Enhancing Competitive Advantage through a Strategic Product Portfolio and Design for Variety in Weather Radar Products

doi:10.21203/rs.3.rs-4370522/v1

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Enhancing Competitive Advantage through a Strategic Product Portfolio and Design for Variety in Weather Radar Products

https://doi.org/10.21203/rs.3.rs-4370522/v1

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In today's competitive industrial landscape, corporations must develop products with a wide range of options to stay ahead. High product quality and mass customization trends have driven industries to adopt product portfolio strategies, enhancing customer satisfaction and optimizing resource use. The design for variety (DFV) technique is crucial for managing product variety impact on costs. This abstract introduces a model for improving weather radar product design by integrating various methodologies for an optimal product structure. By carefully considering competitive advantages and expanding product portfolios, corporations can enhance market share and meet customer needs effectively

variety

Modularity

Goal programming

DEMATEL

Quality function deployment

In today's competitive business environment, companies must design and develop a set of products that can produce, distribute, and stay in the market. To optimize the product portfolio with the highest efficiency and the lowest risk, a model based on design and risk indicators is essential. In the industrial landscape, the use of flexible and agile systems in products and manufacturing processes is necessary to achieve manufacturing variety and a model for measurement. Modular product family development methods require a lot of information and data, which are not always compatible. Compatible data modeling can enable simple changes to all affected tools, identify redundant information, and implement networking between different data (Hanna et al. 2018). Single product markets are difficult to overcome, and many industries are evolving towards mass customization, i.e., customized manufacturing and highly diverse products or services. Mass customization benefits both customers and manufacturers by developing a product portfolio that serves customers better, improving resource utilization for manufacturers, and increasing market share by expanding the product range (Liu and Hsiao 2006). Design for variety (DFV) is a method to decrease the effect of variety on the life cycle costs of a product (Martin and Ishii 2002). A product platform is a set of parts and interfaces of the system and manufacturing process among a set of shared parts. A product family can process a set of variable features or components in a product platform, and also be used in platform design as an efficient tool to fulfill a wide range of products, based on mass order (Zha and Sriram 2006). The development of a production platform architecture brings a significant competitive advantage to a company by reducing design effort and time of supply to market for future product generations. Design for variety (DFV) is a method to decrease the effect of variety on the life cycle costs of a product. The development and expansion of a platform architecture from a robust product bring an important competitive advantage to a company by reducing the amount of design effort and reducing the time of supply to market for future product generations. In the case of weather radar products, designing and developing a family of products with a variety of features and capabilities is essential to meet the diverse needs of customers. The design for variety technique (DFV) is a series of structured methodologies to help the design team reduce the effect of product variation on product life cycle costs. The use of DFV in weather radar product design can provide a competitive advantage by reducing design effort and time of supply to market for future product generations. This study develops a methodology that helps designers address the challenges of responding quickly to dynamic changes in customer needs and increasing complexity resulting from product design changes in a family structure. The aim of the current research is to design to create variety in a weather radar that has the ability to respond to the needs of the industry and customers. This method combines one of DEMATEL's multi-criteria decision-making methods and goal programming (GP) methods and seeks to optimize the product family structure by balancing customer needs and budget constraints. Then, the operational details are explained using a case study.

Introduction of radar types

Radar systems are essential for various applications, including weather monitoring, defense, and navigation. A weather radar, specifically, is a crucial tool for meteorologists and climatologists to track and predict weather patterns. It consists of several components, each with a unique role in the system's functionality.

Transmitter: This component generates an electromagnetic (EM) signal, such as a short sine wave pulse, which is modulated to provide the desired waveform for detection. The waveform should create a stable signal with adjustable bandwidth, high efficiency, and reliability.
Antenna: The radar antenna is a distinct and vital part of any radar system, responsible for creating a parabolic shape to guide signals in the direction of the target. It intercepts part of the energy transmitted by the target and reradiates it back to the radar receiver.
Receiver: The receiver collects the reradiated energy from the antenna, records it using a data recorder, and processes it to determine the presence of the target using the processor's display and location radar.

These components work together to detect and analyze weather patterns, providing valuable data for weather forecasting and storm tracking. Understanding the roles and interactions of these components is essential for designing and optimizing weather radar systems.

In this research, the focus is on developing a model for the variety of a weather radar using the DEMATEL and GP approach. By identifying and prioritizing various external and internal indicators and drivers effective in the design of a weather radar, a more precise and operational design for variety can be achieved, ultimately benefiting both customers and manufacturers in today's competitive markets.

A review of the latest research conducted on the subject

The latest research on the subject of product variety and its management in the context of customer satisfaction and competition has yielded significant insights. Liu and Hsiao (2006) introduced a decision-making method using the Analytic Network Process (ANP) and Goal Programming (GP) methods, which allows for the hierarchical and interdependent nature of the product design process to be considered. This approach aims to reduce design costs and increase efficiency by reusing product designs and expanding product portfolios.

Galizia et al. (2019) proposed an innovative decision support system for the design and selection of product operating systems that better management of trade-off between operating system types and the number of assembly/disassembly tasks creates a product platform and can manage product variety by trying to reconfigure and customize the operating systems.

Saaty (1999) developed the Analytical Network Process (ANP) technique, which is an improvement over the Analytic Hierarchy Process (AHP) method. Both techniques prioritize elements based on pairwise comparisons, but the ANP model has no specific and predictable structure, unlike the AHP model. The DEMATEL method, first designed by Fontela and Gabus (1976), is used to determine the effect of criteria against constraints and normalize the unweighted super matrix ANP. This method establishes relationships and interdependence among the criteria (Tamura and Akazawa 2005).

ElMaraghy et al. (2013) proposed a foundation for managing product variety and its complexity throughout the product life cycle, focusing on design and manufacturing to meet customer needs within budget and time constraints. Kipp and Krausein (2008) conducted research to efficiently support design engineers in developing and improving products with a high number of variables. Hanna et al. (2018) emphasized the importance of visualization-oriented methods and tools for achieving a variety-oriented product structure.

Baylis et al. (2018) proposed a decision support system for the design and selection of product operating systems that better manage the trade-off between operating system types and the number of assembly/disassembly tasks, creating a product platform and managing product variety by reconfiguring and customizing operating systems. Hsiao et al. (2013) divided the modular architecture into two stages, using the Interpretive Structural Modeling (ISM) method to modulate and cluster parts and relationships between parts numerically, and then using cluster analysis and ANP to calculate performance and determine the optimal weighting.

Kuchenhof et al. (2020) proposed an approach for creating a product structure-based system that increases generational variety based on the proposed variety-oriented design. The system dynamism shows the subsequent variety of product components by introducing new product features. The growing network is analyzed using the Cytoscape graph.

In summary, the latest research on product variety and its management highlights the importance of decision-making methods, visualization-oriented tools, and modular architecture in managing product variety and complexity. These studies provide a foundation for developing more efficient and effective product design and manufacturing processes that meet customer needs and expectations.

Research objectives

Main objective

The main objective of this research is to develop a model for the variety of a weather radar using the DEMATEL and GP approach. This model aims to optimize the product family structure by balancing customer needs and budget constraints. The research will achieve this by identifying and prioritizing various external and internal indicators and drivers effective in the design of a weather radar, ultimately benefiting both customers and manufacturers in today's competitive markets. The sub-objectives of the research are:

Determining variety indicators in the weather radar design.
Determining how customer and environmental requirements affect the weather radar design.
Determining the weight and specifying the component affecting and being affected in the weather radar design.

By achieving these sub-objectives, the research will contribute to a more precise and operational design for variety in a weather radar, helping designers address the challenges of responding quickly to dynamic changes in customer needs and increasing complexity resulting from product design changes in a family structure.

Sub-objectives of the research

Identify key indicators of variety in weather radar design to enhance product ‎differentiation and customer appeal.‎
Analyze the impact of changing communication dynamics among weather radar ‎components on design flexibility and performance.‎
Determine the weightage of product components and their reciprocal influence within ‎the weather radar design to optimize functionality and cost-effectiveness.‎

By addressing these sub-objectives, the research aims to provide a comprehensive and ‎detailed understanding of how to develop a model for weather radar variety using the ‎DEMATEL and GP approach. These objectives will contribute to a more precise and ‎operationally efficient design process, enabling designers to adapt quickly to evolving ‎customer needs and navigate the complexities of product design changes within a family ‎structure.‎

Research questions

In this research, the following main question is raised and the result of the research is the answer to it. In line with the main question, sub-questions are also raised, and answering these questions in the research will determine the answer to the main question.

Main question

How can a model be developed for enhancing the variety of a weather radar using the DEMATEL and GP approach?

Sub-questions

What specific indicators contribute to variety in product design, particularly in the context of weather radar systems?
How do changing communication dynamics among weather radar components impact design flexibility and overall system performance?
In what ways can the weighting of product components be determined to optimize the design of a weather radar, considering both its influences and dependencies within the system?

By addressing these research questions, the study aims to provide valuable insights into developing a structured and efficient model for enhancing the variety of weather radar systems, ultimately benefiting both industry stakeholders and end-users

The methodology employed in this research is designed to develop a model for the variety of a weather radar using the DEMATEL (Decision-Making Trial and Evaluation Laboratory) approach and Goal Programming (GP). The methodology aims to optimize the product family structure by balancing customer needs and budget constraints. To achieve this, the research employs the following steps:

Identifying Variety Indicators: The research identifies key indicators of variety in product design, focusing on weather radar systems.
Assessing Component Interdependencies: The study evaluates the impact of changing communication dynamics among weather radar components on design flexibility and overall system performance.
Determining Component Weightage: The research determines the weightage of product components and their reciprocal influence within the weather radar design to optimize functionality and cost-effectiveness.

The methodology is based on the DEMATEL and GP approaches, which are integrated to develop a comprehensive model for weather radar variety. The DEMATEL approach is used to determine the effect of criteria against constraints and normalize the unweighted super matrix ANP, establishing relationships and interdependence among the criteria. The GP method is employed to optimize the product family structure by balancing customer needs and budget constraints.

The research also incorporates a case study to explain the operational details of the proposed methodology. The case study demonstrates the application of the DEMATEL and GP approaches in a real-world context, providing valuable insights into the development of a model for weather radar variety.

By following this methodology, the research aims to contribute to a more precise and operational design for variety in a weather radar, helping designers address the challenges of responding quickly to dynamic changes in customer needs and increasing complexity resulting from product design changes in a family structure.

Figure 1 illustrates the present research's methodology.

Research experts

The information required to form product design structure matrices and experts in this field are given in Table 1.

Implementation of the proposed flexible product design approach

The proposed flexible product design approach for weather radar systems is implemented in a three-step process:

Identifying and Prioritizing Variety Indicators: The research identifies key indicators of variety in product design, focusing on weather radar systems. These indicators are prioritized using a combination of the DEMATEL method and goal programming (GP) methods. The DEMATEL method is used to determine the effect of criteria against constraints and normalize the unweighted super matrix ANP, establishing relationships and interdependence among the criteria. The GP method is employed to optimize the product family structure by balancing customer needs and budget constraints.
Assessing Component Interdependencies: The study evaluates the impact of changing communication dynamics among weather radar components on design flexibility and overall system performance. This step involves analyzing the relationships between components and their impact on the system's adaptability to dynamic customer needs.
Determining Component Weightage: The research determines the weightage of product components and their reciprocal influence within the weather radar design to optimize functionality and cost-effectiveness. This step involves calculating the weight of each component based on its influence on other components and the overall system performance.

These steps are applied in a case study to demonstrate the operational details of the proposed methodology. The case study illustrates the application of the DEMATEL and GP approaches in a real-world context, providing valuable insights into the development of a model for weather radar variety.

By following this implementation process, the research aims to contribute to a more precise and operational design for variety in a weather radar, helping designers address the challenges of responding quickly to dynamic changes in customer needs and increasing complexity resulting from product design changes in a family structure.

Market planning

Determination of key specifications

The product studied in this research is two models of pulsed and continuous-wave radar. The approach presented in this research was investigated for the fixed model. During interviews with experts and the design team and considering the competitive market and changes in customer needs, the key specifications of the intended product platform and the budget limit in the design of this radar were determined. The results for the radar model are shown in Table 2.

Provision of key customer components

Through interviews with experts and asking for designers' comments, the effective factors in designing the list of key components desired by the customer are shown below in Table 3.

Prediction of possible changes

In this step, the changes in the customer's needs are evaluated under the supervision of experts and the design team. The result of the interview with the designers is shown in Table 4.

Approach of QFD structure

QFD matrix

In this stage of research, the relationship between engineering criteria and components used in design is shown. Since the cost is also an external driver for the product, it is added to the engineering criteria. This relationship is shown in Table 5.

Calculation of GVI

Weighting the requirements communication matrix

To determine the numerical value of GVI, according to the changes required for each component, based on Table 7 and under the supervision of experts, the QFD communication matrix relationship of Table 5 is converted into numbers and the power level of each product element is determined. The values of these communication s are given in Table 6.

Calculating the value of the index

To determine the value of GVI, it is sufficient to calculate the sum of each column of the matrix in Table 7. By adding a row to the end of the weight matrix, the calculated GVI value is noted under each component. This method is used according to Martin and Ishii's article in 2000. Table 7 shows this matrix.

By investigating the results of the matrix, it can be seen that in the case of the studied camera, the GVI value, for example, for the duplexer component, is 30. These results indicate that there are components that require higher levels of redesign to meet future specifications.

DEMATEL approach

First phase of the communication matrix

According to the identification and preparation of the list of components and commonalities in the previous step, in this step, the communications of these components with each other are evaluated by experts, and the matrix of the following design structure is presented (Table 8).

The two design structure matrices are summed up together and form the input of the DEMATEL communication matrix (Table 9).

Second phase of the main matrix

In this phase of the research, the relationship between the engineering criteria obtained from the first phase and the components used in the design is shown. By normalizing the main matrix and dividing each presentation by the total, the sum of the rows and columns of the numerical communication matrix is obtained (Table 10).

Third phase of the identity matrix

To form an identity matrix, we set the number one on the diagonal and the rest of the cells are set to zero minus the number of each cell of the normal matrix (Table 11).

After that, the identity matrix is inverted (Table 12).

Then, the normal matrix is multiplied by the inverse matrix (Table 13).

According to Table 14, it can be seen that component A and component E are the most effective.

Planning

The list of indicators obtained from the previous steps, affecting and being affected, is given in Table 15. In this table, a column is also assigned to cost. The cost allocated to each component is determined by the design team.

The graphic diagram of these indicators helps to better visual comparison between the indicators (Figs. 2–2). The comparison chart is shown in the figures below. The top of the chart shows the indicators affecting and the bottom shows those being affected.

According to Formula (1), the data in Table (15) and the obtained weights, the goal model for the circuit investigated in the research for the weather radar studied in the research, assuming that the weight of the goals is 0.25 and for each component, an equivalent cost is considered. it is presented and made as follows:

Min 0.25*(d₁⁻) + 0.25(d₂⁻) + 0.25(d₃⁻) + 0.25(b⁺/270)

s.t

1.409 x₁ + 0.688x₂ + 0.376x₃ + 0.807x₄ + 0.974x₅ + 0.658x₆ + 0.401x₇ + 0.233x₈ + d₁⁻-d⁺ ₁=1

1.012 x₁ + 1.085x₂ + 0.489x₃ + 0.849x₄ + 1.146x₅ + 0.382x₆ + 0.291x₇ + 0.049x₈ + d₂⁻-d⁺ ₂=1

0.157x₁ + 0.157x₂ + 0.183x₃ + 0.078x₄ + 0.078x₅ + 0.165x₆ + 0.078x₇ + 0.104x₈ + d₃⁻-d⁺ ₃=1

35x₁ + 45x₂ + 55x₃ + 65x₄ + 10x₅ + 20x₆ + 15x₇ + 25x₈ + b⁻-b⁺=270

X_i Ɛ [0, 1]

(1)

Data interpretation

In this stage, to identify the component with a higher change priority, the indicator α is calculated for each component. This indicator was defined in the third chapter. But before calculating the indicator α, it is necessary that all design data be normal. Therefore, the amount of data related to design cost is also normalized. The results are shown in Table 16. The results of GVI indicate that the mixer has the highest value; that is, it is a component that needs to be changed and redesigned. Therefore, for the flexible design of the product, it is necessary to design this modular component, and for this purpose, the indicators affecting and being affected in this component need to be reduced.

The value of the indicator α was calculated for all components and the results of the indicator α are given in Table 17.

The results of the indicator α can be shown in a graphic diagram for ease of comparison. Figure 6 illustrates this diagram.

The output of the indicator α shows that the display has the highest value and according to the explanations related to defining the indicators and considering the cost for the design, display is the component that needs to be redesigned.

The research presents a methodology for developing a model for weather radar variety ‎using the DEMATEL and GP approach. The proposed model aims to optimize the product ‎family structure by balancing customer needs and budget constraints. The following key ‎results are obtained:‎

DEMATEL and GP Methodology: The DEMATEL method is used to determine the ‎effect of criteria against constraints and normalize the unweighted supermatrix ‎ANP, establishing relationships and interdependence among the criteria. The GP ‎method is employed to optimize the product family structure by balancing ‎customer needs and budget constraints.‎
Identifying and Prioritizing Variety Indicators: The research identifies key ‎indicators of variety in product design, focusing on weather radar systems. These ‎indicators are prioritized using the DEMATEL and GP methods, resulting in a more ‎precise and operational design for variety.‎
Assessing Component Interdependencies: The study evaluates the impact of ‎changing communication dynamics among weather radar components on design ‎flexibility and overall system performance. This analysis helps to optimize the ‎product family structure by considering the relationships and interdependencies ‎between components.‎
Determining Component Weightage: The research determines the weightage of ‎product components and their reciprocal influence within the weather radar design ‎to optimize functionality and cost-effectiveness. This step involves calculating the ‎weight of each component based on its influence on other components and the ‎overall system performance.‎
Case Study: The research includes a case study to demonstrate the application of the ‎proposed methodology in a real-world context. The case study illustrates the ‎operational details of the proposed methodology, providing valuable insights into ‎the development of a model for weather radar variety.‎

By following this methodology, the research contributes to a more precise and operational ‎design for variety in a weather radar, helping designers address the challenges of ‎responding quickly to dynamic changes in customer needs and increasing complexity ‎resulting from product design changes in a family structure.‎

Author Contribution

Marzieh Azami wrote the manuscript and provided data , Mahdi Karbasian conducted the patient interviews, and Sareh yousefian conducted all statistical analyses. All authors reviewed the final manuscript.

Funding

None.

Conflict of interest

The authors declare that they have no conflict of interest.

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Table 1

Experts involved in the research.
No.	Position	Work experience	No. of people
1	Industrial Engineer	18	1
2	Electrical engineer	20	1
3	Telecommunication engineer	15	1
4	Management	10	1
5	Electronic engineer	18	1

Table 2

Determining the optimal life of the product platform.
Type of radar	Receive and send signal (db)	Transmission power (db)	Frequency (GHz)	Image distance resolution (m)
Pulsed radar	77	24	24	6000
Continuous radar	54	35	35	12000

Table 3

List of customer needs.
No.	Name	Component name
1	A	IF amplifier
2	B	RF amplifier
3	C	Duplexer
4	D	Tracker
5	E	Mixer
6	F	Oscillator
7	G	Video amplifier
8	H	Display

Table 4

Estimation of changes expected by customers by engineering criteria.
Engineering criteria	Qualitative estimation of changes expected by customer	Considered equivalence
Receive and send signal (db)	L	3
Transmission power (db)	H	9
Frequency (GHz)	H	9
Image distance resolution (m)	M	6

Table 5

QFD matrix.
Engineering criteria	Components
Engineering criteria	Display	Video amplifier	Oscillator	Mixer	Tracker	Duplexer	RF amplifier	IF amplifier
Receive and send signal (db)		*	*			*	*	*
Transmission power (db)						*	*	*
Frequency (GHz)			*	*	*	*
Image distance resolution (m)	*
Cost	*	*	*	*	*	*	*	*

Table 6

Relationship weight matrix.
Engineering criteria	Components
Engineering criteria	Display	Video amplifier	Oscillator	Mixer	Tracker	Duplexer	RF amplifier	IF amplifier
Receive and send signal (db)		3	1			3	3	3
Transmission power (db)						6	9	9
Frequency (GHz)			9	6	3	9
Image distance resolution (m)	6
Cost	6	6	9	3	6	3	6	6

Table 7

GVI matrix.
Engineering criteria	Components
Engineering criteria	Display	Video amplifier	Oscillator	Mixer	Tracker	Duplexer	RF amplifier	IF amplifier
Receive and send signal (db)		3	1			3	3	3
Transmission power (db)						6	9	9
Frequency (GHz)			9	6	3	9
Image distance resolution (m)	6
Cost	6	6	9	3	6	3	6	6
GVI	12	9	19	9	9	21	18	18

Table 8

Matrix of design structure.
Pulsed radar
	A	B	C	D	E	F	G	H
A		1	0	1	1	0	0	0
B	1		1	0	1	0	0	0
C	0	1		1	0	0	0	0
D	1	1	1		0	0	0	0
E	1	1	0	0		1	0	0
F	0	0	0	0	1		0	0
G	0	0	0	1	0	0		1
H	0	0	0	0	0	0	1
Continuous wave
	A	B	C	D	E	F	G	H
A		1	0	1	1	0	0	0
B	0		0	0	0	0	0	0
C	0	0		0	0	0	0	0
D	1	1	0		0	0	1	0
E	1	0	0	0		1	0	0
F	0	0	0	0	1		0	0
G	0	0	0	1	0	0		0
H	0	0	0	0	0	0	0

Table 9

The first phase of forming the numerical communication matrix.
W	A	B	C	D	E	F	G	H
A	0	2	0	2	2	0	0	0
B	1	0	1	0	1	0	0	0
C	0	1	0	1	0	0	0	0
D	2	1	1	0	0	0	2	0
E	2	1	0	0	0	2	0	0
F	0	0	0	0	2	0	0	0
G	0	0	0	2	0	0	0	1
H	0	0	0	0	0	0	1	0

Table 10

Normal matrix.
	A	B	C	D	E	F	G	H
A	0	0.33	0	0.33	0.33	0	0	0
B	0.17	0	0.17	0	0.17	0	0	0
C	0	0.17	0	0.17	0	0	0	0
D	0.33	0.17	0.17	0	0	0	0.33	0
E	0.33	0.17	0	0	0	0.33	0	0
F	0	0	0	0	0.33	0	0	0
G	0	0	0	0.33	0	0	0	0.17
H	0	0	0	0	0	0	0.17	0

Table 11

Identity matrix.
	A	B	C	D	E	F	G	H
A	1	-0.333	0	-0.333	-0.333	0	0	0
B	-0.167	1	-0.167	0	-0.167	0	0	0
C	0	-0.167	1	-0.167	0	0	0	0
D	-0.333	-0.167	-0.167	1	0	0	-0.333	0
E	-0.333	-0.167	0	0	1	-0.333	0	0
F	0	0	0	0	-0.333	1	0	0
G	0	0	0	-0.333	0	0	1	-0.167
H	0	0	0	0	0	0	-0.167	1

Table 12

Inverse of the matrix of one.
	A	B	C	D	E	F	G	H
A	1.601	0.808	0.243	0.648	0.752	0.251	0.222	0.037
B	0.411	1.277	0.246	0.201	0.394	0.131	0.069	0.011
C	0.188	0.313	1.098	0.277	0.129	0.043	0.095	0.016
D	0.715	0.603	0.344	1.463	0.381	0.127	0.502	0.084
E	0.678	0.542	0.137	0.281	1.481	0.494	0.096	0.016
F	0.226	0.181	0.046	0.094	0.494	1.165	0.032	0.005
G	0.245	0.207	0.118	0.502	0.131	0.044	1.201	0.200
H	0.041	0.034	0.020	0.084	0.022	0.007	0.200	1.033

Table 13

Importance of sub-criterion
TC	A	B	C	D	E	F	G	H	D
A	0.601	0.808	0.243	0.648	0.752	0.251	0.222	0.037	1.409
B	0.411	0.277	0.246	0.201	0.394	0.131	0.069	0.011	0.688
C	0.188	0.313	0.098	0.277	0.129	0.043	0.095	0.016	0.376
D	0.715	0.603	0.344	0.463	0.381	0.127	0.502	0.084	0.807
E	0.678	0.542	0.137	0.281	0.481	0.494	0.096	0.016	0.974
F	0.226	0.181	0.046	0.094	0.494	0.165	0.032	0.005	0.658
G	0.245	0.207	0.118	0.502	0.131	0.044	0.201	0.200	0.401
H	0.041	0.034	0.020	0.084	0.022	0.007	0.200	0.033	0.233
R	1.012	1.085	0.489	0.849	1.146	0.382	0.291	0.049
D + R	2.421	1.773	0.865	1.657	2.120	1.040	0.692	0.282
D-R	0.396	-0.396	-0.113	-0.042	-0.171	0.276	0.109	0.185

Table 14

Collected key specifications.
Specifications	Affecting	Being affected	Cost	GVI normalize
IF amplifier	1.409	1.012	35	0.157
RF amplifier	0.688	1.085	45	0.157
Duplexer	0.376	0.489	55	0.183
tracker	0.807	0.849	65	0.078
Mixer	0.974	1.146	10	0.078
Oscillator	0.658	0.382	20	0.165
Video amplifier	0.401	0.291	15	0.078
Display	0.233	0.049	25	0.104

Table 15

Solution results in Lingo.
Component	Results
IF amplifier	1
RF amplifier	1
Duplexer	1
Tracker	1
Mixer	1
Oscillator	1
Video amplifier	1
Display	1

Table 16

Normalized cost.
Component	Normal cost
IF amplifier	0.130
RF amplifier	0.167
Duplexer	0.204
Tracker	0.241
Mixer	0.037
Oscillator	0.074
Video amplifier	0.056
IF amplifier	0.093

Table 17

Indicator α.
Indicatorα	Component
0.062	IF amplifier
0.081	RF amplifier
0.171	Duplexer
0.041	Tracker
0.036	Mixer
0.148	Oscillator
0.104	Video amplifier
0.277	IF amplifier

No competing interests reported.

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Enhancing Competitive Advantage through a Strategic Product Portfolio and Design for Variety in Weather Radar Products

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Abstract

Figures

Introduction

Introduction of radar types

A review of the latest research conducted on the subject

Research objectives

Research questions

Methodology

Research experts

Implementation of the proposed flexible product design approach

Market planning

Determination of key specifications

Provision of key customer components

Prediction of possible changes

Approach of QFD structure

QFD matrix

Calculation of GVI

Weighting the requirements communication matrix

Calculating the value of the index

DEMATEL approach

First phase of the communication matrix

Second phase of the main matrix

Third phase of the identity matrix

Planning

Data interpretation

Results

Declarations

Author Contribution

Funding

Conflict of interest

References

Tables

Additional Declarations

Status:

Version 1

W	A	B	C	D	E	F	G	H
A	0	2	0	2	2	0	0	0
B	1	0	1	0	1	0	0	0
C	0	1	0	1	0	0	0	0
D	2	1	1	0	0	0	2	0
E	2	1	0	0	0	2	0	0
F	0	0	0	0	2	0	0	0
G	0	0	0	2	0	0	0	1
H	0	0	0	0	0	0	1	0

W	A	B	C	D	E	F	G	H
A	0	2	0	2	2	0	0	0
B	1	0	1	0	1	0	0	0
C	0	1	0	1	0	0	0	0
D	2	1	1	0	0	0	2	0
E	2	1	0	0	0	2	0	0
F	0	0	0	0	2	0	0	0
G	0	0	0	2	0	0	0	1
H	0	0	0	0	0	0	1	0

W	A	B	C	D	E	F	G	H
A	0	2	0	2	2	0	0	0
B	1	0	1	0	1	0	0	0
C	0	1	0	1	0	0	0	0
D	2	1	1	0	0	0	2	0
E	2	1	0	0	0	2	0	0
F	0	0	0	0	2	0	0	0
G	0	0	0	2	0	0	0	1
H	0	0	0	0	0	0	1	0