROUND
2.1
Applying SPC to an Existing Manufacturing Process
When SPC is introduced to a process, the initial thought should be about the quality
characteristics. What are they and what are their associated measurement
requirements? In most situations, a statistical quality control (SQC) analysis would be
done first by identifying the quality characteristics are critical to quality (CTQ) and
determining how those characteristics can be controlled. As an example, when an
automobile transmission or a four-wheel-drive transfer case is made, one of the
quality characteristics is the noise (or lack of it) at various speeds. The SQC analysis
of the noise at specific speeds could then lead to several SPC charts of CTQ
measurements in the manufacture of the gears that would contribute to the noise
factor. Below are the steps in brief of existing SPC in manufacturing process.
1.
The initial step in SPC is diagramming and analyzing the process to
decide where control charts may be best to applied.
2.
Decrease any obvious variability in the target process.
3.
The third step involves statistically testing the gauges using a gauge
capability study. This must be done before measurements are taken for
control charting. The variation that shows up on the control charts must
reflect the process variation that needs to be reduced.
4.
Make a sampling plan. Determine the size of the sample and when the
samples are to be taken.
5.
By using the control chart, find the out-of-control situation caused by
common-cause and special-cause, evaluates what happened at that
specific time to cause it, and then work to prevent that cause. This
procedure continues until the control chart indicates that there are no
more special-cause variation problems. By this time, the process is
running as well as it possibly can without process modifications and it
is said to be in statistical control.
6.
The sixth step is to put operator in-charged. This step and step 5
actually occurred simultaneously because the operator should be doing
the control charting and attaining statistical control with the help of the
process control team.
7.
This step is to determine how capable the process is according to
product specifications and customer expectations.
8.
This step is designed to improve the process. Eighty-five percent or
more of the process problems are handheld at this stage, according to
quality consultant W. Edwards Deming. At this stage, process changes
can be analyzed on control charts either singly or in variable
interaction studies for signs of process improvement. Designed
experiments may also be used in the search for improvements. When
improvements are found, management must follow thru and see the
appropriate changes are incorporated in the process without
backsliding.
9.
The ninth step calls for a switch to pre-control, a monitoring technique
that compares a measurement with target and warning measurements,
when the process is in control and capable.
10.
Quality improvement is a continuous process. Two things should be
done at this step; first, continue to look for ways to improve the
process at hand and second, return to step 1 for the next critical
measurement.
Historically, many companies did not begin using SPC until they were forced. Either
they could see their competitive position diminishing or they were obliged to meet
their customer's requirement that contracts would not be awarded until their
W orkforce was trained in SPC. Unfortunately, some companies just met the minimum
5.
By using the control chart, find the out-of-control situation caused by
common-cause and special-cause, evaluates what happened at that
specific time to cause it, and then work to prevent that cause. This
procedure continues until the control chart indicates that there are no
more special-cause variation problems. By this time, the process is
running as well as it possibly can without process modifications and it
is said to be in statistical control.
6.
The sixth step is to put operator in-charged. This step and step 5
actually occurred simultaneously because the operator should be doing
the control charting and attaining statistical control with the help of the
process control team.
7.
This step is to determine how capable the process is according to
product specifications and customer expectations.
8.
This step is designed to improve the process. Eighty-five percent or
more of the process problems are handheld at this stage, according to
quality consultant W. Edwards Deming. At this stage, process changes
can be analyzed on control charts either singly or in variable
interaction studies for signs of process improvement. Designed
experiments may also be used in the search for improvements. When
improvements are found, management must follow thru and see the
appropriate changes are incorporated in the process without
backsliding.
9.
The ninth step calls for a switch to pre-control, a monitoring technique
that compares a measurement with target and warning measurements,
when the process is in control and capable.
10.
Quality improvement is a continuous process. Two things should be
done at this step; first, continue to look for ways to improve the
process at hand and second, return to step 1 for the next critical
measurement.
Hi storically, many companies did not begin using SPC until they were forced. Either
they could see their competitive position diminishing or they were obliged to meet
their customer's requirement that contracts would not be awarded until their
Workforce was trained in SPC. Unfortunately, some companies just met the minimum
requireme nt of providing basic SPC training and discovered that it's was not good
enough.
Both workers and supervisors must understand the essential of the SPC for their own
good to prevent losses becoming routine. The management also must have good
interpretation on SPC where when workers suggestion is made based on SPC
analysis, the management should not neglect their recommendation.
2.2
The Basic Tools for SPC
The seven basic SPC tools are:
• Flowchart
• Pareto chart
• Checksheet
• Cause-and-effect diagram
• Histogram
• Control chart, and
• Scatterplot
However for the purpose of case study, control chart is chosen to be implemented as a
SPC tool. Control chart is a broken-line graph illustrates how a process or a point in a
process behaves over time. Samples are periodically taken, checked or measured and
the results plotted on the chart. The charts can show how the specific measurement
changes, how the variation in measurement changes or how the proportion of
defective pieces changes over time. Control chart are used to find sources of specialcause variation, to measure the extent of common-cause and to maintain control of a
process that is operating effectively.
2.3
SPc Techniques'
Essential techniques in SPC include the use of:
1.
Process control chart to achieve and maintain statistical control at each
phase of the process.
2.
Process capability studies that use control chart to access process
capability in relation to product specifications and customer demands.