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Engineering, Technology & Applied Science Research Vol. 9, No. 6, 2019, 5062-5065 5062

www.etasr.com Memon et al.: Controlling the Defects of Paint Shop using Seven Quality Control Tools in an …

Controlling the Defects of Paint Shop using Seven

Quality Control Tools in an Automotive Factory

Imdad Ali Memon

Department of Mechanical Engineering
Quaid-e-Awam University of

Engineering, Science & Technology

Nawabshah, Pakistan
engineerimdad@yahoo.com

Ahmed Ali

Department of Electrical Engineering
Sukkur IBA University

Sukkur, Pakistan

ahmed.shah@iba-suk.edu.pk

Munawar Ayaz Memon

Department of Electrical Engineering
Quaid-e-Awam University of

Engineering, Science & Technology

Nawabshah, Pakistan
engr.mam@gmail.com

Umair Ahmed Rajput

Department of Mechanical Engineering
Quaid-e-Awam University of

Engineering, Science & Technology

Nawabshah, Pakistan
engr.umair@quest.edu.pk

Saeed Ahmed Khan Abro

Department of Electrical Engineering
Sukkur IBA University

Sukkur, Pakistan

saeed.abro@iba-suk.edu.pk

Ahsan Ali Memon

Department of Mechanical Engineering
Quaid-e-Awam University of

Engineering, Science & Technology

Nawabshah, Pakistan
15el06@quest.edu.pk

Abstract—Seven quality control (7QC) tools are used for

reducing defects during manufacturing. These tools are highly

effective in productivity and quality improvement. In this case,

the study of the 7QC tools was applied in an automotive factory
in order to reduce paint shop defects. Within four months the

production line was inspected, defects were categorized and the

7QC tools were successfully applied, reducing the overall defect

rate by 70%. Although every tool was important, the cause and

effect diagram was responsible for finding the root causes of the
defects.

Keywords-defects reduction; productivity improvement; paint

shop; 7QC tools

I. INTRODUCTION

The Seven Quality Control (7QC) tools are highly useful
for improving productivity, resolving problems in the quality
operational process and delivery [1, 2]. The 7QC tools are
applied for improving the performance of the production
processes, solving problems at any stage [3, 4]. Solving
problems by the 7QC tools reduces cost, while they cannot be
replaced by any other complex decision-making support system
[5]. The level of defects or problems in the product is
associated with the process conditions. If the process is under
control limit then the product is useful, while when the process
is out of control the product has demerits resulting to rejection,
rework or scrap [6]. These problem solving tools are directly
beneficial for customers too, as quality products entail the vast
reduction of defects, while this process reduces cost. Improving
the quality of the product is very important for any company
and its endurance in the market. The 7QC tools can be used in
the production processing line ensuring the reduction of defects
while suggesting improvements [7]. Statistical Process Control
(SPC) tools used to hold the position of 7QC tools. These tools
have played an important role in the reduction of variations in

many industries [8]. Many studies focused on these tools which
were found very effective regarding problem solving in many
industries. A comparative study was conducted between them
and the new 7QC tools [8]. It has been reported that there are
more than two SPC techniques, namely the Ishikawa diagram
(cause and effect diagram) and the SPC control charts which
were also applied in automotive industry. The study in [9]
focused on the defects in shocker seals of the automotive
industry. The rejection level was reduced from 9.1% to 5% and
95% process capability was achieved. The control chart alone
was applied to automotive components and monitored process
capability. It was reported that the defect level has been
reduced using a data acquisition system. The automated
inspection method was adopted and offline SPC method was
converted into the online method in [10]. In many companies,
more complex quality tools were used, but they were not highly
effective and also they were not able to examine defects at a
proper level [11].

In today’s challenging environment, every organization
should apply proper productivity improvement tools. This
paper presents a case study of the application of the 7QC tools
in an automotive factory. Initially, the factory used partially
some of the tools without getting fruitful results. A goal to
apply all the 7QC tools and understand their implementation
mechanism was set, focusing on the identification and
reduction of the defects occurring in the paint shop. An
inspection point of the paint shop was used in order to collect,
assess and analyze data.

II. RELATED WORK

The 7QC tools are applicable to any kind of industry
regardless of size and capacity [11, 12]. These techniques (flow
chart, Pareto diagram, check sheet, control chart, histogram,

Corresponding author: Imdad Ali Memon

Engineering, Technology & Applied Science Research Vol. 9, No. 6, 2019, 5062-5065 5063

www.etasr.com Memon et al.: Controlling the Defects of Paint Shop using Seven Quality Control Tools in an …

scatter plot, cause-and-effect diagram) aim to control quality
parameters such as methods, machine, equipment, and
products. Hence, these tools reduce the loss and give more
profit [13]. In [7], the 7QC tools were used for reducing the
rejection of shift fork. The total rejection was reduced from
16.66% to 0.65%, saving Rs 303.000 per year. In [14] the cost
impact of the application of real time SPC in hardwood
sawmills was investigated. In [15], some of the basic statistical
tools were selected, such as Pareto diagram, control chart and
histogram, studying their impact on the overall process
performance, cost and product quality. In [16], the importance
of the 7QC tools was examined, showing a continuous quality
improvement process in an automotive industry, while in [17]
those tools were used for productivity improvement. Many
companies adopted SPC tools and continued the
implementation of process control in manufacturing industries.
These techniques fulfill the customer requirements for high-
quality products at low price. Manufacturing firms use SPC to
analyze their impact, isolate problems, monitor the outcome
and process parameters for achieving quality goals [18, 19].
Moreover, the SPC process can enhance problem solving and
performance [9, 20].

Paint is a dispersion of pigments. It is used as filler in a
fluid vehicle. The fluid vehicle includes a liquid binder that
was solidified during cure. It has the capability to serve as a
liquid carrier, viscosity reducing aid, and also provide healthy
application distinctive [21]. Drying oil, volatile thinner, and
paint have been developed by mixing, grinding, thinning,
straining operations [22]

III. MATERIALS AND METHODS

Data collection started from the assessment of the
automotive factory, through frequent visits. After visual
inspection, defects were categorized in four types: dust,
floatation, scratch, and improper paint. Then, defects having a
direct impact on the car body were focused. Therefore, a check
sheet was developed for finding the root cause of paint defects,
applying the cause and effect diagram technique. This
technique is very useful for the reduction of body section
defects [23, 24]. Data were collected through the check sheet
for four months, using the same pattern. These data can be
simulated, as reported in [25].

IV. RESULTS ΑND DISCUSSION

A. Application of the 7QC Tools

• Flow chart (QC Tool 1): It was developed for collecting
data from the paint shop. The defects observed at paint shop
are of four types: scratch, dust, improper paint and
floatation.

• Check sheets (QC tool 2): After identifying the defects, the
next step is data collection. The check sheets were
developed for collecting defect data for further analysis,
and were designed focusing on attributted data, providing
simple checkmarks, where check inspectors marked defect
occurences. The check sheets mentioned different types of
defects in the paint shop. Data were collected from
November 2015 to February 2016.

• Histograms (QC tool 3): Histograms were created using
Excel. The histograms display variation levels and process
capability. Occurrence frequency of each defect was
displayed in a monthly histogram between November 2015
to February 2016, as shown in Figure 1.

Fig. 1. Histogram for paint shop defects

• Pareto diagrams (QC tool 4): A Pareto diagram shows the
defects in descending order of occurrences. The Pareto
diagram is also monitoring the defects cumulative
frequency level occurrences on its secondary axis. The red
trend line shows the defect cumulative percentage level.
Defects occurring at a high level should be given priority.
The Pareto diagrams are displayed in Figure 2.

Fig. 2. Pareto charts of paint shop defects for (a) Nov-15, (b) Dec-15,

• Cause and effect diagram (QC Tool 5): The cause and
effect diagram helps in discovering the root causes of the
problem. This tool can point out the defects and their
reasons. The preliminary data collected through check
sheets showed a high frequency of defects in November and
December 2015. Cause and effect diagrams were developed
and applied in the paint shop. Using check sheets the data
were collected for January 2016 and February 2016,

Engineering, Technology & Applied Science Research Vol. 9, No. 6, 2019, 5062-5065 5064

www.etasr.com Memon et al.: Controlling the Defects of Paint Shop using Seven Quality Control Tools in an …

showing considerable reduction in paint defects, due to
control on root causes as pointed out in Figure 3.

• Scatter diagram (QC tool 6): This tool was used for the
study and evaluation of the impact of each parameter to
another. Fig. 4 shows the scatter diagram of the paint shop.
This diagram shows time in weeks and the number of
defective bodies. In the first eight weeks, the defect rate
was too high, while after finding the root cause of defects
through cause and effect diagram, it was observed that the
occurrence rate of defective bodies was reduced.

Fig. 3. Cause and effect diagram for paint shop of: (a) improper paint,

(b) floatation, (c) scratch, and (d) dust

Fig. 4. Scatter diagram

• p-chart (QC tool 7): The p-chart was based on the number
of paint defect occurrences using binominal distribution.
The process, running either in statistical control or not, can
be pointed by the P-chart. It also pointed out that the
changes occurred in the defective items when process
measurement took place.

B. Overall Defect Reduction

Figure 5 shows the NP control chart for the paint shop,
where the blue line shows the defect rate, the green line shows
the upper control limit, the red line shows the center and the
purple line shows the lower control limit. Nine weeks after the
implementation of the 7QC tools, the defections were
drastically reduced. Figure 6 shows the impact on the defect
rate before and after the implementation of 7QC tools in the

paint shop. 122 defects occurred during the first month and 155
occurred during the second. After using the 7QC tools the
defects were reduced to 83 during the third month and to 47
during the fourth.

Fig. 5. NP control chart

Fig. 6. Reduction of defects after the application of the 7QC tools

V. CONCLUSION

This research investigated the application of the 7QC tools for
the reduction of defects it an automotive factory. The initial
flow chart was developed and check sheets were designed for
data collection on inspection points. It was observed that the
highest frequency defects were seen in November and
December, 2015. After using the cause and effect diagram, the
defects reduced substantially. During the fourth month
(February 2016) total defects reduced by 70%, comparing to
the first month, from 155 to 47. Every tool played an important
role in the defect reduction, but the cause and effect diagram
was very useful for finding the cause and its effect. The main
contribution of this study is to highlight all the possible defects
or errors affecting production in manufacturing industries.

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