APPLICATION OF DIGITAL CELLULAR RADIO FOR MOBILE LOCATION ESTIMATION


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A HYBRID OF KANSEI ENGINEERING (KE) AND 

ANALYTICAL HIERARCHY PROCESS (AHP) TO  

DEVELOP CONCEPTUAL DESIGNS OF PORTABLE  

OIL SPILL SKIMMER 

 RPRAKASH RAMANATHAN, LOKMAN ABDULLAH*,  MUHAMMAD HAFIDZ FAZLI 

MD FAUADI, MUHAMMAD SYAFIQ SYED MOHAMED  

AND KHAIRUN NAJMI KAMALUDIN 

Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, 

76100 Durian Tunggal, Melaka, Malaysia  

 
*
Corresponding author: lokman@utem.edu.my  

(Received: 9th May 2022; Accepted: 24th August 2022; Published on-line: 4th January 2023)  

ABSTRACT: Currently, there are huge demands on developing a design that fulfils the 

characteristics of performance, cost, safety, and aesthetics. However, the conceptual design 

stages in industrial products lack the involvement of user requirements as it is typically 

focused on the product's performance. Consequently, specific criteria such as the product's 

ease of use, safety, and robustness cannot be compared and measured when designing 

industrial products. Owing to this reason, this research proposes a new technique that 

integrates Kansei Engineering with Analytical Hierarchy Process (AHP) to address the 

issue. The research objective is to investigate an oil spill skimmer's user and technical 

requirements by incorporating the Kansei Engineering method. The approach to carry out 

this research is to incorporate the Kansei and the basic AHP methods. Kansei Engineering 

will suggest the required design elements that must be included to design and fabricate a 

portable oil spill skimmer. At the same time, the AHP method is used to select the best 

design based on the developed conceptual design. The effectiveness of the proposed method 

was verified by comparing it with other established methods, such as TOPSIS (Technique of 

Order Preference by Similarity to Ideal Solution). Moreover, sensitivity analysis was used to 

investigate the robustness of the AHP result. There are 5 conceptual designs in total, 

assessed in this research. The result showed that out of the 5 conceptual designs, design 

number 3 has the highest ranking (priority ranking = 0.2603). Thus, the most suitable 

conceptual design for the portable oil spill skimmer to be fabricated is design 3. The finding 

also shows that the result from AHP was valid and robust. 

ABSTRAK: Pada masa kini, terdapat permintaan besar bagi membangunkan reka bentuk 

yang memenuhi ciri-ciri prestasi, kos, keselamatan dan estetika. Walau bagaimanapun, 

industri kurang melibatkan keperluan pengguna pada peringkat reka bentuk konsep produk 

industri, kerana ia biasanya tertumpu pada prestasi produk. Ini menyebabkan kriteria khusus 

seperti kemudahan menggunakan produk, keselamatan dan keteguhan produk tidak dapat 

dibandingkan dan diukur semasa mereka bentuk produk industri. Disebabkan faktor 

berkenaan, kajian ini mencadangkan teknik baharu yang mengintegrasikan Kejuruteraan 

Kansei bersama Proses Hierarki Analitik (AHP) bagi menangani isu tersebut. Objektif 

kajian adalah bagi menyiasat keperluan pengguna dan keperluan teknikal menyaring 

tumpahan minyak dengan menggabungkan kaedah Kejuruteraan Kansei. Pendekatan kajian 

ini adalah dengan menggabungkan Kansei dan kaedah asas AHP. Kejuruteraan Kansei 

mencadangkan elemen reka bentuk yang diperlukan yang mesti disertakan bagi mereka 

bentuk dan menyaring tumpahan minyak mudah alih. Pada masa sama, kaedah AHP 



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digunakan bagi memilih reka bentuk terbaik berdasarkan reka bentuk konsep yang 

dibangunkan. Keberkesanan kaedah yang dicadangkan telah disahkan dengan 

membandingkannya dengan kaedah lain yang telah terbukti, seperti TOPSIS (Teknik Aturan 

Kehendak Berdasarkan Persamaan dengan Solusi Ideal). Selain itu, analisis sensitiviti 

digunakan bagi mengkaji keteguhan keputusan AHP. Terdapat 5 reka bentuk konseptual 

yang dinilai dalam kajian ini. Dapatan kajian menunjukkan bahawa reka bentuk nombor 3 

mempunyai keputusan tertinggi (keutamaan kedudukan = 0.2603) daripada 5 reka bentuk 

konseptual ini. Oleh itu, reka bentuk konsep yang paling sesuai bagi saringan tumpahan 

minyak mudah alih yang akan dibina adalah reka bentuk 3. Dapatan kajian juga 

menunjukkan bahawa hasil daripada AHP adalah sah dan kukuh. 

KEYWORDS:  Kansei Engineering; analytical hierarchy process (AHP); product 

development process; oil spill skimmer 

1. INTRODUCTION 

Industrial equipment sales often face tough challenges in maintaining their sales volume 

and customer loyalty since many manufacturers compete with one another for customers. 

Therefore, manufacturers often spend their time and resources studying customers' 

purchasing behaviour and preferences, such as function, appearance, and usability. Of all 

these requirements, visual appeal plays a vital role in influencing customer purchasing 

decisions. Customer impressions are influenced by various elements, including the brand of 

the product, its purpose, look, and usefulness. However, the product look is the one that 

creates the most visual engagement to the client and product compared to other aspects [1]. 

The application of AHP has been utilised in the area of the machine tools industry and the 

textile industry 

Consumer products, such as household appliances, are typically regarded as attractive 

and inexpensive, but industrial items, such as heavy machinery, should be of high quality but 

not necessarily attractive. Any industrial product's development usually focuses on technical 

standards, including objectives and well-defined metrics such as speed, power, and many 

others that could be compared and tested. While meeting technical criteria is essential, it is 

not always enough for a product to succeed [2]. Several elements are difficult to quantify yet 

influence machine design and selection. Users' impressions of different machine tools have 

received minimal attention in terms of strategies for selecting, analysing, and comparing 

machine tools. Thus, there is a need to incorporate both customer and technical requirements 

in the industrial product development process. 

A study done by Marini investigated the use of AHP and Analytical Network Process 

(ANP) together with Quality Function Deployment (QFD) in concept design and material 

selection for making better decisions to improve product success [3]. However, the 

involvement of customer requirements is still lacking when developing a design concept [4]. 

Therefore, research was done to propose a method based on TOPSIS and fuzzy AHP, which 

could be used in concept design evaluation [5]. Renzi researched using AHP and ANP 

together with other multi-criteria decision methods for design evaluation in the automotive 

industry, trying to transfer knowledge on decision-making methods to the industrial context 

[6]. Moreover, Rosli investigated a systematic product design model that assists decision-

makers or design engineers in improving current design through the idea generation method 

of Theory of Inventive Problem Solving (TRIZ) and utilising the analytical hierarchy process 

(AHP) to perform the selection of best-generated idea [7]. 



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A study was conducted to show that the proposed (House of Quality) HOQ and Fuzzy-

AHP provided a novel alternative to existing methods to perform design concept evaluations 

in the early stages of product development, with the capability to accommodate uncertainties 

and vagueness using the optimum number of pairwise comparisons [8]. Furthermore, a 

research was conducted by Turan, proposing a rough number to be used by VIKOR and AHP 

methods to develop a systematic framework for the concept evaluation process [9]. 

Nevertheless, the proposed best design concept might be biased toward designers' 

expectations rather than the customers' expectations. 

It is well known that previous researchers have investigated and reported on several 

modified AHP. To the best of the author's knowledge, however, the implementation of 

Kansei Engineering as a tool for optimisation paired with AHP in the development of 

industrial products such as oil spill skimmer is still inadequate and uncommon. Furthermore, 

although several researchers have researched the fabrication of oil spill skimmers, there is no 

proof that KE and AHP were utilised in the design and fabrication process [10]–[15]. 

Therefore, this study aims to employ Kansei Engineering to investigate the user and technical 

requirements to generate multiple conceptual designs for an oil spill skimmer, which will be 

analysed using Analytical Hierarchy Process (AHP) to choose the ideal oil spill skimmer 

conceptual design.  

2. METHODOLOGY 

2.1  Research Framework 

The development and selection of the best conceptual design for a portable oil spill 

skimmer are split into two sections in this study. First, Kansei Engineering is employed to 

build an overall design aspect of the oil spill skimmer in phase one. This methodology has 

five layers: Kansei word selection, product sample collection, questionnaire distribution, 

reliability testing, data interpretation, and design idea creation. The AHP is then utilised in 

part 2 to determine the relative weight of each factor at each level to arrive at the overall 

product assessment system. This is done to determine which design idea produced by Kansei 

Engineering is the best. Finally, to evaluate the robustness of the AHP, the results are 

compared and analysed using TOPSIS and sensitivity analysis. 

2.2  Kansei Engineering and Reliability Testing 

Kansei Engineering (KE) uses seven primary stages to categorise the customer's needs 

and technical requirements. Kansei Engineering type 1 is employed in this study. This 

method transforms a single product concept into a more complex concept, which is then 

developed to numerous levels and interpreted in terms of the physical characteristics of the 

product design. Category categorisation is a breakdown strategy from a targeted concept of a 

new product to the corresponding subjective Kansei to the objective design requirements. 

Mazda's design of the Miata, the world's most successful sports automobile, is a well-known 

example of this category application [16]. A previous research can be referred to identify the 

step-by-step procedure of Kansei Engineering methodology [17]. 

Reliability testing aims to evaluate the reliability or internal consistency by utilising 

Cronbach's alpha to measure the strength of the consistency. Cronbach's alpha analysis 

determines whether the multiple-question Likert scale survey is reliable. Higher values of 

Cronbach's Alpha signify more reliability of the survey or questionnaire; it has a range of 0 to 

1. The minimal number of questions and weak interconnection between objects result in a 

low alpha score in the reliability test. The alpha value achieved, for example, will be pretty 



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low due to a lack of correlation between the variables and items, resulting in the 

questionnaire being rejected and requiring modification. On the other hand, the items may be 

redundant if the alpha is too high since the questions are the same but in a different format. 

The formula of Cronbach's Alpha and the rule of thumb is shown below in Eq. (1) [18]. 

𝑎 = 
𝑁.𝑐̄ 

v +(𝑁−1).c 
  (1) 

where, 

N= the number of items 

c̄ = average covariance between items-pairs 

v̄ = average variance 

 

Fig. 1: Research framework. 

2.3  Analytical Hierarchy Process (AHP) 

In the next step, the research will focus on establishing the Analytical Hierarchy Process 

(AHP) in determining the most proper rank of the design options. Elements related to the 

goals, criteria, and relevant alternatives are synthesised from the Kansei Engineering method. 

Table 1 shows the linguistic variables and the pairwise matrix value. 



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Table 1: Linguistic variables and values for pairwise matrix for basic AHP [19]. 

Linguistic variables AHP Scale 

Important 9 

Very strongly important 7 

Strongly important 5 

Weakly important 3 

Equally important 1 

Intermediate 2,4,6,8 

AHP technique is a process that consists of the following steps with the help of a few 

equations as narrated by [20]: 

Step 1: First, decide what needs to be achieved, then narrow down the options. Selecting 

criteria, a quantifiable aspect that aids in illustrating and specifying options, necessitates 

practical judgment. 

Step 2: Paired comparisons are needed in two segments as follows: 

1. Between criteria  
2. Between alternatives using each criterion. 

The matrices of pairwise comparisons are on a fundamental scale from 1 to 9. The 

application of AHP is based on expert judgment. One of the major advantages of AHP is that 

the analysis does not always require statistically significant sample size as reported by [21]. 

Furthermore, it is obvious that conducting such huge numbers of real expert examinations 

requires too much effort, time, and financial resources. Thus, an evaluation from a single 

qualified expert is adequate [22]. As the input data in AHP analysis are based on an expert’s 

perceived judgement, a single input from an expert is sufficient as a representative in the 

pairwise evaluation of AHP [23]. However, AHP may also be ineffective in research with a 

high sample size since "cold-called" experts are prone to give arbitrary responses, severely 

altering the consistency of the assessments [24]. Data is gathered through questionnaires and 

interviews. The comparison matrix determines which criteria and sub-criteria are more 

critical than others. Therefore, expert input is critical. An oil skimmer expert was selected as 

the respondent in this research. The respondent was chosen based on years of expertise and 

knowledge in the oil skimmer sector. The comparison matrix (n x n), where n is the number 

of criteria. 

Step 3: Let Xij denote the order of preference of the ith factor compared to the jth factor. 

Then Eq. (2): 

Xij = 
1

𝑋𝑖𝑗
 (2) 

Step 4: A normalised pairwise comparison matrix is obtained by adopting the following 

procedure: 

1. Sum of every column.  
2. Divide all the numbers in the matrix respectively by the obtained column sum. 
3. Average the rows to obtain relative weights. 

Step 5: Calculate Eigenvector, maximum Eigenvalue, and Consistency Index (CI). Refer 

to Eq. (3). 

𝐶𝐼 = 
𝜆max− 𝑛

𝑛−1
 (3) 



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Here, λmax is the Eigenvalue corresponding to the matrix of pairwise comparisons, and n is 

the number of criteria. 

Consistency ratio (CR) is defined by referring to Eq. (4): 

𝐶𝑅 = 
𝐶𝐼

𝑅𝐶𝐼
 (4) 

Where (RCI) is a random consistency index. 

Generally, a CR value of less than 0.1 is acceptable; otherwise, the pairwise comparisons 

should be altered to eliminate incoherence. The TOPSIS and sensitivity analysis methods are 

used to validate the AHP results. First, the TOPSIS analysis is used to confirm the AHP 

ranking result. The AHP and TOPSIS parameters are the same, but the mathematical 

computations are different, allowing the TOPSIS ranking result to confirm the AHP ranking 

result. In terms of sensitivity analysis, the AHP result is examined to determine the result's 

robustness in relation to the weighted criteria. When modifications in criterion weightage are 

applied, a complete sensitivity analysis is done to detect the variance in the behaviour of the 

ranking alternatives. 

2.4 Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) 

TOPSIS technique is a process that consists of the following steps with the help of 

equations [25]: 

Step 1: Form the decision matrix. Refer to Eq. (5). 

𝐷𝑀 = 

𝑥11 ⋯ 𝑥1𝑛
⋮ ⋱ ⋮

𝑥𝑚1 ⋯ 𝑥𝑚𝑛
 (5) 

Step 2: The normalised decision matrix is done. Refer to Eq. (6) 

𝑁𝐷𝑀 = 𝑟𝑖𝑗 = 
𝑥𝑖𝑗

√(𝛴𝑖=1
𝑚 𝑥𝑖𝑗

2 )
 (6) 

Step 3: The weighted normalised decision matrix refers to Eq. (7) (skipped as the 

weightage is obtained from AHP). 

𝑉 = 𝑣𝑖𝑗 = 𝑤𝑗.𝑟𝑖𝑗 (7) 

Step 4: The positive and negative ideal solutions for each criterion refer to Eq. (8) and 

Eq. (9), respectively 

𝑃𝐼𝑆 = 𝑉𝑗
+ = 𝑀𝐴𝑋𝑖(𝑉𝑖𝑗) (8) 

𝑁𝐼𝑆 = 𝑉𝑗
− = 𝑀𝐼𝑁𝑖(𝑉𝑖𝑗) (9) 

Step 5: The geometry distance of each alternative from positive and negative ideal 

solution is calculated refer to Eq. (10) and Eq. (11), respectively. 

𝑆𝑖
∗ = √∑ (𝑉𝑖𝑗

𝑛
𝑗=1 − 𝑉𝑗

+)2 (10) 

𝑆𝑖
∗ = √∑ (𝑉𝑖𝑗

𝑛
𝑗=1 − 𝑉𝑗

−)2 (11) 



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2.5 Sensitivity Analysis 

Validation is a crucial method to be incorporated into any type of research to identify the 

credibility and validation of results. The application can be widely seen in the domain of 

renewable energy [26], controller design [27], CNC machining process [28], decision making 

[29] and others. Moreover, in engineering domains such as structural analysis and 

optimisation challenges, sensitivity analysis is used to analyse uncertainty. The goal of 

MCDM sensitivity analysis is to evaluate how changing the weights assigned to the criteria 

affects the ranking of the options. It also assures the robustness of the offered technique [29]. 

In addition, a sensitivity analysis is performed to confirm the final ranking recommendation. 

The sensitivity analysis results in several scenarios show that the best option could change 

depending on how the assessment criteria are weighted. 

3. RESULTS AND DISCUSSION 

3.1 Kansei Engineering 

To conclude, the design aspects of the oil spill skimmer were identified using (PLS) 

analysis, which could be referred from [17]. The technique and analysis provide the 

coefficient plot values, illustrating whether or not that design aspect is essential for designing 

a portable oil spill skimmer. Certain design aspects have many high positive scores based on 

the table below. Therefore, certain design factors will be prioritised when developing the oil 

spill skimmer. On the other hand, several design aspects negatively associate with the Kansei 

terms. As a result, several design features will be neglected when developing the oil spill 

skimmer. These design components must be introduced into the design process and those that 

should be ignored while designing the portable oil spill skimmer (Table 2).  

Table 2: List of positive and negative design elements 

Positive design elements Negative design elements 

• Compact body appearance • More than a pair of floating support 

• Medium body size • External oil tank location 

• 41-100 kg weight • Non-oleophilic skimmer material 

• Medium-size oil tank • Bulky body appearance 

• A pair of floating support aid • Round body shape 

• Outrigger floating support shape • Vacuum type skimmer 

• Square/rectangular body shape • Small body size 

• Oleophilic skimmer material • Cylindrical body shape 

• Water extraction below 10%  

• Brush type skimmer  

• Internal oil tank storage  

• Less than 5 m/s speed  

3.1.1 Reliability Testing 

The acquired data is subjected to reliability testing using a questionnaire to assess 

whether the data is trustworthy. As a result, Cronbach's Alpha is used to assess the data's 

dependability. Cronbach's alpha is an internal consistency measurement that depicts the 

interaction of a group of items in collective data reliability, as represented by the coefficient 

figures. In contrast, homogeneity refers to one-dimensionality and falls under the Reliability 

Internal Consistency component, which measures the phenomenon that provides stable and 



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consistent data collection. The questionnaire was conducted for 30 respondents in this 

research, calculated in Eq. 1. The Cronbach's Alpha calculated using the SPSS programme 

(Statistical Package for Social Science) is 0.912, indicating that the questionnaire set has a 

excellent Reliability Coefficient, as shown in Table 3 below. This result demonstrates that the 

questionnaires fit in, exhibiting the homogeneity of each question. However, originating from 

various parts demonstrates the dimensionality in measuring every response received, 

resulting in a more stable and complete analysis for this study. 

Table 3: The Cronbach's Alpha Value and Reliability Coefficient [30]. 

Cronbach's Alpha Value Reliability Coefficient 

α > 0.9 Excellent 

α > 0.8 Good 

α > 0.7 Acceptable 

α > 0.6 Questionable 

α ≥ 0.5 Poor 

3.1.2 Conceptual Design 

The design concept of a portable oil spill skimmer is based on the results obtained from 

the Kansei Engineering analysis. Figure 2 shows the conceptual designs of the portable oil 

spill skimmer. Furthermore, the strengths and weaknesses of each design are elaborated in the 

AHP analysis, where it is compared using pairwise comparison with several sub-criteria.  

(a)      (b)   (c)       (d)                 (e) 

Fig. 2: Developed conceptual design of portable oil spill skimmer:  

(a) Design 1, (b) Design 2, (c) Design 3, (d) Design 4, (e) Design 5. 

 

3.1.3 Specifications of Each Design 

The specification of each design is shown in Table 4. 

3.2 Analytical Hierarchy Process (AHP) 

The pairwise comparison matrix is created using the hierarchy model from Fig. 3. The 

number in the pairwise matrix is decided by an expert such as oil and gas or OSRR engineers. 

This is done to improve the result accuracy of the AHP. The pairwise comparison in this 

research has three stages: design alternatives, sub-criteria and overall analysis of the criteria, 

sub-criteria, and design alternatives analysis. Finally, a matrix with regards to the plan will be 

determined. AHP is used to implement a hierarchy paradigm for structuring product principal 

decisions. Figure 3 depicts a four-level hierarchy decision mechanism.  



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                                                                     Table 4: The specification of each conceptual design 

 

Specification Design 1 Design 2 Design 3 Design 4 Design 5 

Weight 5 – 40 kg 41 – 100 kg 5 – 40 kg 41 – 100 kg 41 – 100 kg 

Speed Less than 1 m/s Less than 1 m/s Less than 5 m/s Less than 1 m/s Less than 1 m/s 

Oil Tank Capacity Large Large Medium Large Medium 

Oil Suction 

Capacity 

Below 51 L/h Below 51 L/h Below 51 L/h Below 51 L/h Below 51 L/h 

Self-Propelling 

Capacity 

No Yes Yes Yes Yes 

Size Big Big Medium Medium Large 

Oil Tank Location External Internal Internal External Internal 

Water extracted 

with oil 

20% 20% 10% 10% 20% 

Body shape Square/Rectangular Square/Rectangular Square/Rectangular Square/Rectangular Square/Rectangular 

Body appearance Bulky Bulky Bulky Compact Compact 

Portability Carried by 2 

person 

Carried by more 

than 2 person 

Carried by 2 

person 

Carried by more 

than 2 person 

Carried by 2 

person 

Skimmer type Brush Brush Roller Roller Brush 

Material Oleophilic Oleophilic Oleophilic Oleophilic Oleophilic 

Floating support 

aid shape 

Round Cylindrical Outrigger Outrigger Outrigger 

Number of 

floating support 

aid 

4 2 2 4 4 

Support aid type Polyvinyl chloride 

(PVC) 

Polyvinyl chloride 

(PVC) 

Fibreglass PVC Fibreglass 



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The criteria are labelled as follows: Performance (P), Safety (S), Maintenance (M) and 

Portability (P). The sub-criteria are labelled as follows: Oil Suction (OS), Manoeuvrability 

(MA), Strong Body Frame (SBF), Stability (SA), Enclosed Components (EC), Easy to Repair 

(ER), Easy to Disassemble (ED), Easy to Relocate (ETR) and Lightweight (L). The stability 

of the design is evaluated according to the number of floating support (closely influenced by 

the shape of floating support) and floating support shapes (bow-ship shapes are 

recommended). Furthermore, the oil suction is evaluated according to the types of skimmer 

material.  

This research does not consider the cost as main or sub-criteria since the criteria is not a 

concerning factor and also was inconsiderate in the previous research as well. The research in 

the oil and gas field that presents this scenario [31,32]. Moreover, the product will be 

focusing on specific customers as it is characterised as high variety, low volume production. 

The basic characteristics of high variety, low volume is the manufacturing of one or few 

numbers of products designed and produced as per the requirement of customers within pre 

decided time, with a drawback of high cost of production [33]. Thus, cost is not taken as one 

of the influencing factor as cost criteria will not affect the pairwise evaluation. 
 

Fig. 3: A hierarchy model for the selection of design concept. 

The following table is the pairwise comparison matrix of the design alternatives in each 

sub-criterion. The table below shows the step-by-step process of the pairwise comparison 

matrix for the oil suction sub-criteria. This process is done through all the nine sub-criteria. 

Note that the CR value needs to be less than 0.1. The expert decides the value inside the 

matrix. All the calculations were done following the steps explained in the methodology. 

Table 4 shows the pairwise comparison matrix of each design in the oil suction sub-criteria.  

Table 5: Pairwise comparison matrix of each design in oil suction sub-criteria 

Weight D1 D2 D3 D4 D5 

D1 1 4 3 4 4 
D2 0.25 1 2 3 3 
D3 0.33 0.5 1 0.5 0.5 
D4 0.25 0.33 2 1 1 
D5 0.25 0.33 2 1 1 



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Next, the synthesised matrix and consistency testing were done in Table 5. The same 

calculation applies to each of the sub-criteria. It is observed that the consistency ratio (CR) is 

below 0.1; thus, the pairwise comparison evaluation is accepted. 

All the sub-criteria are analysed as shown in Tables 4 and 5. In the following analysis, 

the pairwise comparison of Oil Suction (OS), Manoeuvrability (MA), and Strong Body 

Frame (SBF) was made to calculate which sub-criteria in the performance criteria has higher 

weightage. Table 6 shows the pairwise comparison matrix of performance criteria. Table 7 

shows the process of averaging the normalised columns. First, the eigenvector was calculated 

by multiplying to obtain the new vector. As the consistency ratio (CR) value is less than 0.1, 

thus the pairwise comparison evaluation is accepted. 

Table 6: Synthesised matrix for the design alternatives 

Wt D1 D2 D3 D4 D5 SUM PV NV NV/PV CI RI CR 

D1 0.4800 0.6480 0.3000 0.4210 0.4210 2.271 0.4542 2.5483 5.6111 0.08 1.12 0.07 

D2 0.1200 0.1620 0.2000 0.3150 0.3150 1.1137 0.2227 1.2163 5.4605    

D3 0.1600 0.0810 0.1000 0.0520 0.0520 0.4463 0.0893 0.4689 5.2531    

D4 0.1200 0.0540 0.2000 0.1050 0.1050 0.5846 0.1169 0.6002 5.1332    

D5 0.1200 0.0540 0.2000 0.1050 0.1050 0.5846 0.1169 0.6002 5.1332    

        Total 26.59    

        λ max 5.3182    

 

Table 7: Pairwise comparison matrix of performance criteria 

Wt OS MA SBF 

OS 1.0000 3.0000 5.0000 

MA 0.3333 1.0000 2.0000 

SBF 0.2000 0.5000 1.0000 

 
Table 8: Synthesised matrix for the sub-criteria 

 

Table 9: Overall priority vector for the alternatives 

Alternatives Overall PV 

D1 0.3098 0.0587 0.0588 0.1347 

D2 0.1800 0.1359 0.1367 0.0531 

D3 0.2055 0.2375 0.4580 0.3928 

D4 0.1451 0.2840 0.2474 0.1691 

D5 0.1596 0.2840 0.0991 0.2503 

Table 9 reveals that design 3 (D3) has the most significant value (0.2603 or 26.03 per 

cent) among the various design concepts suitable for further development. Design 4 (D4) is 

the second highest, with a value of 0.2356 (23.6 per cent), while design 1 (D1) is the lowest, 

Wt OS MA SBF PV NV NV/PV CI RI CR 

OS 0.6522 0.6667 0.6250 0.6479 1.9485 3.0071 0.00 0.58 0.0032 

MA 0.2174 0.2222 0.2500 0.2299 0.6902 3.0026    

SBF 0.1304 0.1111 0.1250 0.1222 0.3667 3.0013    

     Total 9.01    

     λ max 5.3182    



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with a value of just 0.1323 per cent (13.23 per cent). As a result, D3 has been chosen as the 

preferred design idea, as it provides the most value among the five options. 

Table 10: Oil spill skimmer design concept ranking 

 

 

 

 

 

3.3 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) 

The TOPSIS multi-criteria decision matrix is utilised in this paper to compare the 

ranking result with AHP for verification purposes. The step-by-step calculations are 

explained earlier in the paper. The weights of the criteria were obtained from AHP. The 

weightage of the criteria and sub-criteria was calculated to identify the global weight of each 

sub-criteria. The calculation example of the criteria and sub-criteria are as follows. Table 11 

shows the global weight of the priority vector obtained from AHP. 

Table 11: Global weight of the priority vector obtained from AHP 

Criteria Priority Vector Sub-criteria Priority Vector Global Weight 

Performance 0.2776  (OS) 0.6479 0.1799 

  (M) 0.2299 0.0638 

   (SBF) 0.1222 0.0339 

Safety 0.5635  (S) 0.7500 0.4226 

   (EC) 0.2500 0.1409 

Maintenance 0.1077  (ER) 0.6667 0.0718 

   (ED) 0.3333 0.0359 

Portability 0.0512  (ETR) 0.2500 0.0128 

  (L) 0.7500 0.0384 

Table 12 shows the pairwise comparison matrix of the TOPSIS analysis showing the 

Beneficial (B) and Non-Beneficial (NB) criteria. The value of the Positive Ideal Solution 

(PIS) is the highest, and the lowest value for the Negative Ideal Solution (NIS) is the 

beneficial (B) criteria. As for the non-beneficial (NB), it is contrariwise. 

Table 12: Pairwise comparison matrix  

 

 

 

 

 

 

The ranking is based on the Ci score, with a more excellent value of Ci indicating the 

best option among the five options (D1, D2, D3, D4, and D5). After calculating relative 

Alternatives Priority Vector Rank 

D1 0.1323 5 

D2 0.1440 4 

D3 0.2603 1 

D4 0.2356 2 

D5 0.2278 3 

 B B NB B NB B NB NB B 

 OS M SBF S EC ETR ETD ER L 

D1 5 2 3 3 2 2 2 4 4 

D2 3 3 2 3 4 3 3 2 2 

D3 4 5 4 4 4 5 5 4 4 

D4 3 4 3 4 4 4 4 4 4 

D5 3 4 5 4 4 2 2 4 4 



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closeness (Ci), the value of Ci is used to rank the candidates. Because the D3 alternative has a 

better value than the other alternatives, it is rated first. D3 >D2 >D4 >D5 >D1 are the 

outcomes of all alternatives based on better scores, and their ordering preferences are shown 

in Table 13. 

Table 13: The ranking of the best design concept for oil spill skimmer 

Alternatives Si+ Si- Ci Rank 

D1 0.1199 0.3024 0.7160 5 

D2 0.0946 0.2524 0.7274 2 

D3 0.1146 0.3078 0.7287 1 

D4 0.1206 0.3113 0.7208 3 

D5 0.1232 0.3154 0.7192 4 

 

Table 14 shows the ranking result from AHP and TOPSIS. The analysis between AHP 

and TOPSIS shows that design 3 is chosen as the best conceptual design for a portable oil 

spill skimmer. This result shows that the ranking from AHP is verified through the TOPSIS 

analysis. 

Table 14: The comparison ranking of AHP and TOPSIS result 

Alternatives AHP Rank TOPSIS Rank 

D1 5 5 

D2 4 2 

D3 1 1 

D4 2 3 

D5 3 4 

3.4 Sensitivity Analysis 

The weighting of the primary criteria significantly impacts the final priority alternatives. 

As a result, even slight adjustments in the weighting of the criteria might have a significant 

impact on the final ranking. Furthermore, when extra criterion weights are applied, the 

ranking's stability must be reviewed because these weights are typically based on highly 

subjective viewpoints. Consequently, sensitivity analysis was used to test the AHP finding's 

robustness.  

Super Decision software was used to conduct the sensitivity analysis. The performance 

graph depicts how the alternatives perform when the situation of all parameter’s changes. D1 

red, D2 blue, D3 black, D4 green, and D5 yellow are the colours of the design choices. In 

figure 1, the first ranking changes from design idea 3 to design concept 1 when the 

performance priority vector increases by 65 per cent. Figure 2 shows that when the priority is 

increased by 75%, design 4 gets the top position, followed by design 5 and design 1. In 

addition, when maintenance priority is increased by 70%, the ranking in design 2 to rank 3 

and design 5 to rank 4 is shown in Fig. 3. Finally, there are ranking modifications for designs 

2, 3, 4 and 5 for the portable criteria in Fig. 4, at 15%, although design 3 remains at the top of 

the ranking. 

 

 



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(a) (b) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(c) (d) 

Fig. 4: The sensitivity analysis graph of the criteria; (a) performance, (b) safety,  

(c) maintenance, (d) portable. 

4. CONCLUSION 

Extensive studies on product development trends and customer demands, as well as the 

study on design techniques and methodologies, are required for product design. Designers 

should be able to see flaws in the design process, sum up the design experience regularly, and 

think about the meaning of the design from the perspectives of businesses, consumers, and 

social culture. The correct balance of perceptual inventiveness and rational assessment is 

required to develop exceptional design works. By combining Kansei Engineering and the 

Analytical Hierarchy Process, this study extracts the approach of analysing an oil spill 

skimmer's user demand and technical specification at the early design stage and proposes a 

technique for assessing and choosing the most appropriate design concepts during the 

conceptual design stage. TOPSIS and the sensitivity analysis approach were used to verify 

the AHP results. Design 3 was the best design concept for a portable oil spill skimmer 

because it has the highest value (26% or 0.26). AHP can help design engineers analyse and 

choose the optimal design concept based on the criteria and sub-criteria for a decision. Other 

forms of MCDM methods might be integrated with Kansei Engineering in the future as a 

recommendation since the findings would be valuable in determining whether other decision 

methods could be integrated to obtain more precise conclusions. In addition, the number of 

criteria and sub-criteria could be increased to produce more accurate result. 



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ACKNOWLEDGMENT 

This paper and its research would not have been possible without the exceptional support 

from the UTeM Short Term Grant no (PJP/2020/FKP/PP/S01775). Furthermore, we are 

eternally grateful for the generous support from the Center of Research Innovation 

Management (CRIM) Universiti Teknikal Malaysia Melaka for the publication of this paper. 

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