# An Integrated QFD Approach for Industrial Robot Selection

Gülçin Büyüközkan, Öykü Ilıcak, Orhan Feyzioğlu

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Gülçin Büyüközkan, Öykü Ilıcak, Orhan Feyzioğlu. An Integrated QFD Approach for Industrial Robot Selection. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.561-570, 10.1007/978-3-030-85906-0_61. http-04022138

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# An Integrated QFD Approach
## for Industrial Robot Selection

Gülçin Büyüközkan¹[0000-0002-2112-3574], Öykü Ilıcak²[0000-0001-6337-9773] and Orhan Feyzioğlu³[0000-0002-8919-191X]

¹ Galatasaray University, Istanbul, 34349, Turkey
gbuyukozkan@gsu.edu.tr

² Galatasaray University, Istanbul, 34349, Turkey
oykuilicak12@gmail.com

³ Galatasaray University, Istanbul, 34349, Turkey
ofeyzioglu@gsu.edu.tr

**Abstract.** Nowadays, where Industry 4.0 is discussed extensively, the selection of industrial robots has become an important issue. These robots enable production companies to produce higher quality products with high efficiency and in a cost-effective manner. However, an incorrect selection of these robots can cause significant losses for companies. Various factors need to be considered for the effective selection of industrial robots. In this study, a decision model is presented for industrial robot selection. Quality function deployment (QFD), a well-known and powerful tool that converts customer requirements into final design characteristics, is used in this study, with Group Decision Making (GDM) perspective. In GDM, decision-makers who have different backgrounds or ideas can state their preferences in various formats. The Multiple Preference Relations (MPR) technique is used to combine different assessments. Therefore, this study combines QFD with MPR to handle the different forms of information while calculating the customer requirements importance. Furthermore, the Complex Proportional Assessment (COPRAS) method is used to choose the most suitable industrial robot for the proposed study. The presented method was analyzed in a case study on the robot selection problem for the assembly line of a company operating in the manufacturing industry. The alternatives evaluated with the COPRAS method were also applied with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The results of both methods were compared and found to be consistent.

**Keywords:** Robot Selection, QFD, Multiple Preference Relations, COPRAS.

## 1 Introduction

The ability of businesses to maintain their positions in an internationally competitive environment depends on their transition to automation-based systems by changing their production structures [1]. Robots can perform repetitive and dangerous tasks and complete all processes much better, more accurately, and more efficiently [2]. Robot-ics-based automation changes and improves manufacturing applications. Customer
choice (CRs) and technical requirements (DRs) should be analyzed correctly to select industrial robots. The Quality Function Deployment (QFD) approach, known as a customer-oriented systematic method that focuses on this analysis, was used in this study. QFD is also a Group Decision Making (GDM) approach with many expert opinions. Considering the GDM approach, Decision Makers' (DMs) perspectives may differ from each other and may wish to evaluate in different formats. Multiple preference relations (MPR) are often applied to deal with varying forms of evaluations [3].

In this study, selecting an industrial robot to be used in the assembly line of ABC company is discussed. QFD approach was applied and integrated with the MPR technique to deal with evaluation structures in different formats in prioritizing the CRs. Then DRs were determined, and their weights were identified by the House of Quality (HoQ) relationship matrix. Finally, alternative robots were selected in line with DRs with the Complex Proportional Assessment (COPRAS) method. The alternatives were also evaluated with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, and the results of both methods were compared.

This study continues with the following sections: In Section 2, a literature review is provided. Section 3 provides the methodology, Section 4 introduces the case study, and Section 5 gives results and discussion, while Section 6 concludes the study and offers future research directions.

## 2 Literature Review

Robots that are starting to enter the production environment with Industry 4.0 are more intelligent and secure. The selection of the correct robot is an important issue. When we examine the literature, it is seen that many studies have been performed on robot selection problems. Vahdani et al. [4] proposed a robot selection problem based on interval-valued fuzzy COPRAS. Pasawang et al. [5] presented a QFD technique to design an autonomous underwater robot. Sen et al. [6] used an extended PROMETHEE method to select the best robot by considering subjective and objective criteria. Yalçın and Uncu [7] applied the EDAS method for the robot selection problem. Fu et al. [8] proposed a GDM approach to handle multiple criteria robot selection problems. Nasrollahi et al. [9] solved a robot selection problem using fuzzy the best-worst method (BWM) with PROMETHEE. More recently, Ali and Rashid [10] used group BWM and group AHP method for robot selection. Another recent study, Rashid et al. [11], proposed a hybrid BWM-EDAS method, which is the first study that integrates BWM with the EDAS method for the proper selection of robots.

The literature review shows that several studies have been done about the robot selection problem. However, there are no such studies on evaluating industrial robots for an assembly line using an integrated QFD methodology with MPR and COPRAS.
# 3 Methodology

## 3.1 Quality Function Deployment (QFD)

QFD is a systematic method that helps to identify the customer's design needs to reflect the needs and expectations of the product/service [12]. The fundamental structure of the QFD is the HOQ. With HOQ, “What's” and “How's” can be defined in a short time. The HOQ matrix is shown in Fig.1.

<figure><img src="image_3.png" /><figcaption>Fig. 1. HOQ matrix for the study</figcaption></figure>

In this study, in the HOQ matrix, the voice of the customer will be the needs for the selection of industrial robots, priorities will be the importance of end-user needs, product characteristics will be technical requirements. The relationship matrix shows the relationship between needs and technical requirements.

## 3.2 Multiple Preference Relations (MPR)

In the GDM process, DMs can present evaluations in different ways. These can be linguistically, numerically, or by subsets according to their level of information. MPR allows DMs, having different backgrounds/perspectives, to submit their preferences in various ways. The advantages of this technique are: (1) It gives flexibility to DMs during the evaluation process. (2) It gives better solutions as it is based on GDM. (3) It allows collecting different types of assessments under a single group [13].

## 3.3 Complex Proportional Assessment (COPRAS)

This method is an MCDM method that can evaluate qualitative and quantitative criteria [14]. The advantages of the COPRAS method compared to other MCDM methods can be given as follows: (1) It compares the evaluated alternatives with each other, expresses how good or how bad it is from other alternatives as a percentage. (2) Since long binary comparisons are not made in this method, as in PROMETHEE and ELECTRE, the high number of alternatives does not complicate the process. (3) It gives easier and faster results than other MCDM methods such as ARAS, VIKOR or TOPSIS. It is possible to apply easily with Excel. One disadvantage of the method is
that it cannot calculate criteria weights alone. This can be determined using different methods or depending on DMs [15].

## 3.4 Proposed Methodology

This study implements an improved QFD methodology by presenting a GDM approach that aggregates different preferences into a single group decision, consisting of the weighting of CRs using the MPR method followed by identification and prioritization of DRs, and ranking the alternatives using the COPRAS method. The framework of the proposed methodology is given in Fig.2 and detailed below.

<figure><img src="image_4.png" /><figcaption>Fig. 2. The framework of the proposed methodology.</figcaption></figure>

(i) Step 1- Identifying the CRs: CRs are identified by benefiting from the detailed literature researches and expert opinions.

(ii) Step 2- Prioritizing the CRs: Here, the importance of CRs is determined by expert opinions. In this step, the MPR technique is utilized [13].

(a) Step 2.1- Unifying DMs evaluations: Opinions in different formats from DMs will be combined at this stage. DMs can give preferences in different formats as follows:

* An importance degree vector ($u_1, ..., u_N$) where $u_i \in [0,1] i = 1, ..., N$. Closer to 1 means more important for $u_i$. With the formula below we can turn it to relevance of relative importance:

$$z_{ij} = u_i/u_j \text{ for all } 1 \le i \ne j \le N \quad (1)$$
* An ordered vector (o(1),...,o(N)). Here, o(i) represents the importance ranking of CR i, where the most important is 1 and the least important is N. With the formula below we can turn it to relevance of relative importance:

$$z_{ij} = 9^{u_i - u_j} \text{ for all } 1 \le i \ne j \le N \text{ where } u_i = (N - o(i)) / (N - 1) \quad (2)$$

* DMs may present a linguistic importance vector. Given a fuzzy triangular number can be noted as $(a_i, b_i, c_i)$ where $b_i$ is the most common value. The membership functions of linguistic terms for fuzzy triangular quantification are: NI = (0.00, 0.00, 0.25), SI = (0.00, 0.25, 0.50), MI = (0.25, 0.50, 0.75), I = (0.50, 0.75, 1.00) and VI = (0.75, 1.00, 1.00). With the formula below the linguistics importance vector can be transformed to relative importance relation.

$$z_{ij} = 9^{b_i - b_j} \text{ for all } 1 \le i \ne j \le N \quad (3)$$

* DMs may give the importance of criteria without degree explicitly. So, $z_{ij} = 9$ and $z_{ij} = 1/9$, if criteria i is more important than j and $z_{ij} = 1$ if nothing mentioned. (4)

* DMs may choose just a subset of criteria (R'). So, the preference relation can be given as, $z_{ij} = 9$ if $i \in R'$, $j \in R/R'$ and $z_{ij} = 1/9$ if $i \in R/R'$, $j \in R'$, otherwise its equal to 1. (5)

(b) Step 2.2- Aggregating the evaluations: Each evaluation is aggregated using the order weighted geometric (OWG) operator to define a common group decision in this step by the following formula:

$$\phi^G \{(\overline{w}k1, p_{ij}^{k1}), \dots, (\overline{w}kLk, p_{ij}^{kLk})\} = \prod_{l=1}^{L_k} (p_{ij}^{k[l]}) \quad (6)$$

Here, $\{1, \dots, L_k\} \rightarrow \{1, \dots, L_k\}$ is a permutation such that $\overline{w}_{kl} \ge \overline{w}_k[1+1]$, $l = \{1, \dots, L_{k-1}\}$, so $\overline{w}k1$ is the lth largest value in the set $(\overline{w}_{k1}, \dots, \overline{w}_{kLk})$. Proportional quantifiers, such as “most,” “as many as possible,” etc., can be represented by fuzzy subsets of the unit interval $[0,1]$. When the ratio t is suitable with the purpose of the quantifier it demonstrates then for any $t \in [0,1]$, Q(t) indicates the degree. For a non-decreasing relative quantifier, Q, the weights can be acquired with the formula below:

$$W_k = Q(k/K) - (Q(k-1)/K) \quad k=1,\dots, K \quad (7)$$

where Q(t) is described as;

$$Q(t) = \begin{cases} 0, & \text{if } t < s \\ \frac{t-s}{v-s}, & \text{if } s \le t \le v \\ 1, & \text{if } t \ge v \end{cases} \quad (8)$$

Note that s, t, v ∈ [0,1] and Q(t) indicates the degree to which the proportion t is compatible with the meaning of the quantifier it represents. Examples of the relative quantifiers in the literature are as follows; “most” (0.3, 0.8), “at least half” (0,0.5), and “as many as possible” (0.5,1). When the fuzzy quantifier Q is used to calculate the
OWG operator's weights $\Phi_W^G$, it is represented by. Therefore, the collective multipli- cative relative importance relation is obtained as follows:

Therefore, the collective multiplicative relative importance relation is obtained as follows:

$$ p_{ij}^k = \Phi_Q^G (p_{ij}^{k1}, p_{ij}^{k2}, \dots, p_{ij}^{kL_k}), \quad 1 \le i \ne j \le N \qquad (9) $$

(c) Step 2.3- Identifying the importance of CRs: To define the importance weights of CRs, the evaluations of DM groups aggregated in the matrix $P^k$ must be utilized. The element $ij$ represent the relative importance of criterion $i$ compared to criterion $j$. Then, calculate the quantifier guided importance degree (QGID) of each criteri- on, which quantifies the importance of one criterion compared to others in a fuzzy majority sense. With using the OWG operator, $\Phi_0^G$, defined as follows:

$$ \mathrm{QGID}_{i}^{k} = 1/2 \left( 1 + \log_{9} \phi_{Q}^{G} (p_{ij}^{k}; j = 1, \dots, N) \right), \quad \text{for all } i = 1, \dots, N \qquad (10) $$

As a final step, the $QGID_i$ values should be normalized as below equa- tion to obtain the importance degrees in percentage for the group $k$.

$$ \mathrm{QGID}_{i}^{k} = \mathrm{QGID}_{i}^{k} / \sum_{i} \mathrm{QGID}_{i}^{k} \qquad (11) $$

(iii) Step 3- Identifying the DRs: DRs for robots have been introduced by benefiting from literature researches and expert opinions.

(iv) Step 4- Developing a relationship matrix between CRs and DRs: The relationship matrix is created to which the need influences each technical characteristic.

(v) Step 5- Prioritizing DRs: DRs are ranked according to their importance.

(vi) Step 6- Ranking the alternatives: After completing the QFD and determining the weights of the DRs, robot selection is made at this stage by using the COPRAS method.

(a) Step 6.1- Creating the decision matrix: The decision matrix includes alterna- tives in the rows and the criteria in the columns are created.

(b) Step 6.2- Normalizing the decision matrix: The normalized decision matrix is formed by applying the following formula to the entries of the decision matrix.

$$ x_{ij}^* = \frac{x_{ij}}{\sum_{i=1}^{m} x_{ij}}, \quad j=1, 2, \dots, n \qquad (12) $$

(c) Step 6.3- Determining the weighted normalized decision matrix: A weighted decision matrix is formed by multiplying the entries in every column of the normalized decision matrix among the corresponding criteria weights.

(d) Step 6.4- Calculating weighted normalized indices: At this step, the sum of the weighted normalized decision matrix values is calculated for the decision problem's criteria.

* For the maximization (benefit) criteria:

$$ S_{+i} = \sum_{j=1}^{k} d_{+ij}, \quad j = 1, 2, \dots, k \qquad (13) $$

* For the minimization (cost) criteria:

$$ S_{-i} = \sum_{j=k+1}^{n} d_{-ij}, \quad j = k+1, k+2, \dots, n \qquad (14) $$
(e) Step 6.5- Calculating the relative importance levels of alternatives: $Q_i$, which means relative importance for each decision alternative, is calculated as follows.

$$Q_i = S_{+i} + \frac{S_{-i} \sum_{i=1}^{m} S_{-i}}{S_{-i} \sum_{i=1}^{m} S_{-i}} \quad (15)$$

(f) Step 6.6- Calculating the performance index of decision alternatives: The index values are calculated with the formula below.

$$P_i = \frac{Q_i}{Q_{max}} * 100 \quad (16)$$

# 4 Case Study

In this section, a case study is carried out in line with the proposed methodology for ABC company operating in the production sector. The company wishes to expand its production capacity by increasing the production speed and producing much more efficient and quality products. Thus, a robot is required for the company. It is aimed to perform assembly line operations by this robot, thus reducing the assembly times and increasing the production speed and improving the quality. A five-expert evaluation team of company executives (DM1, DM2, DM3, DM4, DM5) will evaluate six robot alternatives. These robots are Articulated robots (A1), Cartesian robots (A2), SCARA (A3), Delta robots (A4), Cylindrical Robots (A5), and Polar Robots (A6).

In this study, to evaluate the alternatives, three main criteria and ten sub-criteria are determined as CRs by benefiting from literature review and expert opinions [5], [15]. These criteria are: Configuration criteria (CR1) includes Payload capacity (CR11), Workspace (CR12), Accuracy (CR13), and Repeatability (CR14). The second main criteria are Functional criteria (CR2) includes Life expectancy (CR21), Programmable flexibility (CR22), and Safety and security (CR23). The last main criterion is Cost criteria (CR3) includes Purchase cost (CR31), Maintenance cost (CR32) and Operation cost (CR33). Five DMs assess the CRs and provide their evaluations in the forms of an “importance degree vector”, “ordered vector”, “linguistic importance vector”, “importance of criteria without degree” and “subset of criteria”, respectively. As an example, the evaluations for the main criteria are, (0.9, 0.7, 0.8) for DM1, (3, 1, 2) for DM2, (VI, I, I) for DM3. DM4 says {C1}>{C2} and {C2}>{C3} according to their importance and DM5 says that the C1 is more important. Then by operating Eq. (6) - (9), evaluations in different formats are combined under a single group view, and the group importance relation matrices are specified. Subsequently, with the help of Eq.(10) - (11), final evaluations are given in Table 1 for all primary and sub-criteria.

After identifying and prioritizing CRs, DRs were identified which are Weight of the robot (DR1), Speed of the robot (DR2), Geometrical dexterity (DR3), Path measuring system (DR4), Material of the robot (DR5), Size of the robot (DR6), Easy Programming (DR7), Drive system (DR8) [5, 16]. HoQ relation matrix for CRs and DRs and the final importance degrees of DR are given in Table 2.
<table><caption>**Table 1.** Final evaluations of CRs.</caption><thead><tr><th>Main criteria</th><th>Priority</th><th>Sub-criteria</th><th>Local priority</th><th>Global priority</th></tr></thead><tbody><tr><td rowspan="5">CR1</td><td rowspan="5">0.452</td><td>CR11</td><td>0.333</td><td>0.150</td></tr><tr><td>CR12</td><td>0.186</td><td>0.084</td></tr><tr><td>CR13</td><td>0.258</td><td>0.117</td></tr><tr><td>CR14</td><td>0.223</td><td>0.101</td></tr><tr><td>CR21</td><td>0.391</td><td>0.136</td></tr><tr><td rowspan="4">CR2</td><td rowspan="4">0.348</td><td>CR22</td><td>0.320</td><td>0.112</td></tr><tr><td>CR23</td><td>0.289</td><td>0.100</td></tr><tr><td>CR31</td><td>0.377</td><td>0.076</td></tr><tr><td>CR32</td><td>0.328</td><td>0.066</td></tr><tr><td>CR3</td><td>0.201</td><td>CR33</td><td>0.296</td><td>0.059</td></tr></tbody></table>

<table><caption>**Table 2.** Final HoQ matrix.</caption><thead><tr><th rowspan="2">CRs</th><th rowspan="2">Weights of CRs</th><th colspan="8">DRs for the problem</th></tr><tr><th>DR1</th><th>DR2</th><th>DR3</th><th>DR4</th><th>DR5</th><th>DR6</th><th>DR7</th><th>DR8</th></tr></thead><tbody><tr><td>CR11</td><td>0.150</td><td>S</td><td>M</td><td></td><td></td><td></td><td>M</td><td></td><td>S</td></tr><tr><td>CR12</td><td>0.084</td><td></td><td></td><td>S</td><td></td><td>L</td><td>L</td><td></td><td></td></tr><tr><td>CR13</td><td>0.117</td><td></td><td></td><td>L</td><td>S</td><td></td><td></td><td>L</td><td>S</td></tr><tr><td>CR14</td><td>0.101</td><td>L</td><td>S</td><td>L</td><td></td><td></td><td></td><td></td><td>L</td></tr><tr><td>CR21</td><td>0.136</td><td></td><td></td><td></td><td></td><td>S</td><td></td><td></td><td>M</td></tr><tr><td>CR22</td><td>0.112</td><td>L</td><td>M</td><td>S</td><td>S</td><td></td><td></td><td>M</td><td></td></tr><tr><td>CR23</td><td>0.100</td><td></td><td></td><td></td><td></td><td>M</td><td></td><td></td><td></td></tr><tr><td>CR31</td><td>0.076</td><td></td><td>S</td><td></td><td></td><td>S</td><td>S</td><td></td><td>M</td></tr><tr><td>CR32</td><td>0.066</td><td></td><td></td><td></td><td></td><td>L</td><td></td><td></td><td>S</td></tr><tr><td>CR33</td><td>0.059</td><td></td><td></td><td></td><td></td><td>L</td><td>S</td><td></td><td></td></tr><tr><td>Weights of DRs</td><td>1.</td><td>2.0</td><td>1.9</td><td>1.8</td><td>2.2</td><td>1.6</td><td>0.</td><td>3.8</td></tr><tr><td></td><td>497</td><td>50</td><td>35</td><td>75</td><td>58</td><td>87</td><td>396</td><td>70</td></tr><tr><td>Normalized Value</td><td>9.</td><td>12.</td><td>11.</td><td>11.</td><td>13.</td><td>10.</td><td>2.</td><td>23.</td></tr><tr><td></td><td>18%</td><td>57%</td><td>86%</td><td>50%</td><td>84%</td><td>35%</td><td>43%</td><td>73%</td></tr></tbody></table>

(Strong Relation (S) = 9; Medium Relation (M) = 3; Low Relation (L) = 1; Blank =0)

At the last stage, alternatives are listed using the COPRAS and TOPSIS methods and results are given in Table 3. According to Table 3, it is seen that in the COPRAS method, A2 has the first priority when A1 the third. When we look at the TOPSIS method, it is the opposite, and it is seen that A1 has the first priority when A2 has the third.

<table><caption>**Table 3.** Rankings of alternatives by COPRAS and TOPSIS.</caption><thead><tr><th colspan="4">COPRAS Method</th><th colspan="3">TOPSIS Method</th></tr><tr><th>Alternatives</th><th>Q<sub>i</sub></th><th>P<sub>i</sub></th><th>Ranking</th><th>Alternatives</th><th>Weights</th><th>Ranking</th></tr></thead><tbody><tr><td>A1</td><td>0.126</td><td>96.02</td><td>3</td><td>A1</td><td>0.729</td><td>1</td></tr><tr><td>A2</td><td>0.131</td><td>100.00</td><td>1</td><td>A2</td><td>0.588</td><td>3</td></tr><tr><td>A3</td><td>0.123</td><td>93.68</td><td>4</td><td>A3</td><td>0.495</td><td>4</td></tr><tr><td>A4</td><td>0.129</td><td>98.42</td><td>2</td><td>A4</td><td>0.699</td><td>2</td></tr><tr><td>A5</td><td>0.108</td><td>82.58</td><td>5</td><td>A5</td><td>0.267</td><td>5</td></tr><tr><td>A6</td><td>0.098</td><td>75.05</td><td>6</td><td>A6</td><td>0.253</td><td>6</td></tr></tbody></table>
# 5 Results and Discussion

Determining the relative importance of CRs is a significant challenge in QFD applica-
tions. In order to achieve success in QFD applications, there must be effective com-
munication between DMs and a decision process that reflects the preferences of each.
Therefore, the primary purpose of this study is to select an industrial robot for the
assembly line by considering the QFD with the GDM approach. Because DMs have
different education levels/different perspectives, they may want to express their pref-
erences in various formats. Therefore, using the MPR technique in the GDM applica-
tion allows us to get valuable results. Although the method may seem confusing and
complicated, it can easily be implemented using Microsoft Excel, and results can be
obtained quickly. Using such a detailed methodology for industrial robot selection
will ensure that decision-making processes will be more transparent with higher quali-
ty. As a result of the study, the most essential CR is determined as "Payload capaci-
ty," and the most important DR is defined as "Drive system". In the final, alternatives
are ranked with the COPRAS, and Cartesian robots (A2) seemed to have the highest
importance among the other alternatives. Afterward, the alternatives were also evalu-
ated with the TOPSIS method, and the results were compared with the COPRAS. The
highest priority alternative identified as Articulated robots (A1) in TOPSIS. This
shows that problems applying different evaluation approaches can lead to different
decisions. The reason why the methods cause different results can be shown as the
inability to extract the subjectivity that plays a role in the perception of objective
world environments depending on the expertise of DMs.

# 6 Conclusion

Industry 4.0 is an essential issue from a robotics perspective. It enables a better under-
standing of how automation can improve process efficiency, quality, and safety.
Choosing the right robot for the company is a strategic decision. Industrial robot se-
lection is a GDM process that requires customer expectations and technical require-
ments to be well analyzed by multiple DMs. In this study, a QFD approach is present-
ed to select industrial robots for use in the assembly line. QFD is integrated with MPR
to deal with evaluations in different formats when evaluating the criteria, and
COPRAS is used to assess the alternatives. In future studies, a more comprehensive
study will be introduced by expanding the criteria and alternatives specified. Further-
more, incomplete preference relations will be used to reduce the uncertain nature of
GDM.
## Acknowledgments

The authors would like to express their sincere gratitude to the experts. This work has been supported by the Scientific Research Projects Commission of Galatasaray University.

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