The Challenge in Malaysian Industries in Implementing Design of …ieomsociety.org/ieom_2016/pdfs/495.pdf · 2016-10-18 · The Challenge in Malaysian Industries in Implementing Design

Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, March 8-10, 2016

The Challenge in Malaysian Industries in Implementing Design of Experiment

1,a* HARIS JAMALUDDIN , 1,b JAHARAH A. GHANI, 1,c MOHD NIZAM AB RAHMAN,

1,d BABA MD DEROS ,

Department of Mechanical and Material Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia. 43600 Bangi, Selangor, MALAYSIA.

Tel no.: +603 8921 6505 Fax no.: +603 8925 9659

Corresponding author: a*[email protected], b* [email protected] , c [email protected], d [email protected].

Abstract— This paper presents phenomena and challenge faced by Malaysian Industry in implementing design of experiment (DoE). The pilot survey conducted has unveiled several issues of DoE encountered in industry and to highlight the importance research to unfold the needs for effective implementation of DoE in real working environment. The importance of DoE as a tool for innovation and sustainable process is in line with the government strategic planning targeting for efficient ways in improving competitiveness and penetrating global market. Being superior in product performance as well as having robust processing method ensured quality and cost is manageable to sustain customer confidence. It was found that 45% of companies were interested in applying the DoE, the other 55 % of the companies were unable to deploy DoE as critical tools in industry. It is important that the industry is to be assisted in understanding the important of DoE and to the areas where they are important to be used. A framework is recommended for a successful implementation of DoE programme.

Keywords: Design of Experiment, Manufacturing Industry and Distinctive Capability.

I. INTRODUCTION Over the past few years and for a decade research in design of experiments continue to increase in its remarkable contributions and application in academic, social and industry. Experimental designs are employed in broad application in many disciplines. It is a fundamental part of the knowledge discovery process in science and engineering which are affecting variety of fields. The advances in design of experiment approach are triggered by complexity of problems when factors increase in numbers, constraints in levels of factors setting, cost effective solution, multiple responses in consideration and effect of noise factors over the experimental process. The significant interest and applicability of the DoE lead to new findings, technology innovations and inspiration of new knowledge. Within its rigorous development, several uncertainties arise in few areas of the real-world where DoE is not acknowledged as a way to deploy resources for their significant outcomes. This has resulted in undermining the distinctive capability in many organizations to sustain when there is change in economics, market and global infrastructure that could affect the continual existence of business entity. Such organizations could not be independent from other dominative services, products and supplies. In facts the situation will become absolute critical when cost and profit determine the direction to pursue. This limitation challenges the process of reform when there is a need for technology change or when problems are induced to the organization. Weak and slow actions in responding to tactical and technical problems arising from variation in the processes, components and extraneous equipment have worsened the readiness to meet market demands. This paper will uncover the course of event and scenario to explain the exact situation and reasons for the adverse results in DoE implementation. The misconception of the DoE was unintentionally focused only on its tangible or quantitative outcomes. Many have forgotten on its contribution in qualitative values that affects motivation, education and skill development process. DoE shall also be viewed as a tool to foster dynamic thinking within which it has been adopted. DoE could be used to alleviate employee competency and unearthing new process knowledge. The implementation of DoE inspiring a long term objective when its profound impact will support the operation of core business activity of an organization, despite the fact that short term goals are significantly benefited from its immediate effect. DoE is a cost effective statistical technique employed to reduce number of experiment used to find factors that affect a response in a process [1]. However, prior to the successful application of DoE, three major barriers shall be overcome; they are business barriers, educational barriers and technical

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barriers [2]. The implementation of DoE could be enhanced by adopting other optimization schemes [3] and quality techniques. In a winning DoE, the content of knowledge is explored and unveiled for explicit information which can be used as prime information in research, problems solving, controlling and inventing new ideas. Although it was reported that unsuccessful experiment are rarely published for reference and adversely failure are stigmatized in academic and industry issues [4]. Historically, unsuccessful experimental design is not merely a true setback to the ultimate goals. In fact, skill and knowledge are improved when source of failure is understood. For instance, many successful stories have started with failures and flaws in the beginning of the journey. Most importantly, the needs in understanding DoE should become apparent when solution to the real-world problem is unprecedented upfront and when problem occurred continue to exist with losses is unstoppable. The application of DoE could be the turning points to reform the strategic process in realistic development programmes. In Malaysia context, the competitive advantage and distinctive capability is a central issue in developing sustainable operational excellence. Despite the fact that in the past many researches had been conducted to focus on business techniques and quality management system such as TQM, Six sigma and ISO standards, and some on SPC and least or probably non in DoE. It is imperative to understand specifically how DoE could be applied to improve business processes and how much it could contribute to sustain business activities. The needs in employing DoE would have been so much crucial as discovering of new knowledge and utilization of cutting edge technology drive innovations to improve efficiency and productivity. Innovation shall be focused both in technological and non-technological aspect and this shall be started with a change in the mindset of people [5]. A change in mindset towards effective strategic management process required several enabling tools, for instance, DoE is suitably applied for developing; creative thinking, systematic investigation approach, analytical techniques and effective decision making capability. Definitions of Design of Experiment Literally defined, design of experiment is a test or series of test where input variables of a system or process are intentionally changed and varied at specific ranges or levels in order to determine and observe the changes or variation in the output response [6]. It is a systematic approach to engineering problem-solving which applies principles and techniques to ensure data collection will generate valid and supportable engineering conclusions [7]. In more specific application it is a planned experiment and a technique to identify the major influencing parameters and to control the quality of finish product [8]. In industry, design of experiment is a statistical technique which has been adopted to perform experimental studies focused in product and process improvement [4]. Design of experiments is the whole of general technique by which to heighten the efficiency of acquisition of technical information by experiment [9]. In general, most application of design of experiment concern process optimization [10]. There are two critical aspects which are very much interrelated for the success in design of experiment, the way the experiment is being conducted and statistical analysis used to tabulate and analyze the experimental data [6]. For example, when experiments and regression analysis are conducted, a model of the response to some independent factors can be developed to predict the desirable performance of the output variables [11]. Design of experiment is widely used in manufacturing in order to understand deeper the process knowledge, product and service behavior [12][13]. Undoubtedly, design of experiment able to evaluate complex system economically [14]. In order to gain accurate experimental results, measurement system is the central issue and a key aspect of design which should be addressed and defined clearly before design of experiment could be conducted [15]. The sound of engineering knowledge and common sense play an important role in design of experiments since many experiments involve technical matters and logical thinking. In providing higher level of quality in products and services, manufacturers need to compete on prices and performance to maintain profitability; this can be achieved through design optimization [16]. The production world and customer will be much better if every product is produced with sustainable, reliable and insensitive to process or induced variation from customers’ usage [17]. DoE is an efficient procedure for planning experiments that will yield a valid and objectives conclusions [18]. It is widely used in analyzing factors of phenomena and used to confirm findings from cause and effect analysis [19][20].

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II. METHODOLOGY

Study has been made on past researches to review the importance of Design of experiment in scientific and industrial development. A number of researches in design of experiment are quantified from publications in Emerald Group Publishing Limited and Science-Direct since 1971 to 2011. The figures are plotted over the years to identify the significant growth in later phenomenon, visualizing of how rigorous the study on DoE as a statistical tools to unfold new knowledge and findings. Further information obtained from several research papers were tabulated into category of implementation versus type of DoE employed in such applications. This is to identify which sector DoE are mostly applied and where it is least used. A pilot survey had been conducted in the beginning of this study to unfold the exact situation in Malaysia whether design of experiment is adopted as a primary tool in industrial research and in developing competitive advantage. As noted, research is primary activity in science that is directed to find scientific explanation of phenomena and finding solution to social importance [21]. In this study postal survey had been selected as a medium for collection of data. The survey instrument is a questionnaire focus to obtain general information on the organization, general view on DoE, possible success factors and failure faced in the implementation. In the survey questions, level of agreement ratings utilized a conventional Likert scale (Very High Extent = 6, Very Low extent = 1). The SPSS 16 is used to tabulate the collected information and crosstabs analysis is done to visualize significant relationships.

The pilot survey focused on the manufacturing industry as they were the important section contributed to Malaysia’s economic growth as reported in MPC 20th productivity report [22]. It was reported in Semi Medium Enterprise (SME) Annual report in year 2011, manufacturing sector turned to be the second contributor to GDP in the key economic activity [23]. Further reviews were made from the annual reports published by National SME Development Council (SEMIDEC) and Malaysia Productivity Corporation (MPC) on the achievement of large and semi medium scale of industries. Relevant analysis is conducted to observe how the strategic plan set up by the government could overcome industrial challenge in the progressing years. This is to correlate the strategic planning activity with potential effect of employing DoE in pursuit of sustainable business.

III .RESULTS AND DISCUSSION

Notable outcomes and contribution of this experimental tool is seen in many experiments conducted in individual researches, educational institutions, manufacturing and service industries. Number of researches related to design of experiments increased drastically from 2003 until 2011 indicating there is significant interest on DoE application in the upcoming years as shown in Fig. 1. In year 2011, number of research related to DoE in Science-Direct increased to 4616 publications, three times than in the year of 2000 which was only 1261 publications. Similarly there is an increasing trend reported by publication of the Emerald group although number of publication is very much lesser compare to Science-Direct. The importance of DoE is becoming apparent as many noble findings and results are reported from its applications. In this literature study, the applications of DoE are analyzed for three criteria such as for research and design, product and process optimization, screening and problem solving.

Fig.1. Number of Publication related Design of Experiments.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Num

ber o

f Pub

licat

ion

Year of Publication

NUMBER OF PUBLICATIONS RELATED TO DESIGN OF EXPERIMENT

Emerald

Sci-direct An increasing trend of research activities on subject related to Design of Experiments.


Proceedings of the 2016 International ConfKuala Lumpur, Malaysia, March 8-10, 2016

It was noted that product and procelevel of quality performance. This could be past research papers.

Fig.2. The matrix

As shown in Fig. 2, Taguchi method isexperimental design has resulted in cost savsurface method and central composite designoutput characteristic [25]. It shows that optimcommon [26]. Optimization will focus on hresearch and design are seen to be lesser thdevelopment to ensure complexity in next pcould be conducted at the early stage of the its application in screening process is also identification of real causes of problem at ththe next step of the improvement activity. Bimportant factor, can determine whether facresponse(s) [28]. For example, a computerprovide effective method for continuous st[32];

(1) More information obtained per exp

(2) A reduction in the number and cost

(3) It makes possible the calculation of knowledge of the process and,

(4) It facilitates the determination of th

The Result of Pilot Survey

As the objective is to explore potentialmanufacturing sector around Malaysia to oshown in Fig. 3.

ference on Industrial Engineering and Operations Manag6

ess optimization is the main criteria where DoE is highly useen in Fig. 2 a sample of 62 case studies which were ran

x of application criteria and type of DoE from samples of research papers

s leading the application area in optimization; its approaving and reduction in experimental time [24]. It is followen which are statistically developed for analyzing a numbermization of products and process is the most significant ahow to have the best result within availability resources.

han in optimization, it is vital to have the best design in trocessing system could be minimized. In addition, it is exproduction to avoid the value of experiment falls over timcrucial to industrial environment. As time is always th

he initial stage of problem solving will shorten the analysisBy using DoE one can learn about the process being invctors are interact, build a mathematical model or predictr simulation by using DoE techniques helped to analyzetudy in product design [29][30][31]. Several advantages

eriment than unplanned approaches,

t of experiments,

f the interactions among variables within the range studied

he operating conditions necessary to improve the process.

l aspect in DoE implementation, a number of 714 surveobtain preliminary information on the deployment of D

gement

used for improving such ndomly selected from

s.

ach which simplified the ed by the use of response r of factors over desirable

area where DoE is used in . Although application in the early stage of product xpected more experiments me [27]. The important of he key factor in industry, s time and lead the way to vestigated, able to screen ion and can optimize the e the optimal design and of DoE are listed below

d, leading to a better

eys had been sent to the oE in the work place as


Proceedings of the 2016 International ConfKuala Lumpur, Malaysia, March 8-10, 2016

Fig.3

Information on the company names wereThere are thirty eight respondents who repAlthough the number respondent is small it r

The response towards this survey is loperformance and internal activity is prohibistatistical approach have helped in reducinsurvey by category are shown by the pirespondents, second is metal company whicontributes 11 % respectively. Next, Table found that from the pilot survey, about 45employing this tool in their work place. Sincconclude in the beginning that the implemresolved.

ference on Industrial Engineering and Operations Manag6

3. Number of survey delivered to manufacturing company

e obtained from Federation of Malaysian Manufacturer (Fplied the survey form; there are four who do not complrepresents about 5.3 % of the sample population.

Fig.4. Number of respondent by category

ow due to strategic reasons of each company. Disclosited. Secondly, the DoE is not perceived as an important ng technical and non-technical conflicts or issues in the ie-chart as depicted in Fig. 4. Electrical company conich contributes 13% and third is from chemical and mec1 explains the implementation of DoE by type of manufa5% of the companies were implementing the DoE and ce it is more than 30% of company were not implementing

mentation of this tool in Malaysia’s company is still a ph

gement

FMM) published in 2011. letely answer the survey.

sure of company details, tool as other tactical and company. The results of

ntributes 18% over total chanical company which

acturing industries. It was another 55 % were not

g the DoE, it is prudent to henomena that should be



The response towards this survey is low due to strategic reasons of each company. Disclosure of company details, performance and internal activity is prohibited. Secondly, the DoE is not perceived as an important tool as other tactical and statistical approach have helped in reducing technical and non-technical conflicts or issues in the company. The results of survey by category are shown by the pie-chart as depicted in Figure 4. Electrical company contributes 18% over total respondents, second is metal company which contributes 13% and third is from chemical and mechanical company which contributes 11 % respectively. Next, Table 1 explains the implementation of DoE by type of manufacturing industries. It was found that from the pilot survey, about 45% of the companies were implementing the DoE and another 55 % were not employing this tool in their work place. Since it is more than 30% of company were not implementing the DoE, it is prudent to conclude in the beginning that the implementation of this tool in Malaysia’s company is still a phenomena that should be resolved.

TABLE 1 Manufacturing industries and their implementation of DoE.

Type of Manufacturing Industry/Business Is DOE applied in company

Total

DoE Implemented

Yes No Percent by category

Mechanical 3 1 4 75%

Chemical 2 2 4 50%

Electrical 3 4 7 42%

Plastic Industry 0 2 2 0%

Metal Industry 3 2 5 60%

Oil and Gas Industry 1 1 2 50%

Food and Beverage 1 2 3 33%

Others 4 7 11 36%

In contrast, the implementation by type of company has indicated that 75% of mechanical industry was in favour with application of DoE, this is followed by 60% of metal industry. Furthermore, 50% of chemical and oil and gas companies were also employing this tool. The pilot survey has revealed most of the respondents agreed on the importance of DoE as a tool to be applied in the industry where 76 % out of them agreed in their opinion. As reported, the application of DoE in industry involved more than five factors, this could be a reason that DoE is commonly used as a screening tool. About 52 % of the valid respondent stated that DoE is implemented in product design, 43% said that it is being used in process design and optimization. However 94% of the respondents mentioned that they applied DoE for quality improvement. This shows that DoE is mainly implemented in on-line quality control. On the other hand, information obtained has shown that the application in product and process design is not merely the focus of the industry, although this is the area where competitive advantage is crucially affected. The application in product and process optimization was not also being the main objective in implementing DoE, which actually will contribute to the loss of opportunity in time and cost saving activity. Furthermore, nearly 81% of respondents do not agree to hire consultant to assist them in implementing the DoE, as known, the economic factor is the probable reason why this is declined. Subsequently, it is too early to understand the benefit of DoE in the beginning of its application where initial investment costs are to be decided.

In addition, the crosstabs analysis has shown that collaboration activities with local educational or oversea institutions give some impact on the implementation of DoE in industry. About 64 % of company which had the collaboration activity was implementing DoE. Only 33% of company which did not have collaboration with educational institutions employed DoE in their work place. Hence, this signifies the educational institution function as a place to learn DoE and as an economic source of information to acquire fundamental knowledge and guidance for effective and efficient learning in DoE. In general, 86% respondents agreed on the benefit of having collaboration with local institution or university. As mentioned in Global Competitiveness Report in 2012-2013, one of the drivers that give excellent capacity for innovation is coming from the strong collaboration between academic, for example, Switzerland was ranked the number one with regards to innovation and in its remarkable performance due to its extensive collaboration that exist between industry and academia [5]. Experts from academic institution and practitioner in industry when working together will produce good results and sharing invaluable experiences that benefit both entities. However in Malaysia, many industry are not welcoming experts from academic



institutions, result in the pilot survey has shown that only 58% company have contacted them in their collaboration activity. Encouragement and incentive is proposed to enhance the collaboration activity, not only for DoE implementation purpose but also to other strategic planning process that could propel the business growth. Although, always the trade secret is the main resistance, local company shall have broader scope and better initiatives to ensure improvement is always an opening.

The preliminary analysis on the barrier in implementing DoE has resulted as in Table 2. It was found that about 52.7% of respondents were in favor with barriers in implementing DoE are caused by the limited software aids and 52.4% of them said it was due to inability to communicate the results. The used of software is very much important to perform the statistical calculations, in addition it is also necessary to have software that could describe clearly the detail of experimental results. Despite the fact that there are many in the market, some of the software does not adequately satisfy industrial needs [2].

TABLE 2 Potential barriers in implementing DoE.

Potential Barriers

Moderate

High

Very High

Extent

Total %

1-Unable to communicate clearly the results 34% 10.5% 7.9% 52.4%

2-Insuffiucient resourcess and training 21% 15.5% 10.5% 47.0%

3-Limited software aids 21.1% 18.4% 13.2% 52.7%

4-Difficulty in change of culture and management 18.4% 13.2% 7.95 39.5%

5-Inadequate knowledge and understanding of DoE 15.8% 15.8% 13.2% 44.8%

6-Inadequate guidelines in experimentation 23.7% 10.5% 10.5% 44.7%

7-Not part of company vision 10.5% 15.8% 2.6% 28.9%

The skill in interpretation of experimental data and results shall be communicated in much more understandable way to the audience especially on the practical relevance of the finding [7][33]. When top management is concerned, it will be more important as they are the decision makers to agree on any expenses and resources employed for this purpose. The above two barriers are interrelated with amount of knowledge acquired by experimenters or practitioners since a substantial knowledge on DoE will be the basic fundamental in presenting such findings and recommending appropriate action plans. Next, insufficient resources and trainings is the third barriers when 47 % respondents agreed this will create difficulties in learning and application of the DoE. For example, lack of design knowledge and expert experience in organization resulted in lack of attention on powerful techniques such as DoE, QFD and others [34]. Subsequently, these disputes were supported by 44.8 % of respondents which had replied that inadequate knowledge and lack of understanding in DoE will hinder the effective use of this tool. About 44.7% respondents had voiced out that inadequate guidelines in experimentation will also moderately affect the implementation of DoE. In contrast, the issue of DoE as being part of the company vision only agreed by 28.9% of respondent and this indicates company vision has rarely influenced the implementation. On the other hand, 39.5% of respondent believed that change in culture could be a moderate effect to the implementation of DoE. This similar case was noted in a past research that low managerial commitment and resistance to change was one of the barriers to widespread use of DoE [2].

The reliability of data is confirmed by the value of Cronbach‘s Alpha which is 0.94. This show that internal consistency of a set of the measurement is homogenous and not peculiar indication to void the information obtained. Statistically each questionnaire and their responses are valued for the degree of significant based on the value of the mean and standard deviation. This is shown in Table 3. The higher mean score indicates higher significant level of contribution towards the barrier of DoE implementation. Although there is a difference in significant levels of potential barriers when compare with the descriptive figures in Table 2, but they are the same for main contributors and least significant barrier. It is shown that the limited software aids has the highest mean score of 4.89 and the not part of the company vision/mission obtained the lowest score of 4.21.



Table 3: Calculation of mean and standard deviation of all potential barriers

Potential Barrier Mean Std. Deviation 1- Unable to communicate clearly the result of experiment 2- Insufficient resources and training 3- Limited software aids 4- Difficulty in change of culture and management 5- Inadequate knowledge and understanding of DoE 6- Inadequate guidelines in experimentation 7- Not part of company vision/mission

4.47

4.53

4.89

4.39

4.55

4.47

4.21

1.91

1.94

1.73

1.93

1.96

1.91

2.08

The pilot survey results indicate that the most important barriers to the implementation of the DoE were limited software aids, inadequate knowledge and understanding of DoE, and insufficient resources and training. The ability to communicate clearly the result was equally important to convince the implementation DoE is necessary.

Proposed Framework for DoE Implementation

Implementation framework is proposed upon considering barriers obtained from the pilot survey. A framework is to visualize the way how DoE could be implemented within a controlled environment. As mentioned, lack of adequate guidelines will cause unsuccessful application of DoE. Workplace constraint always pose difficulties in any implementation program, even support is provided by institutions and or by experts, the outcome of an undertaking of this implementation lies under the readiness of the organization to be committed with specific objectives over an implementation framework. The framework does not explain the step how to perform the DoE, in which this can be learned during the trainings or from available reference materials. However it emphasizes more on the function of the management to ensure planned activities are achieved according to the implementation objectives. The framework is shown in Fig. 5.

Fig. 5. Proposed Framework of DoE Implementation

Implementation Step Review Next Action Plan

1 -Develop implementation time frame.

2-Identify resources needed for implementation.

3-Acquire all necessary resources ;trainers , materials, softwares.

4 -Determine areas for implementation and responsible person.

5 -Develop guidelines, coaching and training plans.

7 -Recognition of completed DoE project.

6 -Record and Control of experimentation project.

1 Availability of Resources

2 Competency of Staff in DoE

3 Number of DoE Projects.

4 Results of DoE &Gap Analysis

1- Improve Performance & Removing Pitfalls

2- Intensify Implementation

3- Continuous Recognition



The success of this framework is very much influenced by the completion of each step which covers every significant aspect that could be a resistance to the application of DoE if they are not addressed and resolved in appropriate time frame.

IV. CONCLUSION

The situation in Malaysia industry is very much influenced by local and oversea demands. Fluctuations in world economy will affect the micro and macro factors that shape the present and future of the manufacturing industry. A company that is to survive from any adverse circumstances has to adopt effective and efficient management system which employs relevant enabling technology and tools in the day-to-day operations control. As the pilot survey results has unveiled several issues on DoE implementation in Malaysia, several conclusion could be made to address this matter. DoE which literally known as an enabling tool in product and process design and at present are having a historic failure which left undiscovered within many of local manufacturing company. It could be concluded that the interest in applying Doe is hampered in most company when company which have implemented the DoE are found to face several barriers which are very much related to strategic planning process. Management focus, systematic approach and human capital development are found to be issues in this study. The development of competitive advantage, the distinctive capability of each company is not fostered and supported with knowledge unearthed through DoE. In reality, successful implementation and outcomes are always triggered from action of being focused to the fundamental and principle of technology. Inaccurate information coupled with ambiguous phenomena will pro long and increase level of uncertainties in the workplace. The readiness of resources to cope with technology change could be accelerated by employing DoE during the development process or when changes take place. DoE shall be perceived not only on its tangible or variable output, as the driver to effective use of DoE lies between human and the technology, it can be emphasized that DoE is very much important to increase the competency level of workers in exploring the knowledge in design, processing and controlling activity. As a matter of fact, a company will never grow bigger than its employee ability.

V. RECOMMENDATION

It is suggested to proceed with a full survey on the implementation of DoE in Malaysia Industry. Since potential barriers and difficulties uncovered by the pilot survey are significant in research and development program, in innovations and optimization of product and process design, effort could be made to improve the implementation of DoE nation-wide. A guideline to implement DoE in industry shall be established through a basic framework that could define the implementation steps and area to which DoE approaches or techniques are most suitably used for their notable outcomes.

VI. ACKNOWLEDGEMENT

We would like to thank Ministry of International Trade and Industry by providing such recommendation letter in order to raise concern on the execution of the pilot survey programme, and also to all valuable respondents who have provided such information for this study.

REFERENCES

[1] Hatami, M. , Cuijpers, M.C.M. , Boot. , M.D. Experimental optimization of the vanes geometry for a variable geometry turbocharger (VGT) using a Design of Experiment (DoE) approach. Energy Conversion and Management .2015, 106: 1057–1070.

[2] Tanco, M. Viles, E. Ilzarbe, L.and Alvarez, M.J. Barriers Faced by Engineers When Applying Design of Experiments. The TQM Journal. Vol. 21. No.6. 2009, pp. 565-575.

[3] Besseris, G.J. Applying the DOE toolkit on a Lean –and-Green Six Sigma Maritime-Operation Improvement Project. International Journal of Lean Six Sigma Vol. 2 No.3. 2011, pp. 270 – 284.

[4] Box G.E.P, Hunter J.S and Hunter W.G. Statistics for Experimenters, Second Edition, John Wiley & Sons Inc, United States., 2nd Edition, 2005

[5] Costa, N.R.P., Pires, A.R. & Ribeiro, C.O. Guidelines to help practitioners of design of experiments. The TQM Magazine Vol. 18 No. 4. 2006 pp. 386 – 399.

[6] Malaysia Productivity Corporation, MPC 21st Productivity Report, 2013/2014.

[7] Montgomery, D.C. Design and Analysis of Experiments. Seventh Edition. Asia. John Wiley & Sons (Asia) Pte Ltd. 2009



[8] Firka, D. Statistical, Technical and Sociological Dimensions of Design of Experiments. The TQM JournalVol.23/(No. 4), 2011

[9] Raj, R.E. & Daniel, B.S.S. Customization of closed-cell aluminium foam properties using design of experiments.Materials Science and Engineering. 2011, A 528 : 2067 – 2075

[10] Friday-Stroud, S.S. & Sutterfiled, J.S, A conceptual framework for integrating six-sigma and strategic managementmethodologies to quantify decision making. The TQM Magazine, Vol.19 No. 6, 2007, pp. 561-571.

[11] Charles, J.P. , Hannane, F. , El-Mossaoui, H. , Zegaoui, A. , Nguyen. , Petit, P. , Aillerie, M. Faulty PV panelidentification using the Design of Experiments (DoE) method. Electrical Power and Energy Systems. 2014, 57: 31–38.

[12] Jenarthanan, M.P. , Jeyapaul, N. , Naresh, N. Modelling and analysis of factors influencing surface roughness anddelamination of milling of GFRP laminates using RSM. Multidiscipline Modeling in Materials and Structures,Vol. 8 Issue 4.2012, pp. 489-504.

[13] Antony, J., Chou, T.Y. & Ghosh, S. Training for design of experiments. Work Study Volume k52 Number 7.2003, pp. 341 – 346

[14] Antony, J. , Sivanathan, L. , Gijo, E.V. Design of Experiments in a higher education setting.International Journal of Productivity and Performance Management, Vol. 63 Issue 4 .2014,pp. 513 – 521.

[15] Cox D. R.& Reid N, The Theory of the Design of Experiment. First Edition, Chapman and Hall/CRC. 2000

[16] Roy, R. K. A Primer On The Taguchi Method. Competitive Manufacturing Series. New York. Van NosrrandReinhold. 1990.

[17] Taguchi, S. Special Intensive 2-day Seminar Robust Engineering (Taguchi Methods). Universiti Teknologi Malaysia.22-23 April 2013.

[18] Borkowski, J.J. Discussion of ”Response surface design evaluation and comparison” by Christine Anderson-Cook,Connie Borror, and Douglas Montgomery. Journal of Statistical Planning and Inference .2009, 139 : 650 – 652

[19] Prashar, A. Process improvement in farm equipment sector (FES): a case on Six Sigma adoption. InternationalJournal of Lean Six Sigma, Vol. 5 Issue 1.2014, pp. 62 – 88.

[20] Besseris, G. Multi-factorial Lean Six Sigma product optimization for quality, leanness and safety: A case study infood product improvement. International Journal of Lean Six Sigma, Vol. 5 Issue 3 .2014,pp. 253 – 278.

[21] Nwokah, N.G. , Kiabel, B.D. , Briggs, A.E. Philosophical Foundations and Research Relevance: Issues forMarketing Information Research. European Journal of Scientific Research ISSN 1450-216X Vol.33 No.3.2009,pp.429-437

[22] Malaysia Productivity Corporation, MPC 20th Productivity Report. 2012/2013.

[23] SME Corporation Malaysia, SME Annual Report 2012/13.

[24] Altan, M. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods.Materials and Design. 2010, 31 : 599 – 604.

[25] Li, J., Liu, L., Wu, J., Sun, J., Du, G., Chen, J. & Tao, W. Modeling and optimization of cutinase production byrecombinant Escherichia coli based on statistical experimental designs. Korean Journal Chemical Engineering. 2010,27 : 1233 – 1238.

[26] Gijo, E.V. , Bhat, S. Jnanesh, N.A. Application of Six Sigma methodology in a small-scale foundry industry.International Journal of Lean Six Sigma, Vol. 5 Issue 2.2013, pp. 193 – 211.

[27] Terwiesch, C. Bohn, R.E. Learning and Process Improvement During Production Ramp- Up. Int. Journal ProductionEconomics 70 (2001) 1-19.

[28] Islam, M.F. & Lye, L.M. Combined use of dimensional analysis and modern experimental design methodologiesin hydrodynamics experiments. Ocean Engineering. 2009, 36 : 237 – 247

[29] Shuaeib, F.M., Hamouda, A.M.S., Wong, S.V., Radin Umar, R.S. & Megat Ahmed, M.M.H. A new motorcyclehelmet liner material: The finite element simulation and design experiment optimization. Materials and Design. 2007,28 : 182 – 195.



[30] Díaz, J.J.D.C., Nieto. P.J.G. , Luengas, A.L.M. , Domínguez, F.J.S. , Hernández, J.D. Non-linear numericalanalysis of plywood board timber connections by DOE-FEM and full-scale experimental validation.Engineering Structures.2013, 49: 76–90.

[31] Calman, A.M , Kwak, H. , McDonald, M. , Upton, S. 2015. Capturing experimental design insights in supportof the modelbased system engineering approach. Procedia Computer Science .2015,44: 315 – 324.

[32] Hajjaji, N., Renaudin, V., Houas, A. & Pons, M.N. Factorial design of experiment (DOE) for parametric exegeticinvestigation of a steam methane reforming process for hydrogen production. Chemical Engineering and Processing.2010, 49 : 500 – 507.

[33] Julian, D. & Holmshaw, P., Six Sigma in low volume and complex environment. International Journal of Lean SixSigma, Vol 3, No.2012, pp. 36 – 44.

[34] Valhmohammadi C, 2011.The impact of TQM implementation on the organizational performance of Iranianmanufacturing SMEs. The TQM Journal, Vol. 23. No. 5. 2011, pp. 496-509.

BIOGRAPHY

Haris Jamaluddin is a research student in quality engineering area. He has a Master of Science Degree in Industrial and Technology Management from National University of Malaysia. He is currently conducting a research and study on the application of design of experiment in industry. He is a practioner in quality engineeering activities such as in new product development process and quality improvement activities. He had served the manufacturing industry for more than 20 years in technical, engineering, production, human capital development, implementation of quality management system and quality control initiatives.

Prof Ir Dr Jaharah A. Ghani received her BEng and MSc in Manufacturing System Engineering from Leeds Polytechnic and Warwick University, United Kingdom respectively. She obtained her PhD from University of Malaya, Malaysia in 2005. Her research activities mainly in Machining; including, machinability study, details study of cutting tool development, wear of cutting tool (including tool wear monitoring) and chip formation, and extensively used of Taguchi Design of Experiment.


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