RESEARCH ARTICLE

Uneven wall thickness would result in thinning of the part. To
minimize uneven wall thickness of the drawn part, the alignment between the
punch and die should be controlled. Severe misalignment between the punch
and die would result in undesired thinning of the part and failure initiation by
tearing. This misalignment could be caused by incorrect tolerances used
during die fabrication or alignment inaccuracies in the assembly of the die.
Such misalignment and dimensional inaccuracies can be reduced by precision
workmanship during those stages. Vairavan and Abdullah5, in their studies on
deep drawing of square cups without blank holders, have remarked that that
alignment between punch and die is important for obtaining high quality
parts. Slavič et al.6 have found that misalignment between punch and die will
increase the drawing force, although their study was limited to circular
blanking process only. In contrast to cylindrical cup shape where the
pressure is uniform on all area, rectangular shell requires increased force due
to additional corners on the shape. This was the conclusion of Tamaoki et al.7
whom have stated that stress concentration in a drawing process for a
rectangular shaped part is higher than a cylindrical cup shape. Younis and
Jabber8 found that thinning of a square cup deep drawing usually appear in
the corners of a part due to excessive stretching in those regions. Behrens et
al.9 also concluded that the degree of punch force has a more pronounced
influence in a rectangular cup profile as compared to cylindrical cup shape
when die radius variation was studied.

In recent years, integration of computer-aided technology in the
stamping industry has opened up a new era in the production of high quality
stamped components10. These technologies are commonly implemented in
pre-production as a predictive tool, such as in the use of computer simulation
software for deep drawing processes. For examples, Ogawa et al.11 have

93

utilized LS-DYNA, a finite element analysis (FEA) simulation software, to
acquire early knowledge on the formability of square cup deep-drawing of
pure titanium, which was then verified by experimentations. Yang et al.12
were able to propose an optimal blank shape on deep drawing process of
trapezoidal and square cups with the use of DEFORM-3D, another
commercial FE package. Similarly, Reddy13 used FEA to determine the
formability of warm deep drawing for AA1050-H18 rectangular cup while
Zein et al.14 also used FEA to predict thinning and spring back conditions of
sheet metal in a deep drawing process. Results of FEA simulation enable
early prediction of the forming process which improves the overall
production flow in term of reduced costs, decreased fabrication time and
increased part quality. Computer-aided technology can also be used in
supervision and surveillance of product quality control. These are usually
packaged as a smart automation technology, in which various sensors and
computer-aided monitoring equipment are used to detect part quality
automatically. In a forming operation, the technology can be implemented
before the drawing process (pre-forming), during process (in-situ) or after a
process (post-forming). Past researchers have proposed vision and non-visual
methods of evaluation which can be integrated in the forming production
process. For example, Garcia15 has used an artificial intelligent method to
detect wrinkles and crack formation during a drawing process. In addition,
Zoech et al.16 have used electromagnetic and micromagnetic NDE technique
to detect online crack and thinning while Kibe et al.17 have used a
commercial vision system to detect alignment between the punch and die
before performing a shearing process. Similarly, Berger and Zussman18 have
used a novel measurement technique based on noncontact ultrasonic-based
gauge to detect online wall thickness distribution of circular part during deep

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drawing processes. Among these methods, the computer-aided vision system
is deemed to be the most suitable for integration into a deep drawing process.
The vision system can be used as a pre-forming inspection of the alignment
between punch and die in deep drawing process to detect the severity of
misalignment of the components.

This paper proposes a pre-forming inspection system to detect the
misalignment between the punch and the die for a square cup deep drawing
process. This inspection procedure would be performed before the actual
drawing process takes place. The inspection system consists of a vision
system and a computer algorithm developed in MATLAB. The captured
images of the punch and die is analyzed using MATLAB based algorithm for
centroid recognition. The offset values between centre of the punch and the
centre of die indicate the severity of misalignment between the punch and
die.

2. MODEL DEVELOPMENT

A. Equipment Setup
Figure 1(a) shows the schematic diagram of the system developed in

this study. The system consists of a digital camera, a back-lighting system, an
anti-glare mirror and the die set. A push-through draw dies type with a blank
holder was selected for use in this system. This type of die has a blind slot in
the die block that enables the blank to be drawn into it via a die insert, as
shown in Figure 3. Each component of the die is mounted on a center-post
die set type having two guideposts on the right and left of the die shoes. The
punch is mounted on the upper die shoe using a punch holder and the die
insert is mounted on the lower die shoe by using a die block. The flat surface

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of the punch was aligned to be parallel with the surface of the die insert using
the blank holder. The shape of punch is square with the dimensions of
13.6mm x13.6mm, while the punch nose radius, RPN and the punch corner
radius, RPC, are both 2.5mm. For the die insert, the dimensions of square
opening size are 15.74mm x 15.74mm, the die radius, RD, is 5mm and the die
corner radius, RDC, is 3.57mm. The clearance value, C, between the punch
and the die is 1.07mm. Figure 2 shows the overall dimensions of the punch
and die insert.

As shown in Figure 1(b), the digital camera is mounted on a tripod
facing the front of the die set. The digital camera has a 13 Megapixels sensor
with an aperture size of f/1.9. In this study, the camera resolution was set to
4128×3096 pixel (4:3, 12.78MP), with focus length of 0.15m, ISO 500 film
setting and a shutter speed 1/100 second to ensure clear observation of the die
set. The height of camera is adjustable to ensure that the center of focusing
lens is always straight and parallel with the center of image reflected off the
mirror. The anti-glare mirror, tilted at a 45° angle, provides a clear image of
punch and die insert. A mirror holder is mounted below the die block and
located at the center of the die block opening. The back lighting increases the
ambient illumination, reducing undesired image noise in the image captured.
The lighting system consists of two 15 Lumens LED white light is, arranged
to illuminate the image capture area. The auto focusing and auto exposure
modes were disabled to ensure a consistent image setting was used for all
image captures. The captured images are then uploaded into MATLAB
software for subsequent image processing.

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Die set
Punch
Die
Slanted

LED

Camera

(a) Schematic diagram (b) Actual setup of the system
arrangement of system

Figure 1: Arrangement of the system

15.74mm C
Punch
13.6mm

RPC Die RDC

Figure 2: Dimension of punch and die

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Punch

Blank
holder
Die insert
Die block

Figure 3: Cross-section view push through draws die

B. The Proposed Algorithm
In this study, a centroid recognition method was used to detect

misalignments between the punch and die. The Image Processing Toolbox
function in MATLAB was used to process the captured image (raw image) to
obtain the centroids of the punch and die. Variations of the centroid values
between the punch and die indicates the extend of misalignment between the
two components. The raw image needs to undergo several image analysis
processes prior to the centroid detection procedure. Firstly, the raw image is
calibrated and transformed appropriately. Then, image noises are removed
through an image restoration process. Color segment detection function is
then used to detect the surface of punch while the edge detection function is
used to detect the edges of die. The punch is painted red for the color
segmentation process. For the die, the canny method of edge detection was
used to define the edges of the die. The image is then converted to into a
binary image and morphological filtering is performed to remove unwanted
pixels and to obtain an improved binary image of the punch and die. The
centroid of the punch and die can then be obtained by using image region

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properties function. Figure 4 shows the process of image processing and
analysis to obtain the centroids of the punch and die.

Raw image Image Image
display calibration restoration

Centroid Binary Image Image
recognition & segmentation

Morphological & edge
Filtering detection

Figure 4: Image processing procedure flow chart to determine centroid

The captured raw image needs to undergo appropriate cropping and
transformation procedures before it can be further processed. Since the
position of the camera is not parallel to the image appearing on the 45° tilted
mirror, as shown in Figure 5(a), appropriate corrections needs to be
conducted to correct the projection of the image. This can be achieved by
simple geometrical transformation such as rotating and cropping to obtain an
improved image, as shown in Figure 5(b).

Punch Edge
of die

Die

(a) (b) (c)
Figure 5: (a) Raw image (b) Image after geometrical transformation (c)

Edge detection of die

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Several edge detection methods, such as Sobel, Prewitt and Roberts
can be used. However in this study, the more efficient Canny edge detection
method is selected. Figure 5(c) shows result of Canny edge detection for the
die. For comparison, the uncolored metal punch is shown in Figure 6(a). It
was seen that an uncolored punch located deep inside the die was very
difficult to be observed clearly and the metallic surface was very reflective.
This reflectivity affected the visibility of the punch and edge, especially for
the edge detection process, with increased image noise resulting in an
inaccurate calculation of the centroid. This problem can be overcome by
coloring the punch, and then using the color segmentation procedure to
identify the punch surface area. Figure 6(b) shows an inaccurate centroid of
punch (star mark) due to unclear punch edge identification. In comparison,
the centroid of the colored punch is more precise due to distinct edge profile,
as shown in Figure 6(c).

The resolution of the raw image also plays a role in the accuracy of
centroid detection. Low resolution image also resulted in inaccurate centroid
detection. As shown in Figure 7, a low image resolution of 480×640 pixels
resulted in a blurry image of the punch edge, and the centroid detected was
therefore inaccurate. Using a high-resolution raw image is therefore
recommended for accurate centroid detection.

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Punch

Die

(a) Image of (b) Inaccurate centroid (c) Centroid of colored
original punch
of punch punch (red)

Figure 6: Comparison accuracy of centroid between original punch and a red
colored punch

(a) Raw image of 480×640 pixels (b) Centroid detection of punch

Figure 7: Detection of punch centroid for raw image with 480×640 pixels

Figure 8 shows the centroid value for an image of a punch and die
configuration. The difference between the centroid values of the punch and
die were (4.0696, 2.248) pixels. Using a conversion factor, this correspond to
a displacement of (0.015, 0.05) mm, indicating the punch is slightly to the
left and towards the top of the die.

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Figure 8: Centroid of punch and die
Table 1 shows the example of applicability of the proposed technique
in determining the degree of misalignment of the punch and die for a deep
drawing process. Three images were captured and analyzed in an attempt to
firstly determine and then to align the centroid of the punch with the centroid
of the die using the developed system. The initial offset is calculated from the
original image. Once the offset was determined, the first attempt (Attempt 1)
was made to align the centroids of the punch and die. The second attempt
(Attempt 2) is to fine tune the alignment, based on the offset values
calculated from Attempt 1. The results have shown that after the second
attempt, the centroids of the punch and die were able to be aligned with
minimal offset values.

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Table 1: Result of offset values between original condition and after
modification

Offset value centroid

Image Status punch-die, mm
Original Condition
X axis Y axis

0.411 -0.326

Attempt 1 0.204 -0.047

Attempt 2 -0.008 0.046

4. CONCLUSION

In this work, a computer vision based pre-forming inspection system
to detect misalignments between punch and die for square shape cup has been
developed successfully. Preliminary results showed that the proposed system
has successfully detected the centroids of the punch and die of a deep
drawing die set.

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Future works would concentrate on the drawing process of the aligned

punch and die using a Universal Tensile Machine (UTM). Result from

experimental evaluations such as the punching loads and wall thickness

distribution of the part would be compared with the results from FEA

simulations.

REFERENCES

1. Dwivedi R and Agnihotri G 2017 Study of Deep Drawing Process Parameters Mater.
Today Proc. 4 820–6

2. Joshi A R, Kothari K D and Jhala R L 2013 Effects Of Different Parameters On Deep
Drawing Process: Review Int. J. Eng. Res. Technol. 2 1–5

3. Hill R 1948 A Theory of the Yielding and Plastic Flow of Anisotropic Metals Proc. R.
Soc. A Math. Phys. Eng. Sci. 193 281–97

4. Colgan M and Monaghan J 2003 Deep drawing process: analysis and experiment J.
Mater. Process. Technol. 132 35–41

5. Vairavan H and Abdullah A B 2017 Die-punch alignment and its effect on the thinning
pattern in the square-shaped deep drawing of aluminium alloy Int. J. Mater. Prod.
Technol. 54 147–64

6. Slavič J, Bolka S, Bratuš V and Boltežar M 2014 A novel laboratory blanking apparatus
for the experimental identification of blanking parameters J. Mater. Process. Tech. 214
507–13

7. Tamaoki K, Manabe K, Kataoka S and Aizawa T 2013 Continuous Dry Cylindrical and
Rectangular Deep Drawing by Electroconductive Ceramic Dies J. Manuf. Sci. Eng. 135
031010

8. Younis K M and Jabber A S 2018 Experimental Evaluation and Finite Element
Simulation to Produce Square Cup by Deep Drawing Process Al-Khwarizmi Eng. J. 14
39–51

9. Behrens G, Ruhe M, Tetzel H and Vollertsen F 2015 Effect of tool geometry variations
on the punch force in micro deep drawing of rectangular components Prod. Eng. 9 195–
201

10. Panicker S S and Kumar Panda S 2017 Improvement in Material Flow During
Nonisothermal Warm Deep Drawing of Nonheat Treatable Aluminum Alloy Sheets J.
Manuf. Sci. Eng. 139 031013

11. Ogawa T, Ma N, Ueyama M and Harada Y 2016 Analysis of Square Cup Deep-
Drawing Test of Pure Titanium J. Phys. Conf. Ser. 734 032072

12. Yang C, Li P and Fan L 2014 Blank shape design for sheet metal forming based on
geometrical resemblance Procedia Eng. 81 1487–92

13. Reddy C 2015 Formability of Warm Deep Drawing Process for AA1050-H18
Rectangular Cups Int. J. Mech. Prod. 5 85–98

14. Zein H, Sherbiny M El, Abd-Rabou M and El M 2014 Thinning and spring back
prediction of sheet metal in the deep drawing process J. Mater. Des. 53 797–808

15. García C 2005 Artificial intelligence applied to automatic supervision, diagnosis and
control in sheet metal stamping processes J. Mater. Process. Technol. 164–165 1351–7

16. Zoesch A, Wiener T and Kuhl M 2015 Zero Defect Manufacturing: Detection of Cracks

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and Thinning of Material during Deep Drawing Processes Procedia CIRP 33 179–84
17. Kibe Y, Okada Y and Mitsui K 2007 Machining accuracy for shearing process of thin-

sheet metals—Development of initial tool position adjustment system Int. J. Mach.
Tools Manuf. 47 1728–37
18. Berger M and Zussman E 2002 On-Line Thinning Measurement in the Deep Drawing
Process J. Manuf. Sci. Eng. 124 420

[Publication from IOP Conference Series: Materials Science and Engineering]
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2018 Azman A.

Practical Skills Assessment in Machining Course
Based on Psychomotor Domain

Azri Bin Azman
Department of Stamping Die
Kolej Kemahiran Tinggi MARA Balik Pulau
[Email : [email protected]]

Abstract – The acquisition of psychomotor skills is a key competency in
engineering field. In manufacturing courses, engineering workshop practices
and laboratory work provide students with practical skills (hands-on
activities) thus enhance knowledge prior to their future career. This paper
discuss the current levels of practical skills acquired by students’ in
engineering workshop machining classes with reference to Engineering
Accreditation Council (EAC), Malaysian Qualification Agency (MQA) and
(Ministry of Education) MOE standards. The findings will be applied to
teaching and learning method related accordingly to the specific taxonomy
for further enhancement in machining courses.

[Keywords: Low noise amplifier, engineering workshop practices, practical skills,
assessment, psychomotor domain]

1. INTRODUCTION

Generally, the learners’ concern is to acquire skills from their studies,
which they can subsequently apply in their workplaces and businesses.
Therefore, an institution called KKTM Balik Pulau in Penang, Malaysia was
established to offer Diploma programmes that are relevant to market needs

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which lead to creating workers of different categories to enhance movement
towards professional growth1. It was observed that nations that do not inherit
skilled human resources and technological infrastructures are unable to
develop knowledge industries and cannot participate in the global knowledge
economy2.

The course materials are the vital tools used in instructional delivery
based on the principles of learning theories with the aim to create desirable
conditions that will facilitate effective self-learning2. This means that students
are notified of the skills they are expected to have by the end of a unit. The
course content is created in a way that will ensure learners to acquire the skills
and competencies necessary to compete in the 21st century knowledge
economy. These will then prepare such learners to respond to increasing
demands of the working life.

This study was conducted to ascertain the extent to which KKTM
students have master the practical activities embedded in their course
modules. Designing the practical skills or the psychomotor skills learning
outcomes are very important because the learners competencies would be
assessed and are reflected as their workplace performances. Most researches
are focusing primarily on academic performance, which is generally related to
cognitive domain3 - 5. Very few researches have been devoted to exploring
student learning outcome at the psychomotor domain in machining courses.
This paper discusses on psychomotor skills/activities that are implemented in
the workshop courses offered. The aim of this paper is in addressing the gap
identified in the machining workshop courses.

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2. BACKGROUND OF STUDY
The program learning outcomes for KKTM Balik Pulau, is as shown

in Table 1.

Table 1: Program Learning Outcomes (PLO) for Engineering Technology
Courses in KKTM Balik Pulau

PLO Diploma in Engineering Technology Domain
1 Apply knowledge of mathematics, science, engineering Cognitive
fundamentals and engineering specialization principles to
2 well-defined practical procedures and practices Cognitive
Analyse well-defined engineering problems and formulae
3 solutions to well-defined technical problems in the specified Psychomotor
discipline
4 Conduct investigations of well-defined problems and assist Psychomotor
in the formulation of systems, components or processes to
5 meet specified needs Affective
6 Apply appropriate techniques, resources and engineering Affective
7 tools to well-defined engineering activities, with an Affective
8 awareness of the limitations Affective
9 Demonstrate an awareness of and consideration for societal Affective
10 health, safety, legal and cultural issues and their consequent Affective
11 responsibilities Affective
Communicate effectively with the engineering community
and society at large
Demonstrate leadership qualities and to work effectively in a
diverse technical team
Demonstrate an understanding of professional ethics,
responsibilities and norms of engineering technology
practices
Acquire management or entrepreneurial knowledge and
skills
Demonstrate an understanding of the impact of engineering
practices, taking into account the need for sustainable
development
Recognize the need for professional development and to
engage independent and lifelong learning

PLO 3 and PLO 4 are the related statements to psychomotor domain.
The course learning outcomes (CLO) for machining courses are described as
per Table 2.

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Table 2: Course Learning Outcomes (CLO) for Engineering Technology
Machining Courses in KKTM Balik Pulau

Conventional Conventional CNC Turning CNC Milling
C Turning Milling

L Yr 1 / Semester 1 Yr 1 / Yr 1 / Yr 2 /
O Semester 2 Semester 2 Semester 3

Description

Identify and differentiate various types of

1 Select appropriate cutting tools accessories, CNC Turning machines and

their applications

2 Apply appropriate cutting conditions Select appropriate cutting tools, cutting
based on selected materials conditions to machine selected materials

3 Perform turning operations with proper Perform machining Carry out
safety procedures simulation prior to machining
actual machining simulation prior
tasks to actual
machining tasks

4 Perform inspection according to the Perform CNC Turning / milling
specification operations with proper safety procedures

Perform quality

5- - Perform inspection control and
on the component dimensional
according to the inspection on the
specification component
according to the

specification

Student enrolled in these courses, are expected to acquire these outcomes by
the end of their third year of their studies.

3. OBJECTIVES OF STUDY

The objectives of this paper are to review and identify the gaps of the
current practical skills that related to psychomotor domain to the national
quality standards. Secondly is in aligning the PLOs and CLOs accordingly
and eventually the alignment of the Teaching and Learning (T & L) method.
Lastly, the effective assessment methods are also discussed. These findings
will be used to identify the psychomotor domain level by using Ferris and

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Aziz psychomotor domain model.

A. Governing Council
Malaysian engineering education mainly guided by accrediting body,

Engineering Accreditation Councils (EAC) of Malaysia and the Malaysian
Qualification Agency (MQA) Department of the Ministry of Education
Malaysia (MOE). EAC is the body appointed by Board of Engineers Malaysia
(BEM) for accreditation of engineering program in Malaysia. Accreditation
policy required engineering graduates to have the necessary attributes, skills
and competencies reflected in the graduate outcomes specified in EAC
Manual. According to study done by Basri6 and Abdullah7, Malaysian
employers agreed that more than 70% of the attributes for engineers in EAC
manual are important. Table 3 shows the attributes required by EAC.

Table 3: Engineering Attributes Required by EAC (Malaysia)

Attributes

A Ability to acquire and apply knowledge of science and engineering fundamentals

B Ability to communicate effectively, not only with engineers but also with the
community at large

C In-depth technical competence in a specific engineering discipline

D Ability to undertake problem identification, formulation and solution

E Ability to utilise a systems approach to design and evaluate operational
performance

F Understanding of the principles of sustainable design and development

G Understanding of professional and ethical responsibilities and commitment to
them

H Ability to function effectively as an individual and in a group with the capacity to
be a leader or manager as well as an effective team member

I Understanding of the social, cultural, global and environmental responsibilities as
professional engineer and the need for sustainable development

J Expectation of the need to undertake the lifelong learning and
possessing/acquiring the capacity to do so

EAC attributes clearly indicate the integration of laboratory work in
the curriculum. This justifies the important of learning outcomes for a

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laboratory work. Since learning to operate machines involves several intricate
procedures, a detailed task analysis10 is required for complex learning8. The
provision of adequate scaffolding in the form of instance worked examples,
coaching, feedback, process worksheets, expert advice and referenced sources
is necessary to help direct the learner and support learning10.

Table 4: MQA and MOE Learning Outcomes (LO)

MQA LO Domains MOE LO Domains
Knowledge of the discipline-content (C)
1 Knowledge (C) Practical skills (P)
Thinking and scientific skills (C)
2 Practical skills (P)
Communication skills (A)
3 Social skills and responsibilities (A)
Social skills, teamwork and responsibility
4 Ethics, professionalism and humanities (A)
(A) Value, ethics, moral and professionalism
(A)
5 Communication, leadership and team Information management and lifelong
skills (A) learning skills (A)

6 Scientific methods, critical thinking and Managerial and entrepreneurial skills (A)
problem solving skills (C)
Leadership skills (A)
7 Lifelong learning and information
management skills (A)

8 Entrepreneurship and managerial skills
(A)

9

Table 4 shows the LO domains outlined by MQA and by MOE. The
domain of learning for each LO domain is indicated in brackets where (C) is
for the knowledge or cognitive domain, (P) is the skill or psychomotor domain
and (A) is for the affective domain. Learning outcomes can serve as a
benchmark to measure a success of an institution. Learning outcomes as
‘being something that student can do now that they could not do previously’
are changes in people as a result of a learning experience13. Learning
outcomes can be used in a way that meets the needs of all stakeholders in
learning institutions (i.e. the student, the lecturer and external parties).

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B. Psychomotor Domain
The psychomotor domain is a skill based domain. Students’ practical

skills in the engineering workshop / laboratory are associated with the
psychomotor domain. This domain focuses on manual task that require the
manipulation of objects and physical activities12. According to10, 12, human
mind and body are link together while performing those activities. The
widely accepted classifications for the psychomotor domain are Simpson’s10
and Dave’s11 taxonomies. However, Ferris and Aziz have developed another
psychomotor domain according to engineering discipline.

Ferris and Aziz (2005)15 have introduced seven levels of psychomotor
domain hierarchy related to laboratory experiment in engineering education
(refer to Table 5). According to14, this psychomotor domain model is specific
for engineering students and could be used to assess the physical actions of
engineers16.

Table 5: Ferris and Aziz Psychomotor Domain Level

Level Description

1 Recognition of tools and Ability to recognize the tools of the trade and the
materials materials.

Ability to handle (pick, move and set down) the tools and

2 Handling of tools and materials and to handle objects without damage to either
materials the object or other objects in its environment or hazard to

any person.

Ability to perform the elementary, specific detail tasks

3 Basic operation of tools such as to hold the tool appropriately for use, to set the

tool in action.

4 Competent operation of Ability to fluently use the tools for performing a range of
tools tasks of the kind for which the tools were designed.

5 Expert operation of tools Ability to use tools rapidly, efficiently, effectively and
safety to perform work tasks on regular basis.

Ability to take a specification of a work output required

6 Planning of work and performs the necessary transformation of description
operations of the finished outcome into a sequence of tasks that need

to be performed.

7 Evaluations of outputs Able to look at a finished output product and review that
and planning means for product for quality. Ability to identify particular

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improvement deficiencies and the actions which could be taken to
correct the faults or to prevent the faults through
appropriate planning.

4. METHODOLOGY

This section explains the methodology used in collecting and
analyzing data. Document analyses are used in identifying the gap of
competencies. In this study, machining courses syllabus documents were
critically analyzed and mapped with MQA and MOE standard document in
identifying the current level of psychomotor skills. A document gives
information about the investigated phenomenon and exists independently.
According to16 asserted that “For case studies, the most important use of
documents is to corroborate and augment evidence from other sources”.

Steps in Document analysis starts with:

1. Collect and compile detailed syllabus of machining courses.
2. Analyze and map detailed syllabus between statutory standard from

MQA, EAC and MOE.
3. Confirm and identify the current level of psychomotor skills.
4. Ensure the psychomotor skills conform to statutory standard.
5. Summarize and conclude the result of document analysis.

5. PRELIMINARY FINDINGS

The section will discuss the findings through the mapping between
PLOs and CLOs with psychomotor domain level. The practical skills studied
in this paper relate to the recognition of components, handling of tools and
materials, basic operation of tools and competent operation of tools in the
psychomotor domain level proposed by19. The mapping of learning outcome

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mapped of machining courses during semester 1, semester 2 and semester 3.
Table 6 and Table 7 show the mapping result.

Table 6: Conventional Turning / Milling (Year 1 / Semester 1 / 2) Mapped
with Psychomotor Domain Level

CLO1 CLO Description Ferris & Aziz
CLO2 Domain
Select appropriate cutting tools Level 1
CLO3 Apply appropriate cutting conditions based on Level 2
CLO4 selected materials
Perform turning / milling operations with proper Level 2
safety procedures
Perform inspection according to the specification Level 3

Table 7: CNC Turning and CNC Milling (Year 1 & 2 / Semester 2 & 3)
Mapped with Psychomotor Domain Level

CLO1 CLO Description Ferris & Aziz
Domain
CLO2 Identify and differentiate various types of Level 1
CLO3 accessories, CNC Turning / Milling machines and
CLO4 their applications Level 2
CLO5 Select appropriate cutting tools, cutting conditions to Level 3
machine selected materials Level 3
Perform machining simulation prior to actual Level 4
machining tasks
Perform CNC Turning /Milling operations with
proper safety procedures
Perform inspection on the component according to
the specification

Document has been mapped from21 - 24 with the document of Ministry
of Education (MOE). The preliminary findings showed that the highest level
of psychomotor for manual machining is at level 3. CNC machining activities
reached level 4. This is due to the difficulties and complexity of the activities
involved. The machining courses spend about 80 hours per semester on hands-
on activities in the workshop. The assessment instruments also indicated about

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90% were assessed based on project, formative practical test and summative
practical final. According to19, psychomotor domain levels proposed by17 are
suitable and appropriate to be used in this study for engineering technology
diploma students’ courses. This is stated by17 where the psychomotor domain
proposed by15 is specific for engineering students.

Table 6 mapping result shows the psychomotor domain level (PDL)
for conventional turning and milling courses equivalent to level 3. Level 3 is
referred to the basic operation of tools mainly, the student ability to perform
the elementary, specific detail tasks. These results shows the practical skills
implemented in the machining courses are compliance to its learning
outcomes. Refer to Table 7; the result is different since it has five CLOs’. The
extra CLO added in in performing machining simulation prior to actual
machining tasks. With the addition, the PDL increased to Level 4 with the
increase level of difficulty and complexity of the activities involved.

Table 8: Time in Hours of Machining Courses at KKTM Balik Pulau

Year / Course Lecture / Practical / Hours /
Semester Semester (hrs) Semester (hrs) Week
Conv. Turning
Yr 1 / 1 Conv. Milling 10 80 5
Yr 1 / 2 CNC Turning 11 79
Yr 1 / 2 CNC Milling 18 54 4
Yr 2 / 1 18 54

Table 8 shown the hours spend for practical skills in machining
courses for students’ in gaining knowledge and experiences in machining
operation. Conventional machining (manual hands-on) hours spend is more
than hours spend for operating computer numerical control (CNC) machining.
These indicated the important of acquisition of fundamental machining skills.

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Table 9: Assessment Instrument Format of Machining Courses

NO SUBJECT COURSEWORK FINAL
Final Practical
1 Type of Instruments Theory Test Practical Project
(40%)
2 Type of Items (10%) (50%) Practical
Duration of
Assessment Structure Project 5hrs

3 Total Mark 1hrs 55hrs 100%

100% 100%

According to Table 9 (example of assessment method), it shows that
the percentage of practical is 90% and the theory about 10%. From this
assessment method, enough exposure was given to the students’ on hands-on
activities which related to the psychomotor skills. Therefore, the result shows
that the assessment method and instrument used are aligned to the PLO’s and
CLO’s.

6. DISCUSSION AND FUTURE DEVELOPMENT

This section discusses the preliminary findings on the practical skills.
Document analyses identify the relationship between PLOs and CLOs’
according to the psychomotor domain. The use of17 psychomotor domain level
is suitable for mapping and alignment between CLOs’ and teaching and
learning method and assessment method. The findings from the preliminary
research have identified that the level of psychomotor domain19 in the
machining courses at KKTM Balik Pulau, Penang, Malaysia was as the levels
of the national standards. The items for each level can be identified from
PLOs and CLOs of the each machining courses. These findings’ have satisfied
and aligned with the national standard such as EAC, MQA and MOE. The
[15] psychomotor domain model can be used by lecturers as a guideline in

116

identifying the levels of students’ skills in performing the engineering

workshop / workshop practices / laboratory tasks.

With these findings, the next phases of the research are following the

triangulation method according to18 which is the observation and interview

method. The findings obtain will then use for future development related to

psychomotor skills in machining courses.

REFERENCES

1. Olakulehin, F.K , Effectiveness of proficiency skills development using open and
distance learning system in Nigeria, 2009.

2. Manjulika, S, Reddy V. V, Towards virtualization: Open and Distance Learning, Jan
2002.

3. Rahman, M.H., Developing course materials for open and distance learning: BOU
perspective, Turkish Online Journal of Distance Education, 7 (4), 55-60, 2006.

4. Daymount, T. & Blau, G., Student performance in online and traditional sections of an
undergraduate management course. Institute of Behavioral and Applied Management,
275–294, 2008.

5. Oladejo, M.A., Ige, N. A., Fagunwa, A. O., and Arewa. O.O., Socio-demographic
Variables and Distance Learners’ Academic Performance at the University of Ibadan,
Nigeria. European Journal of Scientific Research, 46 (4), 540-553., 2010.

6. Basri H., Omar M.Z.., Zainal M., Abang Abdullah A.A., Badrulhisham A.A., Abdul
Hamid H., Nik Abdullah N.M., Azmi H. & Zaidi M.R., “The Future of Engineering
Education in Malaysia”, 2007.

7. Abdullah S., Zaharim A., , Harris S.M., , Omar M.Z., Basri H., Nik Mohamed N.A.,
Engineering Education: Using Technical Attributes to Analyse the Employers'
Expectation of Future Engineering Graduates in Malaysia., 2007.

8. Van Merrienboer, J. J. G., & Kirschner, P. A. , Ten steps to complex learning. Mahwah,
NJ: Lawrence Erlbaum Associates. 2007.

9. Clark, R. C., Multimedia learning in e-courses. In R. E. Mayer (ed), The Cambridge
handbook for multimedia learning (pp. 589 – 616), New York: Cambridge University
Press, 2005.

10. E. J. Simpson, The classification of educational objectives in psychomotor domain, vol.
3. Washington D. C: Gryphon House, 1972.

11. Dave, R. H., Developing and Writing Behavioral Objectives. (R. J. Armstrong, ed.).
Tucson, Arizona: Educational Innovators Press. 1975.

12. Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1973). Taxonomy of Educational
Objectives, the Classification of Educational Goals. Handbook II: Affective Domain.
New York: David McKay Co., Inc.

13. Watson, R., Stimpson, A., Topping, A., Porock, D. Clinical competence in nursing: a
systematic review of the literature. Journal of Advanced Nursing 39(5), 421e431, 2002.

14. D. Kennedy, A. Hyland and N. Ryan, "Writing and using learning outcomes: a practical
guide,” Bologna Handbook. European University Association, 2006.

15. T. L. Ferris and S. Aziz, “A psychomotor skills extension to Bloom's Taxonomy of
education objectives for engineering education,” Exploring Innovation in
Education and Research. Tainan, Taiwan, 2005.

16. M. Hoffmann, “Using Bloom's Taxonomy of learning to make engineering
course comparable,”. IEEE Transactions, pp. 205-209, 2008.

117

17. A. Chapman, “Benjamin Bloom's Taxonomy of learning domains - cognitive, affective,
psychomotor domains,” 2006.

18. Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks,
CA: Sage, 2003.

19. K. R. Salim, M. Puteh & S. M. Daud. (2009). A Review of the Current Laboratory
Practice in Malaysian University: A Preliminary Study. March 2009.

20. Felder, R. M. & Brent, R., Designing and Teaching Courses to Satisfy the ABET
Engineering Criteria. Journal of Engineering Education, 92 (1), 7 - 25. 2003.

21. Dasar Pembangunan Kurikulum Program Sijil dan Diploma, Caw. Pengajian Bahagian
Kemahiran dan Teknikal MARA, Edisi Januari 2012.

22. Engineering Accreditation Council (2012). Engineering Programme Accreditation
Manual, 2012.

23. Bahagian Kemahiran dan Teknikal (BKT) MARA, Detailed Assessment Book,
Diploma in Manufacturing Engineering Technology, July 2012.

24. Malaysia Qualification Agency. Malaysian Qualification Framework 2012.

[Publication from International Conference on Teaching and Learning in Computing and Engineering]
118

2018 N Mohamed et al.

The Influence of Environmental Actions and
Customer Activities in GSCM on Operational

Performance

Norhafiza Mohamed*, Prof. Madya Dr Wan Hasrulnizzam Wan Mahmood**,
Dr. Muhamad Zaki Yusup*

*Department of Mould
Kolej Kemahiran Tinggi MARA Balik Pulau,

**Faculty of Manufacturing Engineering
Universiti Teknikal Malaysia Melaka

[Email : [email protected]]

Abstract – This paper aims to explore the level of manufacturing
performance, environmental actions and customer activities in implementing
green supply chain initiatives. Besides, the relationship between
environmental actions and customer activities towards manufacturing
performance also been investigated. For this purposes, the data was collected
using questionnaire-based survey among Malaysian manufacturing firms.
Using the descriptive and correlation test, the data was analyzed. From the
results, it is showing that the manufacturing performance through the
implementation of green supply chain management has a positive
relationship to environmental action and customer activities.

[Keywords: manufacturing performance, green supply chain management, environment
actions, customer activities]

1. INTRODUCTION

Nowadays, manufacturers are actively focus in optimizing the use of
resources in order to achieve the aspects of environmental sustainability

119

practices, primarily in reducing the adverse impact to the environment.. In
line with the concept of sustainability in manufacturing, the use of green
technology has been widely accepted and implemented by most of
manufacturing firms through high focus in Green Supply Chain Management
(GSCM) 8. According to Lee et al.,(2012)., the assessment of GSCM has a
close linkage with Manufacturing Performance (MP). This is because the MP
has been accepted as an important management mechanism as an effective
criteria in achieving the objectives that sets by manufacturing firms7.
Through the focus on Environmental Actions (EA) and Customer Activities
(CA), the level of MP can be strengthening. As a results, manufacturing firms
able to improve their operational activities, primarily when involving with
the process in decision making, and in enhancing the accountability for
overall operational performance4. Realizing on the influence of EA and CA
on MP, this study is attempted to investigate how EA and CA had correlated
to MP. This was based on the study framework as shown in Figure 1.

Figure 1: Relationship between environmental action and customer activities
with manufacturing performance

120

2. RESEARCH METHOD

For the analysis purposes, the data was collected using a
questionnaire based survey. According to Rowley (2014), questionnaires are
one of the most widely used means of collecting data, and therefore many
novice researchers in business and management and other areas of the social
sciences associate research with questionnaires. The structure of the
questionnaire consists of seven statements in assessing the MP, 13 statements
that related with CA, and 21 statements related to EA based on cross
references from 1992 to 2010 review of GSCM. In order to ensure the data is
reliable for analysis, the respondent is requested to give the score for each
statement using four Likert’s scale (1= very poor, 2= poor, 3 = good and 4=
very good). The justification for using a four-point Likert scale was that if we
had used the more popular five-point scale or any other odd number of points
in the scale, there might have been a tendency of having most negative
responses loading heavily on the median level, the center point of the scale13.

3. RESULT AND DISCUSSION

From the descriptive analysis, the demographic data shows that over
60% of respondents are from top management (6.78% - Chief Executive
Officer / Director / Managing Director, 11.86% Manager, 47.46% - General
Manager / Assistant Manager at various levels). According to Lee et
al.,(2012), a person from the managerial level can provide a more reliable
information on the current performance of the manufacturing firms. This
including in setting the internal improvement program and activities by the
management, such as GSCM16. Moreover, the data is much valuable,
particularly in getting the information that relates to the implementation of
GSCM from the perspectives of the top managerial level. Other than that, the

121

data also shows that 41.3% of respondents are from electrical and electronic
engineering (EE) company, followed 9.5% from mechanical engineering
(ME) and manufacture of motor vehicle (MMV). The remaining respondents
are from the manufacturing firms that produces other transport equipment.

A. Level of Manufacturing Performance
The performance measurement is a part of fundamental strategies that

required by the management of manufacturing forms in making the strategic
decisions5, 18. Through the performance measurement, management able to
set the necessary action to increase the competition, and become more
competitive in implementing the GSCM1, 19. According to Jun (2009) and
Miles and Russell, (1997), the implementation of GSCM has been proved in
improving the operational performance through the coordination between all
the core business activities. Moreover, GSCM has been used as a benchmark
in increasing the competition in a global manufacturing environment. In this
study, seven MP criteria were assessed. It is consists of a lead time reduction
(MP1), reduction of through-put time (MP2), minimizing the work in
progress(MP3), reduction of manufacturing cost (MP4), improving the
product quality (MP5), improving the utilization of machines(MP6), and
improving the flexibility of operations(MP7).

Figure 2 shows the mean score of all seven criteria for MP that
assessed from the study population. These criteria’s are arranged and ranked
in accordance from the highest mean score value to the lowest mean score
value. From Figure 2, the highest MP criteria is MP5 (improving the product
quality) with the mean score value of 3.25 out of 4. This was followed by
MP1 (lead time reduction), MP2 (reduction of through-put time) and MP3
(minimizing the work in progress) have scored the same mean score value of

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3.10 out of 4. Meanwhile, MP4 (reduction of manufacturing cost), MP6
(improving the utilization of machines and MP7 (improving the flexibility of
operations) have been given the lowest rank with mean score value of 3.06,
3.02, and 2.98 out of 4, respectively. The results obtained is similar with the
study by (Lee, 2008), indicating that the MP has a positive influenced by the
GSCM practices.

Figure 2: Mean score for Manufacturing Performance
B. Level of Customer Activities

Based on Figure 3, ensuring the customer complaints are properly
addressed (CR6) is ranked with the highest mean score value of 3.46 out of 4.
This is similar with the study by Wei and Wang (2011) that showing the focus
in dealing with the customer needs and complaints, has become a vital
success factor in implementing the GSCM by manufacturing firms. This was
followed by CR1 (the identification of customer needs and focus) with the
mean score value of 3.52 out of 4. This is not surprising because this criteria
is important, mainly to support the goal of Quality Management (QM) and
Supply Chain Management (SCM) in fulfilling the customer requirement that
related to quality, cost, time of delivery, and flexibility in operations13.

123

Meanwhile, the activity in defining the production and operations procedures
to ensure greater efficiency (CR2) is ranked with the mean score of 3.44 out
of 4. This activity is closely related to how manufacturing firms respond to
environmental legislation. As suggested by Nawrocka et al., (2009)., some of
the activities may include the implementation of standardized environmental
management systems (in particular ISO 14001), life cycle assessments,
environmental labelling of products, carbon disclosure projects, and
sustainability reporting schemes. As for top management commitment
(CR13), the mean score result is 3.43 out of 4. This showing that in Malaysia,
the role of top management in manufacturing firms is important. It is
necessary because only the top management can decide the risk that should
be taken, and how to respond actively to any issues occurs in GSCM
practices2.

Figure 3: Mean score for Customer Activities

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C. Level of Environmental Action
Figure 4 shows the mean scores value of EA in population studied

that related to GSCM practices. According to Zhu et al. Zhu and Cote (2004),
the ability to control and use environmental friendly materials are identified
as the most important elements in GSCM practices, particularly by
manufacturing firms. According to Li et al., (2006) have stated that the
readiness to use the environmentally friendly materials is closely related with
the social responsibility and commitment by manufacturing firms to the
environmental preservation. This eventually provides reassurance to the
implementation of green production process, primarily in reducing the
negative effects on the environment23. Besides, Seuring et al., (2008) stated
that the focus in increasing the use of environmental material will reduce the
customer prejudice with the final product or service.

As in Figure 4, 14 out 21 statements are accepted as the most practical
elements in implementing GSCM with the mean score value above 3.00 out
of 4. The highest ranks of GSCM practices is EA 1 (use environmentally
friendly raw materials) with the mean score value of 3.47. This is not
surprising because the environmentally materials is proven to be useful in
environment conservation, mainly in producing an environmentally product
as demand by customers. According to Rao and Holt (2005), most of
manufacturing organizations in South East Asia has increase their focus in
greening practices that inbound the logistics function by using
environmentally-friendly raw materials, reforming the greening production
to cleaner production, as well as increasing the prevention of pollution
practices at its source’s. Meanwhile, the remaining 7 statements are ranked
by the respondents with the mean score value lower than 3 out of 4. However,
all these 7 statements are still perceived to be important in implementing the

125

GSCM practices.

Figure 4: Mean Score for Environmental Actions
D. Correlation Test

In investigating the influence of CA and EA towards MP, Spearman
correlation tests are conducted. The test results are recorded in Table 1. From
a total of 147 matrices of relationship between MP and EA, there are 7.5% of
matrices of relationship produce a moderate positive relationship with the
MP. . Meanwhile, four matrices of relationship found to ne not significant
with the MP, Namely (EA17 - MP1, EA14 - MP6, EA16- MP6, and EA5-
MP7. From the matrices of correlation, the highest significant of relationship
of MP with EA occurs with EA5 (taking environmental criteria into
consideration) and MP 2 (through-put time reduction) at correlation value of
0.533. This results is similar with the study by Yang (2012) that indicates that
sustainability has expanded the fundamentals of project development in the
built environment. As for CA, a total of 91 relationships of matrices produced

126

between MP and CA. From this matrices, a total of 14 (15.4%) produces a
moderate relationship; and the remaining had produced a low level of
relationship. From the matrices, the highest matrices of relationship occurs
between CR11 (employee training/employee involvement) with MP2
(through-put time reduction) at positive correlation value of 0.592. As
suggested by Ghosh et al., (2012), the training programmed is important in
setting a new set of Knowledge, Skills and Abilities (KSAs), behavior, or
attitudes. These indicating that the participants of employees are important in
increasing the level of MP.

Table 1: Correlation between MP with EA and CR

Item ENVIRONMENTAL ACTION CUSTOMER ACTIVITIES

Moderate Low Moderate Low

(0.400-0.599) (<0.390) (0.400-0.599) (<0.390)

MP 1 ea1,ea 5, ea ea2,ea3,ea4,ea6,ea8,ea9,ea10,ea12,ea cr 11,cr 12 cr1,cr2,cr3,cr4,cr5,cr6,cr
MP 2 11, ea 21 13,ea14,ea15,ea16,ea18,ea19,ea20 cr5,cr 8, cr 9,cr 7,cr8,cr9,cr10,cr13
MP 3 ea 2, ea 5,ea 7 ea1,ea3,ea4,ea6,ea8,ea9,ea10,ea11,ea1 11,cr 12 cr1,cr2,cr3,cr4,cr6,cr7,cr
MP 4 2,ea13,ea14,ea15,ea16,ea17,ea18,ea19 cr 11,cr 12 10,cr13
MP 5 ea 6 ,ea20,ea21
MP 6 ea1,ea2,ea3,ea4,ea5,ea7,ea8,ea9,ea10, cr 8 cr1,cr2,cr3,cr4,cr5,cr6,cr
MP 7 ea 2,ea 5,ea ea11,ea12,ea13,ea14,ea15,ea16,ea17,e 7,cr8,cr9,cr10,cr13
12 a18,ea19,ea20,ea21 cr 9, cr 12
- ea1,ea3,ea4,ea6,ea7,ea8,ea9,ea10,ea11 cr1,cr2,cr3,cr4,cr5,cr6,cr
,ea13,ea14,ea15,ea16,ea17,ea18,ea19, cr 5 7,cr9,cr10,cr11,cr12,cr1
- ea20,ea21 3
ea1,ea2,ea3,ea4,ea5,ea6,ea7,ea8,ea9,e cr 2 cr1,cr2,cr3,cr4,cr5,cr6,cr
- a10,ea11,ea12,ea13,ea14,ea15,ea16,ea 7,cr8,cr10,cr11,cr13
17,ea18,ea19,ea20, ea21
ea1,ea2,ea3,ea4,ea5,ea6,ea7,ea8,ea9,e cr1,cr2,cr3,cr4,cr6,cr7,cr
a10,ea11,ea12,ea13,ea15,ea17,ea18,ea 8,cr9,cr10,cr11,cr12,cr1
19,ea20,ea21 3
ea1,ea2,ea3,ea4,ea5,ea7,ea8,ea9,ea10, cr1,cr3,cr4,cr5,cr6,cr7,cr
ea11,ea12,ea13,ea14,ea15,ea16,ea17,e 8,cr9,cr10,cr11,cr12,cr1
a18,ea19,ea20,ea21 3

4. CONCLUSION
In conclusion, the implementation of GSCM by Malaysian

manufacturing firms is focusing on the activities that related in reducing the

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uses of natural sources. From the results, the level of MP is influenced by the

level of sustainability in manufacturing operations. Two variables, namely

EA and CA had contributes in improving the MP in establishing the GSCM.

These findings are useful in establishing the strategy and setting the actions

required in achieving high level of green practices in manufacturing firms.

REFERENCES

1. Beamon, B.M., 1999. Designing the green supply chain. , 12(4), pp.332–342.
2. Garrett, R.P., Covin, J.G. & Slevin, D.P., 2009. Market responsiveness, top

management risk taking, and the role of strategic learning as determinants of market
pioneering. Journal of Business Research, 62(8), pp.782–788.
3. Ghosh, P. et al., 2012. Towards more effective training programmes: a study of trainer
attributes. Industrial and Commercial Training, 44(4), pp.194–202.
4. Hervani, A. a., Helms, M.M. & Sarkis, J., 2005. Performance measurement for green
supply chain management. Benchmarking: An International Journal, 12(4), pp.330–
353.
5. Jabbour, A.B.L.S. & Jabbour, C.J.C., 2009. Are supplier selection criteria going green?
Case studies of companies in Brazil. Industrial Management & Data Systems, 109(4),
pp.477–495.
6. Jun, X., 2009. Model of Cluster Green Supply Chain Performance Evaluation Based on
Circular Economy. 2009 Second International Conference on Intelligent Computation
Technology and Automation, pp.941–944.
7. Lee, S.M., Tae Kim, S. and Choi, D., 2012. Green supply chain management and
organizational performance. Industrial Management & Data Systems, 112(8), pp.1148–
1180.
8. Lee, Su-yol, 2015. The effects of green supply chain management on the supplier ’ s
performance through social capital accumulation.
9. Lee, Su‐Yol, 2008. Drivers for the participation of small and medium‐sized suppliers in
green supply chain initiatives. Supply Chain Management: An International Journal,
13(3), pp.185–198.
10. Li, S. et al., 2006. The impact of supply chain management practices on competitive
advantage and organizational performance. Omega, 34(2), pp.107–124.
11. Miles, M.P. and Russell, G.R., 1997. IS0 14000 Total Quality Environmental
Management : The Integration of Environmental Marketing , Total Quality
Management , and Corporate Environmental Policy. , pp.151–168.
12. Nawrocka, D., Brorson, T. & Lindhqvist, T., 2009. ISO 14001 in environmental supply
chain practices. Journal of Cleaner Production, 17(16), pp.1435–1443.
13. Rao, P. and Holt, D., 2005. Do green supply chains lead to competitiveness and
economic performance? International Journal of Operations & Production
Management, 25(9), pp.898–916.
14. Rashid, K. and Haris Aslam, M.M., 2012. Business excellence through total supply
chain quality management. Asian Journal on Quality, 13(3), pp.309–324.

128

15. Rowley, J., 2014. Designing and using research questionnaires. Management Research
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16. Sarkis, J., 2012. A boundaries and flows perspective of green supply chain
management. Supply Chain Management: An International Journal, 17(2), pp.202–216.

17. Seuring, S. et al., 2008. Sustainability and supply chain management – An introduction
to the special issue. Journal of Cleaner Production, 16(15), pp.1545–1551.

18. Shaw, S., Grant, D.B. and Mangan, J., 2009. Developing Environmental Supply Chain
Performance Measures. Benchmarking: An International Journal, (June).

19. Vachon, S. and Klassen, R.D. (2008), 2008. Environmental management and
manufacturing performance: the role of collaboration in the supply chain. International
Journal of Production Economics, 111(2), pp.299–315.

20. Wei, Y. (Susan) and Wang, Q., 2011. Making sense of a market information system for
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21. Yang, J., 2012. Editorial: promoting integrated development for smart and sustainable
built environment. Smart and Sustainable Built Environment, 1(1), pp.4–13.

22. Zhu, Q. and Cote, R.P., 2004. Integrating green supply chain management into an
embryonic eco-industrial development: a case study of the Guitang Group. Journal of
Cleaner Production, 12(8-10), pp.1025–1035.

23. Zhu, Q. and Sarkis, J., 2006. An inter-sectoral comparison of green supply chain
management in China: Drivers and practices. Journal of Cleaner Production, 14(5),
pp.472–486.

APPENDIX

How would you consider the performance of the manufacturing system, with respect to the
following?

no Items
MP 1 Lead time reduction
MP 2 Through-put time reduction
MP 3 Work-in-progress reduction
MP 4 Manufacturing cost reduction
MP 5 Product quality improvement
MP 6 Machine utilization improvement
MP 7 Flexibility improvement

129

In the last two years, company has taken in the following Customer Related Activities.

no Items
CR 1 Identification of customer needs/customer focus
CR 2 (Re) Defining production / operations procedures to ensure greater efficiency
CR 3 Ensuring that staff are issued correct versions of documentation needed to perform task
CR 4 Ensure identity of preferred suppliers and a system for advising them of what is expected
to be supplied
CR 5 Ensure training needs are identified and records of who has been trained in which topics
CR 6 Ensure customer complaints are properly addressed
CR 7 Ensure minimization and commitment to remove non-conformities
CR 8 Use of statistical process control (SPC)
CR 9 Identification of courses for non-conformity
CR 10 Ensure workers commitment
CR 11 Employee training / employee involvement
CR 12 Benchmarking
CR 13 Top management commitment

In the last two years, the company has taken environmental actions in the following areas.

no Items
EA 1
EA 2 Environment-friendly raw materials
EA 3 Substitution of environmental questionable materials
EA 4 Choice of suppliers by environmental criteria
EA 5 Urging/pressuring supplier(s) to take environmental actions
EA 6 Taking environmental criteria into consideration
EA 7 Design considerations
EA 8 Optimization of processes to reduce solid wastes
EA 9 Optimization of processes to reduce water use
EA 10 Optimization of processes to reduce air emissions
EA 11 Optimization of processes to reduce noise
EA 12 Use of cleaner technology processes to make savings (energy, water, wastes)
EA 13 Recycling of materials internal to the company
EA 14 Use of waste of other companies
EA 15 Use of alternative sources of energy
EA 16 Helping suppliers to establish their own EMS
EA 17 Recovery of the company’s end-of-life products
EA 18 Eco-labeling
EA 19 Environmental improvement of packaging
EA 20 Taking back packaging
Providing consumers with information on environmental friendly products and/or
EA 21 production methods
Change for more environmental-friendly transportation

[Publication from Journal of Advanced Research in Applied Sciences and Engineering Technology]
130

2018 Yusup M.Z.

Implementation of Lean and Cleaner Production by
Malaysian Manufacturers – Preliminary Survey

Dr. Muhamad Zaki Yusup

Department of Mould
Kolej Kemahiran Tinggi MARA Balik Pulau

[Email : [email protected]]

Abstract – Adoption of lean production in managing the production
operations with a cleaner production due to of environmental concerns
positively drive new manufacturing paradigm. This leads to this study to
investigate the extent of which these practices has been adopted by Malaysian
manufacturers. The finding revealed that good performance in environmental
practice in tandem with the appraisal of labour safety in material handling has
helped the respondents to successfully sustain the quality and durability of
products, improves the operation efficiency, as well as production’s
productivity. The correlation test results between all items in Lean Production
and Cleaner Production have a significant positive relationship with each
other where the appraisal in the selection of equipment from Cleaner
Production practices have a very strong relationship with the improvement of
working conditions that resulted from the adoption of Lean Production
practices. These potentially make both practices as the best approach that has
potential to be integrated together to support the development of a sustainable
manufacturing practice principally in Malaysia’s manufacturing industry.

[Keywords: Lean Production, Cleaner Production, Manufacturing Industry, Malaysia]

131

1. INTRODUCTION

The ability of adapting the Lean Production (LP) and Cleaner
Production (CP) in managing production operations was a substantial
proactive action in improving the performance of manufacturing
sustainability. As a multi-dimensional approach that aim to eliminate waste
in the operations, a high focus in LP evidently improves the productivity and
cost reduction strategies1, 2. Meanwhile, as a radical improvement strategy in
managing the environmental concern, CP potentially able to be integrated
with LP practice in achieving high economic performance, as well as increase
the management of environmental care, starting from the earliest stage in
product development, until how the waste are managed at the last stage of the
manufacturing process3. The ability to adapt these practices can boost the
capability of the manufacturer as to continually improve its competitiveness
in the global market4.

The implementation of LP and CP is seemed timely in enhancing the
degree of management efficiency, while reducing the barriers in continuous
improvement activity, especially in dealing with the increase of cost of
production that is caused by the increase of price in the production input5, 6.
The ability to adopt both practices may lead to optimal economic development
as well as reduce the environmental impact through efficient resource
utilisation.

Therefore, this preliminary study is used to measure the current
adoption of LP and CP in Malaysia’s manufacturing industry. This can
potentially be used in the transformation of the economic and environmental
performance in Malaysia’s manufacturing industry, in achieving sustainable
development, which is the basis to realize a new economic circular. The

132

following sub-titles explain the research methods, findings, and conclusion
from this preliminary study. The findings in this study may be referred by
academician and manufacturers, in improving the performance and
sustainability in the manufacturing operations.

2. RESEARCH METHOD

A total of 340 questionnaires were mailed out to various
manufacturing industries in Malaysia. The questionnaire, comprising of 25
items, were created to measure the current performance of LP practice and 26
items related to CP practice. The development of this questionnaire was
based on the review on several research papers in LP and CP. The
questionnaires were mailed out to the company’s senior management staffs,
who is have working experience of more than 2 years at same manufacturing
industry. Each respondents were asked to rate the current performance of
their LP and CP practice based on seven Likert’s scales (e.g.: 1 = strongly
disagree until 7 = strongly agree). Initially, a total of 40 questionnaires or
11.7 percent were returned. However, only 38 were considered valid to be
used to evaluate the performance from the adoption of LP practice, while
only 37 were valid to be used in evaluating the current performance of CP
practice.

3. RESULT AND DISCUSSION

The highest percentage of the respondents in this study are from the
mechanical product group (47.5%), followed by electrical or electronic
products group (20%), automotive product group and chemical or scientific
product groups (15%), and lastly, the other product group (2.5%). The

133

respondents are mainly from companies with less than 150 employees
(34.2%), less than 300 employees (23.7%) and more than 750 employees
(18.4%). Meanwhile, 47.5% of the respondents experienced with the
ISO14001 management system in which 63.1 % have more than 10 years of
experience. Furthermore, 80% of the respondents possess certified
management certification in other disciplines where 90.6% of them possess a
certification in ISO 9001.

A. Lean and Cleaner Production Performance
The internal consistency test shows that all of the items in the

questionnaire used were reliable for analysis where the measurement of
Cronbach’s alpha coefficient for the LP and CP practices were 0.988 and
0.975 respectively. This means that every question modelled to measure the
current performance in production practices correlated with both practices at
a higher internal consistency level. The mean score of respondents’
performance for both practices are arranged in rank from high to low, as
shown in Table 1.

For LP practice, Table 1 shows that the respondent agreed that all 25
items meet their current practice in adopting LP, where the intention to
increase the quality of products (LPP16) has the highest mean score of 6.45,
followed by the concentration to increase the operation efficiency (LPP20)
and production productivity (LPP21) at a mean score 6.18 respectively.
However, the focus to decrease the customer lead time (LPP1) has the lowest
mean score of 5.71.

As for the standard deviation, the analysis of this practice shows that
the action taken to decrease customer lead time (LPP1) has the highest value
of 1.227, and the action taken to increase the quality of products (LPP16) has

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a smaller variance at the lowest value of 0.891. Meanwhile, the respondents
also agreed that their current practices fulfil all 26 items in the adoption of CP
practice. The commitment to environmental protection rules, regulation and
practices (CPP24) has the highest score value with a mean score of 6.14,
followed by the focus to increase product durability (CPP11) and appraise
labour safety in material handling, with a mean score of 6.05 respectively.
The application of energy consumption technologies and equipment (CPP1)
was the lowest rated item with a mean score 5.05. As for the standard
deviation, there was a large variance of answers from the respondents that
agreed to consider recycled, re-manufactured or re-used product design
(CPP14) at a score value 1.547. However, the respondents have similar
opinion in considering increase of product durability (CPP11) where this
element has the lowest value at 0.911.

The result on LP shows that an improvement on the quality of
products is the main priority of the respondents. It is not surprising as this is
in line with the awareness of customers on the product purchased, as well as
high competitiveness in the market7. Although the reduction of customer lead
times is in the last position, it still needs to be considered. This possibly
depends on the nature of the business, and group of products produced.

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Table 1: Performance of Lean (LPP) and Cleaner Production Practices (CPP)

Item LLP Mean Item CPP Mean
LPP 16 6.45 CPP 24 Environmental protection regulation 6.14
LPP 20 Increase quality of products 6.16 CPP5 and policy 6.05
LPP 21 6.16 CPP 11 Appraise labour safety in materials 6.05
LPP 6 Increase the operation 6.08 CPP 3 handling 5.97
LPP 15 efficiency 6.05 CPP 9 5.97
LPP 22 Increase the production 6.05 CPP 12 Increase product durability 5.89
LPP 25 productivity 6.05 CPP 7 5.86
LPP5 Reduction in the throughput 6.03 CPP 13 Appraise the selection of suppliers 5.84
LPP 3 time 6.00 CPP 19 5.81
LPP 7 Environmental practice and 6.00 CPP 8 Recyclability and reusability in 5.76
LPP 14 performance 6.00 CPP 16 product design 5.76
LPP 17 Improve the organization of 6.00 CPP 4 Reduce usage of raw material and 5.73
LPP 19 work environment 6.00 CPP 6 resources 5.70
LPP 10 Improve the operation 5.97 CPP 20 Effect of production planning on 5.70
LPP 18 procedure 5.97 CPP 25 environmental 5.70
LPP 9 Defect detection ability of the 5.95 CPP 2 Encourage waste minimisation and 5.65
LPP 4 product 5.92 CPP 10 management 5.62
LPP 2 Knowledge of production 5.87 CPP 26 Environmental issues in 5.62
LPP 23 management 5.84 CPP 17 manufacturing systems 5.59
LPP 8 Maximise the operational 5.82 CPP 21 5.57
LPP 11 flexibility 5.82 CPP 15 Selection of equipment 5.54
LPP 12 Better environmental 5.76 CPP 18 5.54
LPP 13 management and control 5.76 CPP 23 Promotes employee involvement 5.41
LPP 24 Improve working conditions 5.76 CPP 14 5.32
LPP 1 manufacturing capability and 5.71 CPP 22 Improve layout and work design 5.30
flexibility Increase design of logistics 5.05
CPP 1 networks
Reorganise of working space Possibilities of recyclability from
activities
Reduce the non-added value
activities Reduce usage of natural resources

Optimise usage of equipment Proactive in process and technology
innovation
Improve the production takt Increase renewable resource
time utilisation
Improve layout to reduce Simplified the product installation
unnecessary movement process
Reduce the production lead Usage of non-toxic and non-polluting
time materials
Increase recyclability in composition
Minimising handling of products
Well-organized use of chemical in
Reduce changeover and process
handling time Evaluate environmental effects of
Reduce inventories and products
storage Disposal methods during designing
products
Setup time reduction Recycled, re-manufactured or reused
in design
Improve the material flow Sharing information with
stakeholders
Decrease customer lead time Application energy consumption
technologies

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Meanwhile, the result of the CP showed that the respondents have the
commitment to comply with environmental regulations, and the laws and
policy of environmental management. This indirectly promotes the proactive
action in dealing with issues related to the environment. Even though a
number of the respondents that agree that the use of energy conservation
equipment is low, it is still considered necessary by several respondents. This
requirement has contrasting values possibly due to the use of equipments and
technology that might vary by the category of products. Besides that, the
implementation of new technology is not always profitable for certain
business nature8. This result also shows that the manufacturing sector in
Malaysia has a high tendency to adopt LP and CP practices. In fact, the
analysis result also shows that these practices have worked together in
increasing the quality and durability of the products, in a comprehensive
manufacturing environments.

B. Spearman Rho Correlation Test
Based on Table 2, 23 items in the LP practice and 16 items in the CP

practice are seen to have a very strong correlated value, at a significance level
of 0.01. As explained by Szmidt and Kacprzyk9, the value obtained from
Spearman correlation test that are near to 1 are considered to have a stronger
relationship. In this study, the significant relationship for LP is recorded at a
value of 0.800 to 0.970, whereas CP ranged from 0.800 to 0.881 at a 99
percent confidence level. The analysis shows that the focus to increase the
operation efficiency (LPP20) with the ability to increase the production
productivity (LPP21) has a high correlation, at a significant value of 0.970.
This shows that the ability to take action in improving manufacturing
efficiency positively influence the improvement of productivity in production
operations2.

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Table 2: Spearman correlation coefficient of Lean (LLP) and Cleaner
Production Practices (CPP)

Item LP Practice Item CP Practice
LPP1 CPP1 -
LPP2 - CPP2 -
LPP3 LPP3, LPP4, LPP5 CPP3 -
LPP4 LPP2, LPP12, LPP13 CPP4 CPP5, CPP11 , LPP17
LPP2 , LPP6 , LPP11 , LPP12, LPP13 ,
LPP5 LPP14 ,LPP23, LPP24 CPP5 CPP4, CPP11
LPP2, LPP6, LPP12 LPP13, LPP19, LPP24,
LPP6 LPP25 CPP6 - LPP15,
LPP4, LPP5, LPP10, LPP11, LPP12, LPP13 , CPP7
LPP7 LPP14, LPP19, LPP25 CPP8 CPP8, CPP19, LPP15
LPP8 CPP9 CPP7, CPP9, CPP19,
- LPP17
LPP9 CPP10 CPP8, CPP19, LPP17
LPP13
LPP10 -
LPP10, LPP11, LPP13
LPP11 CPP11 CPP4, CPP5
LPP 6, LPP, LPP11, LPP12, LPP13, LPP23,
LPP12 LPP24, LPP25 CPP12 -

LPP13 LPP4, LPP6, LPP9, LPP10, LPP12, LPP13, CPP13 -
LPP23, LPP24, LPP25
LPP14 LPP3, LPP4, LPP5, LPP6, LPP10, LPP1!, CPP14 -
LPP15 LPP13, LPP14, LPP23, LPP24 CPP15
LPP16 LPP3, LPP4, LPP5, LPP6, LPP8, LPP9, LPP10, CPP16 -
LPP17 LPP11, LPP12, LPP 19, LPP23, LPP24, LPP25 CPP17 CPP18
LPP18 LPP 4, LPP 6, LPP12, LPP15, LPP24, CPP20 CPP18 CPP17, CPP19
LPP19 LPP14, LPP17,CPP7, CPP8, CPP19 CPP9, CPP18, CPP 20, LPP15,
CPP19 LPP17
LPP20 - CPP19, CPP21, CPP22
LPP21 LPP 15, LPP22, CPP5, CPP8, CPP9 CPP20 CPP20, CPP22
LPP22 LPP20, LPP21 CPP21 CPP20, CPP21
LPP23 CPP22 CPP25
LPP5, LPP6, LPP13, LPP24
LPP24 CPP23 -
LPP18, LPP21 CPP24
LPP25 LPP18, LPP20 CPP24 -
LPP17
LPP4, LPP10, LPP11, LPP12, LPP13, LPP24, CPP25
LPP25 CPP26
LPP4, LPP5, LPP10, LPP11, LPP12, LPP13 ,
LPP14, LPP19, LPP23
LPP5, LPP6, LPP10, LPP11, LPP13 , LPP23

As for CP, the highest significant value of 0.881 appears between the
actions in evaluation of environmental effects of products (CPP18) and the
practice in evaluating the environmental issues in the selection of
manufacturing systems (CPP19). This indicates that the selection of
manufacturing system should be considered parallel with the impact on the
environment that could potentially arise from the product produced. This fact
has proven that CP is able to be streamlined in all stages of the manufacturing

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process3. Besides, there are 3 items in LP and 6 items in CP that have a very
strong relationship with each other, with a correlation value of 0.807 to 0.835,
whereas the appraisal in selection of equipment’s in producing the products
(CPP8) with the improvement of working conditions (LPP17) has a very
strong correlation at valued at 0.847.

In addition, almost 40 percent of matrixes between the LP and CP
have a strong correlation result which values from 0.600 to 0.798. The result
was not surprising as the computed result for every item in the LP and CP has
strong relationships with each other, at a significant correlation value of 0.87.
This result is in line with the findings by 10 that the execution of LP practices
is usually closely related with the implementation of environmental
management practice. Several of the items listed do not have a strong
correlation with one another, but the respondents still agree that the measured
performance is still a vital key that influence the performance in LP and CP
Practices. This is because, the correlation tests on all items that were used to
measure the performance showed that each item in the LP and CP produces a
significant positive relationship with each other.

Even though there are some differences in its implementation, the
respondents have a high tendency to fulfil the adaptation needs of these
practices. The ability to improve the quality of products and capabilities in
enhancing resource management has a high influence in managing the
production costs. This similarity shows the adoption of both practices
potentially have substantial influence on manufacturing operations.

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4. CONCLUSION

As a conclusion, the study found that the current manufacturing

practice by the respondents in Malaysia’s manufacturing industry has met the

items in the adoption of LP and CP practices. In addition, the correlation

results also show that all items used in evaluating the current practices related

in each LP and CP has a significant correlation with each other where

majority of the items have a strong relationship with the adoption of both

practices. Moreover, both practices are also seen to have a strong relationship,

if they are implemented simultaneously. The result of this preliminary study

has provided basic information for next research level, primarily in

formulating the best approach that integrates both practices in Malaysia’s

manufacturing industry

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