# Integrated Process for Developing and Selecting More or Less Better Products Based on QFD and MCGP

Authors: Chih-Fu Wu; Chung-Jen Wu; Hsiang-Ju Chen
Journal: Mathematical Problems in Engineering
 (2023)

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## Abstract

As consumer needs for product features change rapidly, companies must develop new products (NPD) that satisfy these needs in the shortest possible time. Consumer product selection behavior is generally overlooked when developing new products. This is a more complicated problem for consumers belonging to multiple types, such as hospitals and patients. Quality function deployment (QFD) is a good and commonly used method for product design and selection. Choosing the right product design for a company is a crucial strategic consideration to satisfy the needs of consumers and the company’s technical requirements. Designing a new product is multicriteria decision-making (MCDM). In this study, the analytical hierarchy process (AHP), QFD, and multichoice goal programming (MCGP) were combined to address the design and selection of new products. This integrated method considers both tangible (qualitative) and intangible (quantitative) factors, as well as “the higher criterion is better” and “the lower criteria is better” when selecting new design projects. To prove the practicality and sustainable value of the model, we considered a Taiwanese company that developed medical aid products as an example. This paper suggests a good decision-making tool for selecting a new product design based on “more is better/less is better” criteria.


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## Body

## 1. Introduction

To grow in rapidly changing markets, companies must develop new products faster than their competitors do. This development not only helps companies gain a larger market share but also significantly shortens the development time [1]. To meet the general ideas of the market, many companies traditionally consider offering high-quality products, fast delivery, low-cost, and thoughtful after-sales service. However, effectively translating consumer needs into technical features and quality factors is a key process during product design and modern enterprise development. Product design is typically used to create product categories that satisfy different consumer needs. A successful product design requires close integration of product marketing and technical aspects. Therefore, designing a new product is a matter of multicriteria decision-making (MCDM) [2]. Previous studies have investigated product design using different evaluation methods that focus on consumer issues [3]. Wang et al. presented a multichoice goal programming (MCGP) model to solve choice problems in product design with multiple audiences [4].Several methods for optimizing product design and selection have been described in the literature. Using the fuzzy theory [5], Huang et al. showed how a neural network incorporates customers’ needs into a measurement matrix to estimate the product concept description [6]. Komoto and Tomiyama addressed a solution to a product design problem that must be comprehensively evaluated [7]. The following perspectives were considered: functionality, structure, parameter level, and new product development (NPD) behavior. In the product design phase, Huang et al. used a linear programming model (LP) and fuzzy neural network models to select design cases [8]. Wei et al. extended multicriteria optimization and evaluation tools for the structural design of modular products [9]. Nikander et al. proposed preliminary decision research for product design and selection [10]. Gupta et al. proposed a product quality function with a quality function deployment (QFD), which was defined and evaluated based on the ratio of actual value to ideal value and was selected for product design [11]. He et al. developed a five-layer weighted directional product design step to provide a combination tool for each phase of product life cycle design [12]. Current evaluation methods for product design can be divided into the following two categories: (1) qualitative and (2) quantitative. Qualitative methods are widely used for this purpose. For example, when critiquing quality and cost in the product design phase, multiple evaluation terms are used, such as low, medium, and high [12]. Quantitative methods, such as physical planning [13], fuzzy logic [14], neural networks [15], genetic algorithms, analytic hierarchy processes (AHP) [16], dynamic programming algorithms [17], and human factors engineering [18], are interesting. Quantitative methods can also be divided into the following two types: (1) alliances based on design goals and (2) alliances based on the design process.Previous research has lacked integration between qualitative and quantitative conventional reference models for selecting diversified product designs to satisfy consumer needs. However, this study attempted to address the shortcomings of the integrated QFD, AHP, and MCGP methods and apply them to product design and selection problems. To demonstrate the practicality and value of this method, a case study involving a Taiwanese medical device company was conducted.The remainder of this paper is organized as follows: in Section2, we provide an overview of the related work on QFD. Section 3 proposes integration methods for product design and selection. Section 4 presents a case study of a medical device company. Finally, Section 5 concludes the paper and provides an overview of future case studies.


## 2. Literature Review

Quality is a loose definition that can be understood as matching user expectations with user needs [19]. Therefore, QFD is a useful analytical method for product design and development [5]. QFD has a wide range of applications, such as comfort and discomfort in hand tools [20], environmental sustainability [21], strategic supplier performance evaluation [22], hospital service design [23], sustainable product design, production network strategy development [24], product design [25], and new product development [26]. In addition, QFD is an effective technology that can transform customer voices into technical requirements and help companies plan their products and services [27]. Chan and Wu [28] used QFD to convert consumer demand into technical requirements. Therefore, QFD can be used to analyze and evaluate the relationship between consumer requirements (CRs) and engineering characteristics (ECs). For example, Liu [5] used a fuzzy QFD (FQFD) to evaluate the relationship between product design and selection. In addition, cloud computing services use QFD as an indispensable tool in product designing [28–30]. The main advantage of QFD is that it helps decision-makers identify the key trade-offs between consumer needs and the technology that the company can provide in product manufacturing.QFD is considered an effective design method that integrates ergonomic requirements and comfort into hand tool designs because it can transform customer needs [20]. QFD can be used to plan and design a new product development (NPD) [31]. In addition, QFD is a comprehensive concept that can transform CRs into appropriate technical requirements (TRs) at each stage of a new product design. QFD tools can provide many benefits, including improving customer satisfaction and shortening the design cycle of new products [32]. In the QFD process, the planning matrix CR also represents the house of quality (HoQ) [33]. The goal of HoQ is that product design should satisfy the customer requirements, in addition to the marketer, designer, and manufacturer requirements. A typical HoQ model can be used to represent the “WHATs” (i.e., the voice of the customer; in this case, the CRs of medical aid products) and the “HOWs” (i.e., the quality attributes, such as the TRs for medical aid product design). The basic structure of the HoQ includes the following seven specific elements [34]: (1) customer requirements, (2) importance of CRs, (3) technical requirements, (4) relationship matrix between CRs and TRs, (5) correlation between TRs, (6) competition analysis, and (7) design priority requirements. The basic form of HoQ used in this study is shown in Figure 1 [34].Figure 1
Basic form of a house of quality (HoQ) (Liao & Kao [30]).In addition, recently, the AHP has been frequently used to calculate weights to address MCDM problems [35]. The weights for the criteria can be assigned using the AHP, and they can then be used to optimize the scenario using the goal programming (GP) objective function. The three principles of the AHP model are summarized as follows: (1) calculate the criteria priority weight for the model structure, (2) create a comparative judgment matrix for comparing criteria and subcriteria, and (3) generate the weight for each objective using a pairwise comparison process [36]. Furthermore, most product design research considers a single aspiration level for each criterion; however, in practice, these criteria often involve multiple goals and aspiration levels. Chang [37] proposed the MCGP model to decision makers (DMs) set multiple aspirations or goals for each criterion. Cyril et al. [36] developed the AHP and MCGP models to improve public transportation performance from the perspectives of users and operators.


## 3. Techniques Proposed

In this case, the proposed methods were based on the translation of HoQ rules from CRs (e.g., user requirements) into a finished product design. In this study, the customer’s product design requirements were related to eight key technologies (or actions), which were related to product operation function production (e.g., WHATs, the voice of the customers). The company’s research and development (R&D) department can use these eight key technologies to develop the design process (e.g., HOWs, the quality attributes). This method is suitable for the QFD technology (mainly HoQ) to identify TRs to achieve a defined product module in the CRs function. First, the Delphi method was used to obtain CRs and weights. In collecting information through independent and repeated subjective judgments of multiple experts, objective customers and technical requirements were obtained [38]. The AHP was used to determine the priority of CRs; here, a consistency ratio ≤0.10 was considered acceptable for all CRs. The AHP process consisted of six steps [29].Step 1.
Define the unstructured problem and clearly state the goalStep 2.
Decompose the decision problem into a hierarchical structure with decision factors such as criteria and alternativesStep 3.
Use pairwise comparisons between the decision factors and comparison matricesStep 4.
Use the eigenvalue approaches to estimate the relative weight of the decision factorsStep 5.
Check the consistency property of the matrices to ensure that the judgments of the DMs are consistentStep 6.
Aggregate the relative weights of the decision factors to obtain the total score of the alternatives.
The method developed in this study is a hybrid research framework that integrates AHP, QFD, and MCGP for medical aid product design and selection, as shown in Figure2.Figure 2
Proposed research framework.


### 3.1. QFD Method

The implementation steps of the QFD method are shown in Table1 [39].Table 1
Steps for implementing the QFD method.
StepsDescription1Identify CRs and TRs in the product design2Use the technical expertise of the group QFD for the determination of the central relation matrix (RM)3Get the weight for each customer demand from the AHP, this iswi4Calculate the relative importance (RIj) of TR in product development (TP) and weighting priority (RIj∗) using equations 1 and 2RIj=∑i=1nwi×Rij,j=1,2,…m,(1)RIj∗=RIj+∑k=jTkj×RIk,j=1,2,…m. (2)Here, RIj is the relation between the jth TP and the ithCRs specified by RM. In addition, RIj∗ is the importance level of TP in the jth service (j = 1, 2, …, m) and Tkj, j,k=1,…,m,k≠j, indicates the correlation between kth and jth “HOWs.”5Normalize the importance level of TRs to product design (NRIj∗)6Set up a pairwise comparison matrix for each TR designed for the product using AHP7To evaluate the weight of each candidate for each TR in the AHP, determine the pairwise comparison matrix for each TR in the product design8Using equation (3), find the total weightSj=∑j=1mNRIj∗×eij,i=1,2…n, (3)Sj is the total score (or weight) for the jth product design alternative, and eij is the weight of the jth, the alternative for ith of TRs, in the product design9Based on the total score (or weight) obtained for each new product design candidate using equation (3), use the weight values obtained in Step8 to set up the MCGP model (e.g., next subsection) and select the best candidate for the product design


### 3.2. MCGP

Goal programming (GP) is widely used because of its flexibility in multiple-goal problems when decision-makers (DMs) want to minimize the variance between goal achievement and aspiration level [37]. However, when there are multiple-choice levels, such as “higher/more is better” or “lower/less is better”, the traditional GP technology cannot solve these problems. When considering multiple goals, a new product design is a multicriteria decision-making (MCDM) problem. Equation (5) is an MCDM problem; Chang [37] developed an MCGP model to solve multiple-choice problems. The MCGP model is expressed as follows:(4)Minz=∑i=1nwidi++di−,(5)subjectedtofiX−di++di−=gi1orgi2or…orgim,di+,di−≥0i=1,2,…,n,X∈FFisafeasibleset,where fiX is the linear function of the ith goal, gi is the aspiration level of the ith goal, gij (i=1,2,…,n and j=1,2,…,m) is the jth aspiration level of the ith goal, and wi is the weight.In this study, QFD was an essential product design approach that was used to produce demand weights (Si equation (3) or wi equation (4)) and then using (Si or wi) MCGP model, whereas MCGP was employed to solve multiobjective decision-making issues on product designs.


## 3.1. QFD Method

The implementation steps of the QFD method are shown in Table1 [39].Table 1
Steps for implementing the QFD method.
StepsDescription1Identify CRs and TRs in the product design2Use the technical expertise of the group QFD for the determination of the central relation matrix (RM)3Get the weight for each customer demand from the AHP, this iswi4Calculate the relative importance (RIj) of TR in product development (TP) and weighting priority (RIj∗) using equations 1 and 2RIj=∑i=1nwi×Rij,j=1,2,…m,(1)RIj∗=RIj+∑k=jTkj×RIk,j=1,2,…m. (2)Here, RIj is the relation between the jth TP and the ithCRs specified by RM. In addition, RIj∗ is the importance level of TP in the jth service (j = 1, 2, …, m) and Tkj, j,k=1,…,m,k≠j, indicates the correlation between kth and jth “HOWs.”5Normalize the importance level of TRs to product design (NRIj∗)6Set up a pairwise comparison matrix for each TR designed for the product using AHP7To evaluate the weight of each candidate for each TR in the AHP, determine the pairwise comparison matrix for each TR in the product design8Using equation (3), find the total weightSj=∑j=1mNRIj∗×eij,i=1,2…n, (3)Sj is the total score (or weight) for the jth product design alternative, and eij is the weight of the jth, the alternative for ith of TRs, in the product design9Based on the total score (or weight) obtained for each new product design candidate using equation (3), use the weight values obtained in Step8 to set up the MCGP model (e.g., next subsection) and select the best candidate for the product design


## 3.2. MCGP

Goal programming (GP) is widely used because of its flexibility in multiple-goal problems when decision-makers (DMs) want to minimize the variance between goal achievement and aspiration level [37]. However, when there are multiple-choice levels, such as “higher/more is better” or “lower/less is better”, the traditional GP technology cannot solve these problems. When considering multiple goals, a new product design is a multicriteria decision-making (MCDM) problem. Equation (5) is an MCDM problem; Chang [37] developed an MCGP model to solve multiple-choice problems. The MCGP model is expressed as follows:(4)Minz=∑i=1nwidi++di−,(5)subjectedtofiX−di++di−=gi1orgi2or…orgim,di+,di−≥0i=1,2,…,n,X∈FFisafeasibleset,where fiX is the linear function of the ith goal, gi is the aspiration level of the ith goal, gij (i=1,2,…,n and j=1,2,…,m) is the jth aspiration level of the ith goal, and wi is the weight.In this study, QFD was an essential product design approach that was used to produce demand weights (Si equation (3) or wi equation (4)) and then using (Si or wi) MCGP model, whereas MCGP was employed to solve multiobjective decision-making issues on product designs.


## 4. Case Study

Using the recommended QFD method, we conducted a case study at a Taiwanese company (Company A) that developed medical aid products to select the best design for medical personnel. Medical personnel and families of patients used medical aid products. According to Ms. Chen, the general manager of Company A, this was the first time a decision-making group for a medical aid product design (DM) was established.The product design module transferred patients from one bed to another, as shown in Figure3. Similar products on the market are more expensive and inconvenient to move. To solve this problem, Company A developed a device that can be folded and transformed into a suitcase. Therefore, we investigated the most appropriate combination of cost, the function of the product design, and the customer’s choice to find the best design that satisfies TRs and CRs.Figure 3
Module product design pattern.The DM group was composed of the CEO and staff from the production, quality control, product design, finance, and market analysis departments. In addition, nine experts from medical departments and academic organizations were invited to participate in the design team and provide their opinions on product design. The market analysis department provided six key CRs to medical staff and patients through survey forms, as shown in Table2.Table 2
Six important CRs (WHATs, the voice of the customers).
CRsItemsDescriptionCR1Stable functionStable structure and avoid slidingCR2Reduce injuryLabor-saving modules to reduce medical injuriesCR3Easy operationEasy to use and intuitive operationCR4Mobile efficiencyMobile efficiency and avoiding fixed-point use problemsCR5Alternative functionElectric labor-saving module, but there must be an alternative to power outagesCR6Emergency operationRetractable for emergency operationsSubsequently, the DM group converted the six CRs into TPs to be considered in the product design. Through a discussion and analysis of the Delphi method and a review of the literature, the DM group proposed eight TPs, as shown in Table3.Table 3
Eight important TPs (HOWs, the quality attributes).
TPsItemsDescriptionTP1Modular designEasy processing and modular component designTP2Material loadMetal materials should be light and tough, with a large loadTP3Tracked designTracked design to help cases be shifted automaticallyTP4Wheel designUniversal wheel design for increased mobilityTP5Fold designX-fold design and easy to foldTP6Parallel operationElectric operation and mechanical operation run in parallelTP7Drive motorSufficient drive motor unitTP8Battery availabilityUse batteries to increase mobilityIn addition, using the Delphi technology, four types of product design modules were considered suitable for CRs and TPs. The concept of the four-product design modules is illustrated in Figure3.However, the company lacked an evaluation method to determine the best product design that satisfies customers’ needs. To create a reliable product design module for the key benefits, the QFD method was used.We used the original structure of the HoQ to combine CRs and TRs for product design and selection. The specific structures of the AHP and HoQ were combined, as shown in Figure4.Figure 4
AHP-HoQ for product design and selection.The AHP process consisted of six steps [29].Step 7.
Define the unstructured problem and clearly state the goalStep 8.
Decompose the decision problem into a hierarchical structure with decision factors, such as criteria and alternativesStep 9.
Use pairwise comparisons between the decision factors and comparison matricesStep 10.
Use the eigenvalue approach to estimate the relative weight of the decision factorsStep 11.
Check the consistency property of the matrices to ensure that the judgments of the DMs are consistentStep 12.
Aggregate the relative weights of the decision factors to obtain the total score of the alternatives
The main purpose of the AHP-HoQ model was to explain HoQ in the selection of product designs.
Based on a questionnaire provided to medical staff and patients’ family members, the market analysis department identified the definitions of the following six important CRs and stability functions: injury reduction, ease of use, mobile efficiency, alternative functions, and emergency operations. Based on the AHP method, an AHP questionnaire was used to calculate the six important CR weights in the DM group. Therefore, the decision matrix was used to measure the relative importance of each CR using the geometric mean method, as shown in (4).(6)D=1334750.3310.170.5230.33613470.2520.331730.140.50.250.1410.50.200.330.140.3321.
The weight of C1 (e.g., the stable function) was calculated as (1 × 3 × 3 × 4 × 7 × 5)1/6 = 3.286. Similarly, all other elements of the CR weights were calculated as C3 = 2.349, C4 = 1.232, C5 = 0.329, and C6 = 0.430. In addition, the sum of the weights from C1 to C6 was 8.369; thus, the normalized weight of C1 was (3.286/8.369) = 0.393. Thus, the decision matrix was (0.393, 0.089, 0.281, 0.147, 0.039, 0.051)T.
When CR < 0.1, the decision was consistent and the derived weight was used. Therefore, to prove the degree of inconsistency, the following typical eigenvalues of the decision matrix were determined:λmax = 6.555, consistency index (CI) = 0.111, RI = 1.24, and consistency ratio (CR = CI/RI) = 0.089. RI is the average random index and it was determined in an order different from that of the paired comparison matrix. The QFD group determined the weighting of the conversion matrix, as shown in Figure 5. Thus, the QFD matrix was used to solve the product design problem.
Subsequently, we obtain the overall evaluation module for the four-product designs, as shown in Table4. The AHP method prioritizes the requirements of TPs by assigning relative importance to each product design module. An overview of the corresponding AHP questionnaire was applied to each decision. Matrices 1(M-1) through 8(M-8) show the eight paired comparison matrices.M-1: modular design criterion.(7)A1=10.335431360.20.33120.250.170.51,where the weights of A1 were calculated as(8)1×0.33×5×41/4=1.607,3×1×3×61/4=2.711,0.2×0.33×1×21/4=0.604,0.25×0.17×0.5×11/4=0.380.The sum of these weights was 1.607 + 2.711 + 0.604 + 0.380 = 5.302. The total weight was 5.302, and the calculated standard weights (A1) were 1.607/5.302, 2.711/5.302, 0.604/5.302, and 0.380/5.302. Thus, the weight of Decision Matrix 1 (M-1) was (0.303, 0.511, 0.114, and 0.072)T. In addition, to prove the degree of inconsistency, the following typical eigenvalues of the decision matrix were determined: λmax = 4.096, consistency index (CI) = 0.032, RI = 0.9, and consistency ratio (CR = CI/RI) = 0.036. By adopting the same calculation process as in M-1, we obtained the seven other weights from M-2 to M-8.M-2: material load criterion.(9)A2=13260.3310.550.52140.170.20.251,where the weight ofA2 was (0.478, 0.187, 0.276, and 0.059)T.M-3: tracked design criterion.(10)A3=10.20.50.25516420.1710.240.2551,where the weight ofA3 was (0.070, 0.580, 0.089, and 0.262)T.M-4: wheel design criterion.(11)A4=10.335331740.20.1410.250.330.2541,where the weight ofA4 was (0.268, 0.543, 0.052, and 0.136)T.M-5: fold design criterion.(12)A5=10.335731560.20.2120.140.170.51,where the weight ofA5 was (0.319, 0.532, 0.092, and 0.057)T.M-6: parallel operation criterion.(13)A6=15270.210.1720.56140.140.50.251,where the weight ofA6 was (0.514, 0.090, 0.331, and 0.065)T.M-7: drive motor criterion.(14)A7=15230.210.1420.57140.330.50.251,where the weight ofA7 was (0.449, 0.094, 0.371, and 0.087)T.M-8: battery availability criterion.(15)A8=13450.331220.250.5130.20.50.331,where the weight ofA8 was (0.534, 0.206, 0.150, and 0.082)T.
Based on the results in Table4, the overall score of the P1 product design module was extremely high because it was selected using the AHP method.
Using the product development records of relevant parameters over the past five years, Company A considered its capabilities and resources when developing new products. The CEO and General Manager set five goals (Gi) for this case, as follows:(i)
G1: excluding medical aid product development costs, the total cost per vehicle was between USD 21,000 and USD 26,500 (i.e., less cost is better)(ii)
G2: the total weight of each vehicle was between 90.5 and 152 kg (i.e., less weight is better)(iii)
G3: the total efficiency of the battery was set from no battery to 5.5 h (i.e., longer time is better)(iv)
G4: the total efficiency of the battery was set from no battery to 5.5 h (t, i.e., longer time is better)(v)
G5: the candidate with the highest weight was selected as the best product design module (i.e., more is better)
Table5 presents the data for the design configuration of each medical aid product candidate.
The QFD-MCGP model was constructed by combining the AHP, QFD, and MCGP methods and using the functions and resources related to the product design and selection of Company A, as shown in Table6.Figure 5
QFD matrix used to select the product design.Table 4
Overall scores of product design module candidates.
Technical requirements of product design (TPs)WeightwiImportancewi of the product design moduleCICI/RIP1P2P3P41. Modular design17.700.3030.5110.1140.0720.0840.0932. Material load1.830.4780.1870.2760.0590.0430.0433. Tracked design3.210.0700.5800.0890.2620.1040.1044. Wheel design16.070.2680.5430.0520.1360.0630.0635. Fold design1.830.3190.5320.0920.0570.0750.0756. Dual operation7.250.5140.0900.3310.0650.0500.0507. Drive motor33.210.4490.0940.3710.0870.1200.1208. Battery availability18.890.5340.2060.1500.0820.0510.051Overall score (OC)40.0728.6221.379.41Normalized OC0.4030.2880.2150.095Table 5
Medical aid product design resource utilization.
Product design module candidatesCost (USD)Car weight (kg)Batteries efficiency (hour)Drive power (W)x1 (P1)2100150No batteriesNo drivex2 (P2)18001352.5120x3 (P2)22001254200x4 (P2)26001005400Table 6
Combining equations for QFD-MCGP.
MCGP model formulationPurposeSerial numberMinz=d1++d1−+e1++e1−+d2++d2−+e2++e2−+d3++d3−+e3++e3−+d4++d4−+e4++e4−+d5++d5−Satisfy all obligatory goals(15)st2100x1+1800x2+2200x3+2600x4−d1++d1−=y1For the cost of the design(16)y1−e1++e1−=1100Fory1−g1,min(17)1100≤y1≤2600For the bound ofy1(18)150x1+135x2+125x3+100x4−d2++d2−=y2For the goal of car weight(19)y2−e2++e2−=100Fory2−g2,min(20)100≤y2≤150For the bound ofy2(21)0x1+2.5x2+4x3+5x4−d3++d3−=y3For the goal of battery efficiency(22)y3−e3++e3−=5Fory3−g3,max(23)0≤y3≤5For the bound ofy3(24)0x1+120x2+200x3+400x4−d4++d4−=y4For the goal of drive power(25)y4−e4++e4−=400Fory4−g4,max(26)0≤y4≤400For the bound ofy4(27)0.388x1+0.289x2+0.223x3+0.100x4−d5++d5−=1To maximize candidate weight(28)di+,di−≥0,i=1,2,…,n.Target deviation(29)ei+,ei−≥0,i=1,2,…,n.Target deviation(30)


### 4.1. QFD-MCGP Model

Using the QFD modelP1 (weight = 0.40 is the best choice, but after considering the weight criterion), P2 becomes the best design choice, so in terms of management, decision makers should examine the selection criteria in multiple ways. The QFD-MCGP model was solved using the LINGO software [40], where the optimal solutions were x2 = 1 and x1 = x3 = x4 = 0. The product design module P2 was selected based on the most important quantitative measures for Company A. Different results were obtained using the method, and the advantages of the model compared with other methods are shown in Table 7.Table 7
Comparison of product design selection methods.
MethodsSelection criteriaMultiple-choice aspiration levelsQualitativeQuantitativeLP/GPNoYesNoTOPSISYesNoNoCWYesNoNoMCGP-UNoYesYesDEANoYesNoCPMNoYesNoAHP/ANPYesNoNoMOOPNoYesYesAHP + TOPSISYesNoNoThis proposed method (AHP + MCGP)YesYesYes


## 4.1. QFD-MCGP Model

Using the QFD modelP1 (weight = 0.40 is the best choice, but after considering the weight criterion), P2 becomes the best design choice, so in terms of management, decision makers should examine the selection criteria in multiple ways. The QFD-MCGP model was solved using the LINGO software [40], where the optimal solutions were x2 = 1 and x1 = x3 = x4 = 0. The product design module P2 was selected based on the most important quantitative measures for Company A. Different results were obtained using the method, and the advantages of the model compared with other methods are shown in Table 7.Table 7
Comparison of product design selection methods.
MethodsSelection criteriaMultiple-choice aspiration levelsQualitativeQuantitativeLP/GPNoYesNoTOPSISYesNoNoCWYesNoNoMCGP-UNoYesYesDEANoYesNoCPMNoYesNoAHP/ANPYesNoNoMOOPNoYesYesAHP + TOPSISYesNoNoThis proposed method (AHP + MCGP)YesYesYes


## 5. Conclusions

Product design and selection are key factors for successful business operations and management strategies of a company. Companies can adopt successful product designs for TRs through CRs. Choosing the right product design for customers is an important and complex decision. The case link between CRs and TRs is QFD, which is a suitable analytical method for product design and selection. In previous research, product design did not integrate qualitative and quantitative formal reference models that can satisfy the needs of a diversified customer base. To the best of our knowledge, the QFD-MCGP method has not been applied yet to product design and selection. Therefore, this study developed the QFD-MCGP method that considers CRs and TRs in the selection of the best product design. Thus, considering the quantitative measures most relevant to Company A, the product design module P2 was selected. The proposed QFD-MCGP tool can easily and effectively express customer needs and technical requirements using qualitative and quantitative measures. The advantage of the proposed method is that it allows DMs to set multiple target levels for the decision criteria. Through a practical case study, we give a reference template for other associations or companies when designing and opting for new products.Many techniques have been proposed to solve MCDM problems. These approaches include LP, GP, TOPSIS, combined weight (CW), multichoice goal programming with utility functions (MCGP-U), data envelopment analysis (DEA), cost point methods (CPMs), analytic hierarchy process (AHP), analytic network process (ANP), multi-objective optimization (MOOP), and fuzzy set theory. However, these methods fail to make correct decisions in certain situations because the available real-life data are inherently vague, imprecise, and uncertain. Table7 shows a comparison between the proposed analytical method and other methods. This integrated method considers both tangible (qualitative) and intangible (quantitative) criteria, as well as “higher is better” (e.g., user benefit criteria) and “lower is better” (e.g., cost criteria) in the selection of new products.In summary, the case studies revealed practical ways in which new product development may be valuable for commercial transactions using other technologies, such as combining fuzzy techniques for order preference by similarity to an ideal solution (TOPSIS) to get different weights into the MCGP model algorithm or use multiattribute group decision-making (MAGDM) [41], group decision-making (GDM) [42], to consider the situations when multiple experts are involved in the process, and use multisegment goal programming (MSGP) and a new GP model for product development and design. Therefore, the next research should integrate MAGDM, MSGP, and QFD in medical aid products developed and designed.



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*Source: 7543739-2023-01-25.xml*